this paper provides a novel semi-automatic ontology
extension method for Semantic Web Services (SWS). This is
significant since ontology extension methods those existing
in literature mostly deal with semantic description of static
Web resources such as text documents. Hence, there is a need
for methods that can serve dynamic Web resources such as
SWS. The developed method in this paper avoids redundancy
and respects consistency so as to assure high quality of the
resulting shared ontologies.
The increased potential of the ontologies to reduce the human interference has wide range of applications. This paper identifies requirements for an ontology development platform to innovate artificially intelligent web. To facilitate this process, RDF and OWL have been developed as standard formats for the sharing and integration of data and knowledge. The knowledge in the form of rich conceptual schemas called ontologies. Based on the framework, an architectural paradigm is put forward in view of ontology engineering and development of ontology applications and a development portal designed to support ontology engineering, content authoring and application development with a view to maximal scalability in size and complexity of semantic knowledge and flexible reuse of ontology models and ontology application processes in a distributed and collaborative engineering environment.
Representation of ontology by Classified Interrelated object modelMihika Shah
1. The document discusses representing ontology using the Classified Interrelated Object Model (CIOM) data modeling technique. CIOM represents ontology components like classes, subclasses, attributes, and relationships between classes.
2. Key components of an ontology like classes, subclasses, attributes, and inter-class relationships are described and examples are given of how each would be represented using CIOM notation.
3. CIOM provides a general purpose methodology for representing ontologies using existing database technologies and overcomes limitations of specialized ontology languages and tools.
Tools for Ontology Building from Texts: Analysis and Improvement of the Resul...IOSR Journals
Text2Onto is a tool that learns ontologies from textual data by extracting ontology components like concepts, relations, instances, and hierarchies. It analyzes texts through linguistic preprocessing using Gate to tokenize, tag parts of speech, and identify noun and verb phrases. Algorithms then extract ontology components and store them probabilistically in a Preliminary Ontology Model independent of any representation language. The study aimed to understand Text2Onto's architecture, analyze errors in its extractions, and attempt improvements by using a meta-model of the text to better classify concepts under core concepts.
Ekaw ontology learning for cost effective large-scale semantic annotationShahab Mokarizadeh
This document discusses using ontology learning to semantically annotate a corpus of 15,000 web service interfaces. It proposes extracting terms from the interfaces at a fine-grained level and using pattern-based methods to discover taxonomic and non-taxonomic relations to automatically generate an ontology. The method achieved 62% accuracy for common concepts and 71% for common instances compared to a golden ontology.
Ontology matching finds correspondences between similar entities of different ontologies. Two ontologies may be similar in some aspects such as structure, semantic etc. Most ontology matching systems integrate multiple matchers to extract all the similarities that two ontologies may have. Thus, we face a major problem to aggregate different similarities.
Some matching systems use experimental weights for aggregation of similarities among different matchers while others use machine learning approaches and optimization algorithms to find optimal weights to assign to different matchers. However, both approaches have their own deficiencies.
ONTOLOGICAL MODEL FOR CHARACTER RECOGNITION BASED ON SPATIAL RELATIONSsipij
In this paper, we present a set of spatial relations between concepts describing an ontological model for a
new process of character recognition. Our main idea is based on the construction of the domain ontology
modelling the Latin script. This ontology is composed by a set of concepts and a set of relations. The
concepts represent the graphemes extracted by segmenting the manipulated document and the relations are
of two types, is-a relations and spatial relations. In this paper we are interested by description of second
type of relations and their implementation by java code.
Association Rule Mining Based Extraction of Semantic Relations Using Markov L...IJwest
Ontology may be a conceptualization of a website into a human understandable, however machine-readable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the intentional aspects of a site, whereas the denotative part is provided by a mental object that contains assertions about instances of concepts and relations. Semantic relation it might be potential to extract the whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations describe the linguistics relationships among the entities involve that is beneficial for a higher understanding of human language. The relation can be identified from the result of concept hierarchy extraction. The existing ontology learning process only produces the result of concept hierarchy extraction. It does not produce the semantic relation between the concepts. Here, we have to do the process of constructing the predicates and also first order logic formula. Here, also find the inference and learning weights using Markov Logic Network. To improve the relation of every input and also improve the relation between the contents we have to propose the concept of ARSRE. This method can find the frequent items between concepts and converting the extensibility of existing lightweight ontologies to formal one. The experimental results can produce the good extraction of semantic relations compared to state-of-art method.
The increased potential of the ontologies to reduce the human interference has wide range of applications. This paper identifies requirements for an ontology development platform to innovate artificially intelligent web. To facilitate this process, RDF and OWL have been developed as standard formats for the sharing and integration of data and knowledge. The knowledge in the form of rich conceptual schemas called ontologies. Based on the framework, an architectural paradigm is put forward in view of ontology engineering and development of ontology applications and a development portal designed to support ontology engineering, content authoring and application development with a view to maximal scalability in size and complexity of semantic knowledge and flexible reuse of ontology models and ontology application processes in a distributed and collaborative engineering environment.
Representation of ontology by Classified Interrelated object modelMihika Shah
1. The document discusses representing ontology using the Classified Interrelated Object Model (CIOM) data modeling technique. CIOM represents ontology components like classes, subclasses, attributes, and relationships between classes.
2. Key components of an ontology like classes, subclasses, attributes, and inter-class relationships are described and examples are given of how each would be represented using CIOM notation.
3. CIOM provides a general purpose methodology for representing ontologies using existing database technologies and overcomes limitations of specialized ontology languages and tools.
Tools for Ontology Building from Texts: Analysis and Improvement of the Resul...IOSR Journals
Text2Onto is a tool that learns ontologies from textual data by extracting ontology components like concepts, relations, instances, and hierarchies. It analyzes texts through linguistic preprocessing using Gate to tokenize, tag parts of speech, and identify noun and verb phrases. Algorithms then extract ontology components and store them probabilistically in a Preliminary Ontology Model independent of any representation language. The study aimed to understand Text2Onto's architecture, analyze errors in its extractions, and attempt improvements by using a meta-model of the text to better classify concepts under core concepts.
Ekaw ontology learning for cost effective large-scale semantic annotationShahab Mokarizadeh
This document discusses using ontology learning to semantically annotate a corpus of 15,000 web service interfaces. It proposes extracting terms from the interfaces at a fine-grained level and using pattern-based methods to discover taxonomic and non-taxonomic relations to automatically generate an ontology. The method achieved 62% accuracy for common concepts and 71% for common instances compared to a golden ontology.
Ontology matching finds correspondences between similar entities of different ontologies. Two ontologies may be similar in some aspects such as structure, semantic etc. Most ontology matching systems integrate multiple matchers to extract all the similarities that two ontologies may have. Thus, we face a major problem to aggregate different similarities.
Some matching systems use experimental weights for aggregation of similarities among different matchers while others use machine learning approaches and optimization algorithms to find optimal weights to assign to different matchers. However, both approaches have their own deficiencies.
ONTOLOGICAL MODEL FOR CHARACTER RECOGNITION BASED ON SPATIAL RELATIONSsipij
In this paper, we present a set of spatial relations between concepts describing an ontological model for a
new process of character recognition. Our main idea is based on the construction of the domain ontology
modelling the Latin script. This ontology is composed by a set of concepts and a set of relations. The
concepts represent the graphemes extracted by segmenting the manipulated document and the relations are
of two types, is-a relations and spatial relations. In this paper we are interested by description of second
type of relations and their implementation by java code.
Association Rule Mining Based Extraction of Semantic Relations Using Markov L...IJwest
Ontology may be a conceptualization of a website into a human understandable, however machine-readable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the intentional aspects of a site, whereas the denotative part is provided by a mental object that contains assertions about instances of concepts and relations. Semantic relation it might be potential to extract the whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations describe the linguistics relationships among the entities involve that is beneficial for a higher understanding of human language. The relation can be identified from the result of concept hierarchy extraction. The existing ontology learning process only produces the result of concept hierarchy extraction. It does not produce the semantic relation between the concepts. Here, we have to do the process of constructing the predicates and also first order logic formula. Here, also find the inference and learning weights using Markov Logic Network. To improve the relation of every input and also improve the relation between the contents we have to propose the concept of ARSRE. This method can find the frequent items between concepts and converting the extensibility of existing lightweight ontologies to formal one. The experimental results can produce the good extraction of semantic relations compared to state-of-art method.
it's our presentation during the third international conference of information systems and technologies ICIST 2013 held at Tangier, Morocco in which we propose a new approach for human assessment of ontologies using an online questionnaire.
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING cscpconf
In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used toface the great challenge of representing the semantics of data, in order to bring the actual web to its full
power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. In deed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the
main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former
analysis the concept’s names and the later analysis their properties. Each one of these two sub-modules is
it self based on the combination of lexical and semantic similarity measures.
The document discusses the basics of ontologies, including their origin in philosophy, definitions, types, benefits and application areas. Some key points are:
- An ontology is a formal specification of a conceptualization used to help humans and programs share knowledge. It establishes a shared vocabulary for exchanging information.
- Ontologies describe domain knowledge and provide an agreed-upon understanding of a domain through concepts and relations. They help solve problems of ambiguity and enable knowledge sharing.
- Ontologies benefit applications like information retrieval, digital libraries, knowledge engineering and natural language processing by facilitating semantic search and integration of data.
This document discusses an integrated approach to ontology development methodology and provides a case study using a shopping mall domain. It begins by reviewing existing ontology development methodologies and identifying their pitfalls. An integrated methodology is then proposed which aims to reduce these pitfalls. The key steps in the proposed methodology are: 1) capturing motivating user scenarios or keywords, 2) generating formal/informal questions and answers from the scenarios, 3) extracting terms and constraints, and 4) building the ontology using a top-down approach. The methodology is applied to developing an ontology for a shopping mall domain to provide multilingual information to visitors.
This document summarizes a workshop on data integration using ontologies. It discusses how data integration is challenging due to differences in schemas, semantics, measurements, units and labels across data sources. It proposes that ontologies can help with data integration by providing definitions for schemas and entities referred to in the data. Core challenges discussed include dealing with multiple synonyms for entities and relationships between biological entities that depend on context. The document advocates for shared community ontologies that can be extended and integrated to facilitate flexible and responsive data integration across multiple sources.
ONTOLOGY VISUALIZATION PROTÉGÉ TOOLS – A REVIEW ijait
The document discusses ontology visualization tools in Protégé. It reviews four main visualization methods used in Protégé tools: indented list, node-link and tree, zoomable, and focus+context. It then examines specific Protégé tools that use each method, including their key features and limitations. The tools assessed are Protégé Class Browser (indented list), Protégé OntoViz and OntoSphere (node-link and tree), Jambalaya (zoomable), and Protégé TGVizTab (focus+context). The document concludes by summarizing and comparing the visualization characteristics of these Protégé tools.
Use of ontologies in natural language processingATHMAN HAJ-HAMOU
The document discusses the limitations of classical natural language processing approaches that rely solely on text-based search and statistics. It describes how ontologies can help address these limitations by representing knowledge at the semantic level rather than just words. The document outlines how ontologies can solve problems related to vagueness, high-level concepts, semantic relations, and time dimensions. It then summarizes two research papers that explore using ontologies to index and categorize documents as well as provide multiple views and dimensions to explore a document collection.
Using ontology for natural language processing can help mediate human-machine communication. Ontologies provide a formal representation of knowledge that can be used in natural language processing. The document discusses how general and specific ontologies can be combined and used with natural language processing techniques like machine translation and speech recognition. It also describes how ontologies can be extended over time using artificial neural networks to improve natural language understanding abilities.
This document discusses ontology-based data access. It begins by defining ontology as a representation of concepts and relationships that define a domain. It then provides examples of ontology elements like concepts, attributes, and relations. It describes how ontologies can be used to share understanding, enable knowledge reuse, and separate domain from operational knowledge. The document outlines the process for developing ontologies including scope, capture, encoding, integration, and evaluation. It discusses using ontologies to provide a user-oriented view of data and facilitate query access across data sources. The document concludes by discussing ongoing work on semantic query analysis and graphical ontology mapping tools.
The document summarizes and compares schema matching and ontology mapping. It discusses how schema matching approaches can be applied to ontology mapping given the similarities between schemas and ontologies. The document outlines different categories of schema matching techniques (element-based, structure-based) and provides examples. It also summarizes several ontology mapping tools and approaches that utilize different matching strategies like string, structure, and semantic similarity.
Ontology and Ontology Libraries: a Critical StudyDebashisnaskar
The concept of digital library revolutionized its popularity with the development of networking technology. Digital library stores various kind of documents in digitized format that enables user smooth access to these documents at subsidized costs. In the recent past, a similar concept i.e., ontology library has gained popularity among the communities like semantic web, artificial intelligence, information science, philosophy, linguistics, and so forth.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This paper proposes Natural language based Discourse Analysis method used for extracting
information from the news article of different domain. The Discourse analysis used the Rhetorical Structure
theory which is used to find coherent group of text which are most prominent for extracting information
from text. RST theory used the Nucleus- Satellite concept for finding most prominent text from the text
document. After Discourse analysis the text analysis has been done for extracting domain related object
and relates this object. For extracting the information knowledge based system has been used which
consist of domain dictionary .The domain dictionary has a bag of words for domain. The system is
evaluated according gold-of-art analysis and human decision for extracted information.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Many applications required integration of data from different sources, such as data mining and data / information fusion, etc., and the problem facing any project like this is that data structure different way and the terms and their meanings different from each other, and in this paper we will discuss the most important problems and how solve it using ontology.
The document discusses ontology matching, which is the process of finding relationships between entities in different ontologies. It describes various techniques for ontology matching including basic techniques that operate at the element-level or structure-level, as well as classifications of matching techniques based on the type of input used and level of interpretation. The document also provides examples of commonly used methods for ontology matching like string-based, language-based, and structure-based techniques.
Towards Ontology Development Based on Relational Databaseijbuiiir1
Ontology is defined as the formal explicit specification of a shared conceptualization. It has been widely used in almost all fields especially artificial intelligence, data mining, and semantic web etc. It is constructed using various set of resources. Now it has become a very important task to improve the efficiency of ontology construction. In order to improve the efficiency, need an automated method of building ontology from database resource. Since manual construction is found to be erroneous and not up to the expectation, automatic construction of ontology from database is innovated. Then the construction rules for ontology building from relational data sources are put forward. Finally, ontology for �automated building of ontology from relational data sources� has been implemented
CONTEMPORARY SEMANTIC WEB SERVICE FRAMEWORKS: AN OVERVIEW AND COMPARISONSijwscjournal
The growing proliferation of distributed information systems, allows organizations to offer their business processes to a worldwide audience through Web services. Semantic Web services have emerged as a means to achieve the vision of automatic discovery, selection, composition, and invocation of Web services by encoding the specifications of these software components in an unambiguous and machine-interpretable form. Several frameworks have been devised as enabling technologies for Semantic Web services. In this paper, we survey the prominent Semantic Web service frameworks. In addition, a set of criteria is identified and the discussed frameworks are evaluated and compared with respect to these criteria. Knowing the strengths and weaknesses of the Semantic Web service frameworks can help researchers to utilize the most appropriate one according to their needs.
CONTEMPORARY SEMANTIC WEB SERVICE FRAMEWORKS: AN OVERVIEW AND COMPARISONSijwscjournal
The growing proliferation of distributed information systems, allows organizations to offer their business processes to a worldwide audience through Web services. Semantic Web services have emerged as a means to achieve the vision of automatic discovery, selection, composition, and invocation of Web services by encoding the specifications of these software components in an unambiguous and machine-interpretable
form. Several frameworks have been devised as enabling technologies for Semantic Web services. In this paper, we survey the prominent Semantic Web service frameworks. In addition, a set of criteria is identified and the discussed frameworks are evaluated and compared with respect to these criteria. Knowing the strengths and weaknesses of the Semantic Web service frameworks can help researchers to utilize the most appropriate one according to their needs.
it's our presentation during the third international conference of information systems and technologies ICIST 2013 held at Tangier, Morocco in which we propose a new approach for human assessment of ontologies using an online questionnaire.
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING cscpconf
In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used toface the great challenge of representing the semantics of data, in order to bring the actual web to its full
power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. In deed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the
main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former
analysis the concept’s names and the later analysis their properties. Each one of these two sub-modules is
it self based on the combination of lexical and semantic similarity measures.
The document discusses the basics of ontologies, including their origin in philosophy, definitions, types, benefits and application areas. Some key points are:
- An ontology is a formal specification of a conceptualization used to help humans and programs share knowledge. It establishes a shared vocabulary for exchanging information.
- Ontologies describe domain knowledge and provide an agreed-upon understanding of a domain through concepts and relations. They help solve problems of ambiguity and enable knowledge sharing.
- Ontologies benefit applications like information retrieval, digital libraries, knowledge engineering and natural language processing by facilitating semantic search and integration of data.
This document discusses an integrated approach to ontology development methodology and provides a case study using a shopping mall domain. It begins by reviewing existing ontology development methodologies and identifying their pitfalls. An integrated methodology is then proposed which aims to reduce these pitfalls. The key steps in the proposed methodology are: 1) capturing motivating user scenarios or keywords, 2) generating formal/informal questions and answers from the scenarios, 3) extracting terms and constraints, and 4) building the ontology using a top-down approach. The methodology is applied to developing an ontology for a shopping mall domain to provide multilingual information to visitors.
This document summarizes a workshop on data integration using ontologies. It discusses how data integration is challenging due to differences in schemas, semantics, measurements, units and labels across data sources. It proposes that ontologies can help with data integration by providing definitions for schemas and entities referred to in the data. Core challenges discussed include dealing with multiple synonyms for entities and relationships between biological entities that depend on context. The document advocates for shared community ontologies that can be extended and integrated to facilitate flexible and responsive data integration across multiple sources.
ONTOLOGY VISUALIZATION PROTÉGÉ TOOLS – A REVIEW ijait
The document discusses ontology visualization tools in Protégé. It reviews four main visualization methods used in Protégé tools: indented list, node-link and tree, zoomable, and focus+context. It then examines specific Protégé tools that use each method, including their key features and limitations. The tools assessed are Protégé Class Browser (indented list), Protégé OntoViz and OntoSphere (node-link and tree), Jambalaya (zoomable), and Protégé TGVizTab (focus+context). The document concludes by summarizing and comparing the visualization characteristics of these Protégé tools.
Use of ontologies in natural language processingATHMAN HAJ-HAMOU
The document discusses the limitations of classical natural language processing approaches that rely solely on text-based search and statistics. It describes how ontologies can help address these limitations by representing knowledge at the semantic level rather than just words. The document outlines how ontologies can solve problems related to vagueness, high-level concepts, semantic relations, and time dimensions. It then summarizes two research papers that explore using ontologies to index and categorize documents as well as provide multiple views and dimensions to explore a document collection.
Using ontology for natural language processing can help mediate human-machine communication. Ontologies provide a formal representation of knowledge that can be used in natural language processing. The document discusses how general and specific ontologies can be combined and used with natural language processing techniques like machine translation and speech recognition. It also describes how ontologies can be extended over time using artificial neural networks to improve natural language understanding abilities.
This document discusses ontology-based data access. It begins by defining ontology as a representation of concepts and relationships that define a domain. It then provides examples of ontology elements like concepts, attributes, and relations. It describes how ontologies can be used to share understanding, enable knowledge reuse, and separate domain from operational knowledge. The document outlines the process for developing ontologies including scope, capture, encoding, integration, and evaluation. It discusses using ontologies to provide a user-oriented view of data and facilitate query access across data sources. The document concludes by discussing ongoing work on semantic query analysis and graphical ontology mapping tools.
The document summarizes and compares schema matching and ontology mapping. It discusses how schema matching approaches can be applied to ontology mapping given the similarities between schemas and ontologies. The document outlines different categories of schema matching techniques (element-based, structure-based) and provides examples. It also summarizes several ontology mapping tools and approaches that utilize different matching strategies like string, structure, and semantic similarity.
Ontology and Ontology Libraries: a Critical StudyDebashisnaskar
The concept of digital library revolutionized its popularity with the development of networking technology. Digital library stores various kind of documents in digitized format that enables user smooth access to these documents at subsidized costs. In the recent past, a similar concept i.e., ontology library has gained popularity among the communities like semantic web, artificial intelligence, information science, philosophy, linguistics, and so forth.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This paper proposes Natural language based Discourse Analysis method used for extracting
information from the news article of different domain. The Discourse analysis used the Rhetorical Structure
theory which is used to find coherent group of text which are most prominent for extracting information
from text. RST theory used the Nucleus- Satellite concept for finding most prominent text from the text
document. After Discourse analysis the text analysis has been done for extracting domain related object
and relates this object. For extracting the information knowledge based system has been used which
consist of domain dictionary .The domain dictionary has a bag of words for domain. The system is
evaluated according gold-of-art analysis and human decision for extracted information.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Many applications required integration of data from different sources, such as data mining and data / information fusion, etc., and the problem facing any project like this is that data structure different way and the terms and their meanings different from each other, and in this paper we will discuss the most important problems and how solve it using ontology.
The document discusses ontology matching, which is the process of finding relationships between entities in different ontologies. It describes various techniques for ontology matching including basic techniques that operate at the element-level or structure-level, as well as classifications of matching techniques based on the type of input used and level of interpretation. The document also provides examples of commonly used methods for ontology matching like string-based, language-based, and structure-based techniques.
Towards Ontology Development Based on Relational Databaseijbuiiir1
Ontology is defined as the formal explicit specification of a shared conceptualization. It has been widely used in almost all fields especially artificial intelligence, data mining, and semantic web etc. It is constructed using various set of resources. Now it has become a very important task to improve the efficiency of ontology construction. In order to improve the efficiency, need an automated method of building ontology from database resource. Since manual construction is found to be erroneous and not up to the expectation, automatic construction of ontology from database is innovated. Then the construction rules for ontology building from relational data sources are put forward. Finally, ontology for �automated building of ontology from relational data sources� has been implemented
CONTEMPORARY SEMANTIC WEB SERVICE FRAMEWORKS: AN OVERVIEW AND COMPARISONSijwscjournal
The growing proliferation of distributed information systems, allows organizations to offer their business processes to a worldwide audience through Web services. Semantic Web services have emerged as a means to achieve the vision of automatic discovery, selection, composition, and invocation of Web services by encoding the specifications of these software components in an unambiguous and machine-interpretable form. Several frameworks have been devised as enabling technologies for Semantic Web services. In this paper, we survey the prominent Semantic Web service frameworks. In addition, a set of criteria is identified and the discussed frameworks are evaluated and compared with respect to these criteria. Knowing the strengths and weaknesses of the Semantic Web service frameworks can help researchers to utilize the most appropriate one according to their needs.
CONTEMPORARY SEMANTIC WEB SERVICE FRAMEWORKS: AN OVERVIEW AND COMPARISONSijwscjournal
The growing proliferation of distributed information systems, allows organizations to offer their business processes to a worldwide audience through Web services. Semantic Web services have emerged as a means to achieve the vision of automatic discovery, selection, composition, and invocation of Web services by encoding the specifications of these software components in an unambiguous and machine-interpretable
form. Several frameworks have been devised as enabling technologies for Semantic Web services. In this paper, we survey the prominent Semantic Web service frameworks. In addition, a set of criteria is identified and the discussed frameworks are evaluated and compared with respect to these criteria. Knowing the strengths and weaknesses of the Semantic Web service frameworks can help researchers to utilize the most appropriate one according to their needs.
Mapping of extensible markup language-to-ontology representation for effectiv...IAESIJAI
Extensible markup language (XML) is well-known as the standard for data exchange over the internet. It is flexible and has high expressibility to express the relationship between the data stored. Yet, the structural complexity and the semantic relationships are not well expressed. On the other hand, ontology models the structural, semantic and domain knowledge effectively. By combining ontology with visualization effect, one will be able to have a closer view based on respective user requirements. In this paper, we propose several mapping rules for the transformation of XML into ontology representation. Subsequently, we show how the ontology is constructed based on the proposed rules using the sample domain ontology in University of Wisconsin-Milwaukee (UWM) and mondial datasets. We
also look at the schemas, query workload, and evaluation, to derive the extended knowledge from the existing ontology. The correctness of the ontology representation has been proven effective through supporting various types of complex queries in simple protocol and resource description framework query language (SPARQL) language.
Ontology languages are used in modelling the semantics of concepts within a particular domain and the relationships between those concepts. The Semantic Web standard provides a number of modelling languages that differ in their level of expressivity and are organized in a Semantic Web Stack in such a way that each language level builds on the expressivity of the other. There are several problems when one attempts to use independently developed ontologies. When existing ontologies are adapted for new purposes it requires that certain operations are performed on them. These operations are currently performed in a semi-automated manner. This paper seeks to model categorically the syntax and semantics of RDF ontology as a step towards the formalization of ontological operations using category theory.
An Incremental Method For Meaning Elicitation Of A Domain OntologyAudrey Britton
This document describes MELIS (Meaning Elicitation and Lexical Integration System), a tool developed to support the annotation of data sources with conceptual and lexical information during the ontology generation process in MOMIS (Mediator envirOnment for Multiple Information Systems). MELIS improves upon MOMIS by enabling more automated annotation of data sources and by extracting relationships between lexical elements using lexical and domain knowledge to provide a richer semantics. The document outlines the MOMIS architecture integrated with MELIS and explains how MELIS supports key steps in the ontology creation process, including source annotation, common thesaurus generation, and relationship extraction.
Towards From Manual to Automatic Semantic Annotation: Based on Ontology Eleme...IJwest
This document describes a proposed system for automatic semantic annotation of web documents based on ontology elements and relationships. It begins with an introduction to semantic web and annotation. The proposed system architecture matches topics in text to entities in an ontology document. It utilizes WordNet as a lexical ontology and ontology resources to extract knowledge from text and generate annotations. The main components of the system include a text analyzer, ontology parser, and knowledge extractor. The system aims to automatically generate metadata to improve information retrieval for non-technical users.
Semantic Annotation: The Mainstay of Semantic WebEditor IJCATR
Given that semantic Web realization is based on the critical mass of metadata accessibility and the representation of data with formal
knowledge, it needs to generate metadata that is specific, easy to understand and well-defined. However, semantic annotation of the
web documents is the successful way to make the Semantic Web vision a reality. This paper introduces the Semantic Web and its
vision (stack layers) with regard to some concept definitions that helps the understanding of semantic annotation. Additionally, this
paper introduces the semantic annotation categories, tools, domains and models
An approach for transforming of relational databases to owl ontologyIJwest
Rapid growth of documents, web pages, and other types of text content is a huge challenge for the modern content management systems. One of the problems in the areas of information storage and retrieval is the lacking of semantic data. Ontologies can present knowledge in sharable and repeatedly usable manner and provide an effective way to reduce the data volume overhead by encoding the structure of a particular domain. Metadata in relational databases can be used to extract ontology from database in a special domain. According to solve the problem of sharing and reusing of data, approaches based on transforming relational database to ontology are proposed. In this paper we propose a method for automatic ontology construction based on relational database. Mining and obtaining further components from relational database leads to obtain knowledge with high semantic power and more expressiveness. Triggers are one of the database components which could be transformed to the ontology model and increase the amount of power and expressiveness of knowledge by presenting part of the knowledge dynamically.
An Approach to Owl Concept Extraction and Integration Across Multiple Ontolog...dannyijwest
Increase in number of ontologies on Semantic Web and endorsement of OWL as language of discourse for
the Semantic Web has lead to a scenario where research efforts in the field of ontology engineering may be
applied for making the process of ontology development through reuse a viable option for ontology
developers. The advantages are twofold as when existing ontological artefacts from the Semantic Web are
reused, semantic heterogeneity is reduced and help in interoperability which is the essence of Semantic
Web. From the perspective of ontology development advantages of reuse are in terms of cutting down on
cost as well as development life as ontology engineering requires expert domain skills and is time taking
process. We have devised a framework to address challenges associated with reusing ontologies from the
Semantic Web. In this paper we present methods adopted for extraction and integration of concepts across
multiple ontologies.
ONTOLOGY VISUALIZATION PROTÉGÉ TOOLS – A REVIEWijait
The document discusses ontology visualization tools in Protégé. It reviews four main visualization methods used in Protégé tools: indented list, node-link and tree, zoomable, and focus+context. It then examines specific Protégé tools that use each method, including their key features and limitations. The tools discussed are Protégé Class Browser (indented list), Protégé OntoViz and OntoSphere (node-link and tree), Jambalaya (zoomable), and Protégé TGVizTab (focus+context). The document aims to categorize the characteristics of existing Protégé visualization tools to assist in method selection and promote future research.
Semantic Web: Technolgies and Applications for Real-WorldAmit Sheth
Amit Sheth and Susie Stephens, "Semantic Web: Technolgies and Applications for Real-World," Tutorial at 2007 World Wide Web Conference, Banff, Canada.
Tutorial discusses technologies and deployed real-world applications through 2007.
Tutorial description at: http://www2007.org/tutorial-T11.php
An Annotation Framework For The Semantic WebAndrea Porter
This paper presents an ontology-based annotation framework called OntoAnnotate that supports the creation of semantically interlinked metadata. The framework addresses issues like avoiding duplicate annotations, managing changes to ontologies over time, and recognizing duplicate documents. OntoAnnotate allows annotators to create objects from text passages in documents and semantically relate them using properties from domain ontologies. This framework aims to enable the generation of semantically useful metadata for intelligent agents on the Semantic Web.
Topic Modeling : Clustering of Deep Webpagescsandit
The internet is comprised of massive amount of info
rmation in the form of zillions of web
pages.This information can be categorized into the
surface web and the deep web. The existing
search engines can effectively make use of surface
web information.But the deep web remains
unexploited yet. Machine learning techniques have b
een commonly employed to access deep
web content.
Under Machine Learning, topic models provide a simp
le way to analyze large volumes of
unlabeled text. A "topic" consists of a cluster of
words that frequently occur together. Using
contextual clues, topic models can connect words wi
th similar meanings and distinguish
between words with multiple meanings. Clustering is
one of the key solutions to organize the
deep web databases.In this paper, we cluster deep w
eb databases based on the relevance found
among deep web forms by employing a generative prob
abilistic model called Latent Dirichlet
Allocation(LDA) for modeling content representative
of deep web databases. This is
implemented after preprocessing the set of web page
s to extract page contents and form
contents.Further, we contrive the distribution of “
topics per document” and “words per topic”
using the technique of Gibbs sampling. Experimental
results show that the proposed method
clearly outperforms the existing clustering methods
.
Topic Modeling : Clustering of Deep Webpagescsandit
The internet is comprised of massive amount of information in the form of zillions of web pages.This information can be categorized into the surface web and the deep web. The existing search engines can effectively make use of surface web information.But the deep web remains unexploited yet. Machine learning techniques have been commonly employed to access deep web content.
Under Machine Learning, topic models provide a simple way to analyze large volumes of unlabeled text. A "topic" consists of a cluster of words that frequently occur together. Using
contextual clues, topic models can connect words with similar meanings and distinguish between words with multiple meanings. Clustering is one of the key solutions to organize the deep web databases.In this paper, we cluster deep web databases based on the relevance found among deep web forms by employing a generative probabilistic model called Latent Dirichlet
Allocation(LDA) for modeling content representative of deep web databases. This is implemented after preprocessing the set of web pages to extract page contents and form
contents.Further, we contrive the distribution of “topics per document” and “words per topic”
using the technique of Gibbs sampling. Experimental results show that the proposed method clearly outperforms the existing clustering methods.
Feature analysis of ontology visualization methods and toolsCSITiaesprime
Visualization is a technique of creating images, graphs or animations to share knowledge. Different kinds of visualization methods and tools are available to envision the data in an efficient way. The visualization tools and techniques enable the user to understand the knowledge in an easy manner. Nowadays most of the information is presented semantically which provides knowledge based retrieval of the information. Knowledge based visualization tools are required to visualize semantic concepts. This article analyses the existing semantic based visualization tools and plug-ins. The features and characteristics of these tools and plug-ins are analyzed and tabulated.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
USING RELATIONAL MODEL TO STORE OWL ONTOLOGIES AND FACTScsandit
The storing and the processing of OWL instances are important subjects in database modeling.
Many research works have focused on the way of managing OWL instances efficiently. Some
systems store and manage OWL instances using relational models to ensure their persistence.
Nevertheless, several approaches keep only RDF triplets as instances in relational tables
explicitly, and the manner of structuring instances as graph and keeping links between concepts
is not taken into account. In this paper, we propose an architecture that permits relational
tables behave as an OWL model by adapting relational tables to OWL instances and an OWL
hierarchy structure. Therefore, two kinds of tables are used: facts or instances relational tables.
The tables hold instances and the OWL table holds a specification of how the concepts are
structured. Instances tables should conform to OWLtable to be valid. A mechanism of
construction of OWLtable and instances tables is defined in order to enable and enhance
inference and semantic querying of OWL in relational model context.
Ähnlich wie A Semi-Automatic Ontology Extension Method for Semantic Web Services (20)
Power System State Estimation - A ReviewIDES Editor
This document provides a review of power system state estimation techniques. It discusses both static and dynamic state estimation algorithms. For static state estimation, it covers weighted least squares, decoupled, and robust estimation methods. Weighted least squares is commonly used but can have numerical instability issues. Decoupled state estimation approximates the gain matrix for faster computation. Robust estimation uses M-estimators and other techniques to handle outliers and bad data. Dynamic state estimation applies Kalman filtering, leapfrog algorithms, and other methods to continuously monitor system states over time.
Artificial Intelligence Technique based Reactive Power Planning Incorporating...IDES Editor
This document summarizes a research paper that proposes using artificial intelligence techniques and FACTS controllers for reactive power planning in real-time power transmission systems. The paper formulates the reactive power planning problem and incorporates flexible AC transmission system (FACTS) devices like static VAR compensators (SVC), thyristor controlled series capacitors (TCSC), and unified power flow controllers (UPFC). Evolutionary algorithms like evolutionary programming (EP) and differential evolution (DE) are applied to find the optimal locations and settings of the FACTS controllers to minimize losses and costs. Simulation results on IEEE 30-bus and 72-bus Indian test systems show that UPFC performs best in reducing losses compared to SVC and TCSC.
Design and Performance Analysis of Genetic based PID-PSS with SVC in a Multi-...IDES Editor
Damping of power system oscillations with the help
of proposed optimal Proportional Integral Derivative Power
System Stabilizer (PID-PSS) and Static Var Compensator
(SVC)-based controllers are thoroughly investigated in this
paper. This study presents robust tuning of PID-PSS and
SVC-based controllers using Genetic Algorithms (GA) in
multi machine power systems by considering detailed model
of the generators (model 1.1). The effectiveness of FACTSbased
controllers in general and SVC-based controller in
particular depends upon their proper location. Modal
controllability and observability are used to locate SVC–based
controller. The performance of the proposed controllers is
compared with conventional lead-lag power system stabilizer
(CPSS) and demonstrated on 10 machines, 39 bus New England
test system. Simulation studies show that the proposed genetic
based PID-PSS with SVC based controller provides better
performance.
Optimal Placement of DG for Loss Reduction and Voltage Sag Mitigation in Radi...IDES Editor
This paper presents the need to operate the power
system economically and with optimum levels of voltages has
further led to an increase in interest in Distributed
Generation. In order to reduce the power losses and to improve
the voltage in the distribution system, distributed generators
(DGs) are connected to load bus. To reduce the total power
losses in the system, the most important process is to identify
the proper location for fixing and sizing of DGs. It presents a
new methodology using a new population based meta heuristic
approach namely Artificial Bee Colony algorithm(ABC) for
the placement of Distributed Generators(DG) in the radial
distribution systems to reduce the real power losses and to
improve the voltage profile, voltage sag mitigation. The power
loss reduction is important factor for utility companies because
it is directly proportional to the company benefits in a
competitive electricity market, while reaching the better power
quality standards is too important as it has vital effect on
customer orientation. In this paper an ABC algorithm is
developed to gain these goals all together. In order to evaluate
sag mitigation capability of the proposed algorithm, voltage
in voltage sensitive buses is investigated. An existing 20KV
network has been chosen as test network and results are
compared with the proposed method in the radial distribution
system.
Line Losses in the 14-Bus Power System Network using UPFCIDES Editor
Controlling power flow in modern power systems
can be made more flexible by the use of recent developments
in power electronic and computing control technology. The
Unified Power Flow Controller (UPFC) is a Flexible AC
transmission system (FACTS) device that can control all the
three system variables namely line reactance, magnitude and
phase angle difference of voltage across the line. The UPFC
provides a promising means to control power flow in modern
power systems. Essentially the performance depends on proper
control setting achievable through a power flow analysis
program. This paper presents a reliable method to meet the
requirements by developing a Newton-Raphson based load
flow calculation through which control settings of UPFC can
be determined for the pre-specified power flow between the
lines. The proposed method keeps Newton-Raphson Load Flow
(NRLF) algorithm intact and needs (little modification in the
Jacobian matrix). A MATLAB program has been developed to
calculate the control settings of UPFC and the power flow
between the lines after the load flow is converged. Case studies
have been performed on IEEE 5-bus system and 14-bus system
to show that the proposed method is effective. These studies
indicate that the method maintains the basic NRLF properties
such as fast computational speed, high degree of accuracy and
good convergence rate.
Study of Structural Behaviour of Gravity Dam with Various Features of Gallery...IDES Editor
The size and shape of opening in dam causes the
stress concentration, it also causes the stress variation in the
rest of the dam cross section. The gravity method of the analysis
does not consider the size of opening and the elastic property
of dam material. Thus the objective of study is comprises of
the Finite Element Method which considers the size of
opening, elastic property of material, and stress distribution
because of geometric discontinuity in cross section of dam.
Stress concentration inside the dam increases with the opening
in dam which results in the failure of dam. Hence it is
necessary to analyses large opening inside the dam. By making
the percentage area of opening constant and varying size and
shape of opening the analysis is carried out. For this purpose
a section of Koyna Dam is considered. Dam is defined as a
plane strain element in FEM, based on geometry and loading
condition. Thus this available information specified our path
of approach to carry out 2D plane strain analysis. The results
obtained are then compared mutually to get most efficient
way of providing large opening in the gravity dam.
Assessing Uncertainty of Pushover Analysis to Geometric ModelingIDES Editor
Pushover Analysis a popular tool for seismic
performance evaluation of existing and new structures and is
nonlinear Static procedure where in monotonically increasing
loads are applied to the structure till the structure is unable
to resist the further load .During the analysis, whatever the
strength of concrete and steel is adopted for analysis of
structure may not be the same when real structure is
constructed and the pushover analysis results are very sensitive
to material model adopted, geometric model adopted, location
of plastic hinges and in general to procedure followed by the
analyzer. In this paper attempt has been made to assess
uncertainty in pushover analysis results by considering user
defined hinges and frame modeled as bare frame and frame
with slab modeled as rigid diaphragm and results compared
with experimental observations. Uncertain parameters
considered includes the strength of concrete, strength of steel
and cover to the reinforcement which are randomly generated
and incorporated into the analysis. The results are then
compared with experimental observations.
Secure Multi-Party Negotiation: An Analysis for Electronic Payments in Mobile...IDES Editor
This document summarizes and analyzes secure multi-party negotiation protocols for electronic payments in mobile computing. It presents a framework for secure multi-party decision protocols using lightweight implementations. The main focus is on synchronizing security features to avoid agreement manipulation and reduce user traffic. The paper describes negotiation between an auctioneer and bidders, showing multiparty security is better than existing systems. It analyzes the performance of encryption algorithms like ECC, XTR, and RSA for use in the multiparty negotiation protocols.
Selfish Node Isolation & Incentivation using Progressive ThresholdsIDES Editor
The problems associated with selfish nodes in
MANET are addressed by a collaborative watchdog approach
which reduces the detection time for selfish nodes thereby
improves the performance and accuracy of watchdogs[1]. In
the related works they make use of credit based systems, reputation
based mechanisms, pathrater and watchdog mechanism
to detect such selfish nodes. In this paper we follow an approach
of collaborative watchdog which reduces the detection
time for selfish nodes and also involves the removal of such
selfish nodes based on some progressively assessed thresholds.
The threshold gives the nodes a chance to stop misbehaving
before it is permanently deleted from the network.
The node passes through several isolation processes before it
is permanently removed. Another version of AODV protocol
is used here which allows the simulation of selfish nodes in
NS2 by adding or modifying log files in the protocol.
Various OSI Layer Attacks and Countermeasure to Enhance the Performance of WS...IDES Editor
Wireless sensor networks are networks having non
wired infrastructure and dynamic topology. In OSI model each
layer is prone to various attacks, which halts the performance
of a network .In this paper several attacks on four layers of
OSI model are discussed and security mechanism is described
to prevent attack in network layer i.e wormhole attack. In
Wormhole attack two or more malicious nodes makes a covert
channel which attracts the traffic towards itself by depicting a
low latency link and then start dropping and replaying packets
in the multi-path route. This paper proposes promiscuous mode
method to detect and isolate the malicious node during
wormhole attack by using Ad-hoc on demand distance vector
routing protocol (AODV) with omnidirectional antenna. The
methodology implemented notifies that the nodes which are
not participating in multi-path routing generates an alarm
message during delay and then detects and isolate the
malicious node from network. We also notice that not only
the same kind of attacks but also the same kind of
countermeasures can appear in multiple layer. For example,
misbehavior detection techniques can be applied to almost all
the layers we discussed.
Responsive Parameter based an AntiWorm Approach to Prevent Wormhole Attack in...IDES Editor
The recent advancements in the wireless technology
and their wide-spread deployment have made remarkable
enhancements in efficiency in the corporate and industrial
and Military sectors The increasing popularity and usage of
wireless technology is creating a need for more secure wireless
Ad hoc networks. This paper aims researched and developed
a new protocol that prevents wormhole attacks on a ad hoc
network. A few existing protocols detect wormhole attacks but
they require highly specialized equipment not found on most
wireless devices. This paper aims to develop a defense against
wormhole attacks as an Anti-worm protocol which is based on
responsive parameters, that does not require as a significant
amount of specialized equipment, trick clock synchronization,
no GPS dependencies.
Cloud Security and Data Integrity with Client Accountability FrameworkIDES Editor
This document summarizes a proposed cloud security and data integrity framework that provides client accountability. The framework aims to address issues like lack of user control over cloud data, need for data transparency and tracking, and ensuring data integrity. It proposes using JAR (Java Archive) files for data sharing due to benefits like portability. The framework incorporates client-side verification using MD5 hashing, digital signature-based authentication of JAR files, and use of HMAC to ensure data integrity. It also uses password-based encryption of log files to keep them tamper-proof. The framework is intended to provide both accountability and security for data sharing in cloud environments.
Genetic Algorithm based Layered Detection and Defense of HTTP BotnetIDES Editor
A System state in HTTP botnet uses HTTP protocol
for the creation of chain of Botnets thereby compromising
other systems. By using HTTP protocol and port number 80,
attacks can not only be hidden but also pass through the
firewall without being detected. The DPR based detection
leads to better analysis of botnet attacks [3]. However, it
provides only probabilistic detection of the attacker and also
time consuming and error prone. This paper proposes a Genetic
algorithm based layered approach for detecting as well as
preventing botnet attacks. The paper reviews p2p firewall
implementation which forms the basis of filtering.
Performance evaluation is done based on precision, F-value
and probability. Layered approach reduces the computation
and overall time requirement [7]. Genetic algorithm promises
a low false positive rate.
Enhancing Data Storage Security in Cloud Computing Through SteganographyIDES Editor
This document summarizes a research paper that proposes a method for enhancing data security in cloud computing through steganography. The method hides user data in digital images stored on cloud servers. When data needs to be accessed, it is extracted from the images. The document outlines the cloud architecture and security issues addressed. It then describes the proposed system architecture, security model, and data storage and retrieval process. Data is partitioned and hidden in multiple images to improve security. The goal is to prevent unauthorized access to user data stored on cloud servers.
The main tasks of a Wireless Sensor Network
(WSN) are data collection from its nodes and communication
of this data to the base station (BS). The protocols used for
communication among the WSN nodes and between the WSN
and the BS, must consider the resource constraints of nodes,
battery energy, computational capabilities and memory. The
WSN applications involve unattended operation of the network
over an extended period of time. In order to extend the lifetime
of a WSN, efficient routing protocols need to be adopted. The
proposed low power routing protocol based on tree-based
network structure reliably forwards the measured data towards
the BS using TDMA. An energy consumption analysis of the
WSN making use of this protocol is also carried out. It is
found that the network is energy efficient with an average
duty cycle of 0:7% for the WSN nodes. The OmNET++
simulation platform along with MiXiM framework is made
use of.
Permutation of Pixels within the Shares of Visual Cryptography using KBRP for...IDES Editor
The security of authentication of internet based
co-banking services should not be susceptible to high risks.
The passwords are highly vulnerable to virus attacks due to
the lack of high end embedding of security methods. In order
for the passwords to be more secure, people are generally
compelled to select jumbled up character based passwords
which are not only less memorable but are also equally prone
to insecurity. Multiple use of distributed shares has been
studied to solve the problem of authentication by algorithms
based on thresholding of pixels in image processing and visual
cryptography concepts where the subset of shares is considered
for the recovery of the original image for authentication using
correlation function[1][2].The main disadvantage in the above
study is the plain storage of shares and also one of the shares
is being supplied to the customer, which will lead to the
possibility of misuse by a third party. This paper proposes a
technique for scrambling of pixels by key based random
permutation (KBRP) within the shares before the
authentication has been attempted. Total number of shares to
be created is dependent on the multiplicity of ownership of
the account. By this method the problem of uncertainty among
the customers with regard to security, storage, retrieval of
holding of half of the shares is minimized.
This paper presents a trifocal Rotman Lens Design
approach. The effects of focal ratio and element spacing on
the performance of Rotman Lens are described. A three beam
prototype feeding 4 element antenna array working in L-band
has been simulated using RLD v1.7 software. Simulated
results show that the simulated lens has a return loss of –
12.4dB at 1.8GHz. Beam to array port phase error variation
with change in the focal ratio and element spacing has also
been investigated.
Band Clustering for the Lossless Compression of AVIRIS Hyperspectral ImagesIDES Editor
Hyperspectral images can be efficiently compressed
through a linear predictive model, as for example the one
used in the SLSQ algorithm. In this paper we exploit this
predictive model on the AVIRIS images by individuating,
through an off-line approach, a common subset of bands, which
are not spectrally related with any other bands. These bands
are not useful as prediction reference for the SLSQ 3-D
predictive model and we need to encode them via other
prediction strategies which consider only spatial correlation.
We have obtained this subset by clustering the AVIRIS bands
via the clustering by compression approach. The main result
of this paper is the list of the bands, not related with the
others, for AVIRIS images. The clustering trees obtained for
AVIRIS and the relationship among bands they depict is also
an interesting starting point for future research.
Microelectronic Circuit Analogous to Hydrogen Bonding Network in Active Site ...IDES Editor
A microelectronic circuit of block-elements
functionally analogous to two hydrogen bonding networks is
investigated. The hydrogen bonding networks are extracted
from â-lactamase protein and are formed in its active site.
Each hydrogen bond of the network is described in equivalent
electrical circuit by three or four-terminal block-element.
Each block-element is coded in Matlab. Static and dynamic
analyses are performed. The resultant microelectronic circuit
analogous to the hydrogen bonding network operates as
current mirror, sine pulse source, triangular pulse source as
well as signal modulator.
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...IDES Editor
In this paper a method is proposed to discriminate
real world scenes in to natural and manmade scenes of similar
depth. Global-roughness of a scene image varies as a function
of image-depth. Increase in image depth leads to increase in
roughness in manmade scenes; on the contrary natural scenes
exhibit smooth behavior at higher image depth. This particular
arrangement of pixels in scene structure can be well explained
by local texture information in a pixel and its neighborhood.
Our proposed method analyses local texture information of a
scene image using texture unit matrix. For final classification
we have used both supervised and unsupervised learning using
K-Nearest Neighbor classifier (KNN) and Self Organizing
Map (SOM) respectively. This technique is useful for online
classification due to very less computational complexity.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Fueling AI with Great Data with Airbyte WebinarZilliz
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Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP