Semantic Enriched Service Environments
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Presentation for final dissertation defense. Look for a PDF with notes and also a copy of my dissertation in my slidespace.
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The recent emergence of the “Linked Data” approach for publishing data represents a major step forward in realizing the original vision of a web that can "understand and satisfy the requests of people and machines to use the web content" – i.e. the Semantic Web. This new approach has resulted in the Linked Open Data (LOD) Cloud, which includes more than 70 large datasets contributed by experts belonging to diverse communities such as geography, entertainment, and life sciences. However, the current interlinks between datasets in the LOD Cloud – as we will illustrate – are too shallow to realize much of the benefits promised. If this limitation is left unaddressed, then the LOD Cloud will merely be more data that suffers from the same kinds of problems, which plague the Web of Documents, and hence the vision of the Semantic Web will fall short.
This thesis presents a comprehensive solution to address the issue of alignment and relationship identification using a bootstrapping based approach. By alignment we mean the process of determining correspondences between classes and properties of ontologies. We identify subsumption, equivalence and part-of relationship between classes. The work identifies part-of relationship between instances. Between properties we will establish subsumption and equivalence relationship. By bootstrapping we mean the process of being able to utilize the information which is contained within the datasets for improving the data within them. The work showcases use of bootstrapping based methods to identify and create richer relationships between LOD datasets. The BLOOMS project (http://wiki.knoesis.org/index.php/BLOOMS) and the PLATO project, both built as part of this research, have provided evidence to the feasibility and the applicability of the solution.Prateek Jain's Dissertation Defense - Linked Open Data Alignment and Querying
Prateek Jain's Dissertation Defense - Linked Open Data Alignment and QueryingArtificial Intelligence Institute at UofSC
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The recent emergence of the “Linked Data” approach for publishing data represents a major step forward in realizing the original vision of a web that can "understand and satisfy the requests of people and machines to use the web content" – i.e. the Semantic Web. This new approach has resulted in the Linked Open Data (LOD) Cloud, which includes more than 70 large datasets contributed by experts belonging to diverse communities such as geography, entertainment, and life sciences. However, the current interlinks between datasets in the LOD Cloud – as we will illustrate – are too shallow to realize much of the benefits promised. If this limitation is left unaddressed, then the LOD Cloud will merely be more data that suffers from the same kinds of problems, which plague the Web of Documents, and hence the vision of the Semantic Web will fall short.
This thesis presents a comprehensive solution to address the issue of alignment and relationship identification using a bootstrapping based approach. By alignment we mean the process of determining correspondences between classes and properties of ontologies. We identify subsumption, equivalence and part-of relationship between classes. The work identifies part-of relationship between instances. Between properties we will establish subsumption and equivalence relationship. By bootstrapping we mean the process of being able to utilize the information which is contained within the datasets for improving the data within them. The work showcases use of bootstrapping based methods to identify and create richer relationships between LOD datasets. The BLOOMS project (http://wiki.knoesis.org/index.php/BLOOMS) and the PLATO project, both built as part of this research, have provided evidence to the feasibility and the applicability of the solution.Prateek Jain's Dissertation Defense - Linked Open Data Alignment and Querying
Prateek Jain's Dissertation Defense - Linked Open Data Alignment and QueryingArtificial Intelligence Institute at UofSC
Dissertation Defense:
" Mining and Analyzing Subjective Experiences in User Generated Content "
By Lu Chen
Tuesday, April 9, 2016
Dissertation Committee: Dr. Amit Sheth, Advisor, Dr. T. K. Prasad, Dr. Keke Chen, Dr. Ingmar Weber, and Dr. Justin Martineau,
Pictures: https://www.facebook.com/Kno.e.sis/photos/?tab=album&album_id=1225911137443732
Video: https://youtu.be/tzLEUB-hggQ
Lu's Home page: http://knoesis.wright.edu/researchers/luchen/
ABSTRACT
Web 2.0 and social media enable people to create, share and discover information instantly anywhere, anytime. A great amount of this information is subjective information -- the information about people's subjective experiences, ranging from feelings of what is happening in our daily lives to opinions on a wide variety of topics. Subjective information is useful to individuals, businesses, and government agencies to support decision making in areas such as product purchase, marketing strategy, and policy making. However, much useful subjective information is buried in ever-growing user generated data on social media platforms, it is still difficult to extract high quality subjective information and make full use of it with current technologies.
Current subjectivity and sentiment analysis research has largely focused on classifying the text polarity -- whether the expressed opinion regarding a specific topic in a given text is positive, negative, or neutral. This narrow definition does not take into account the other types of subjective information such as emotion, intent, and preference, which may prevent their exploitation from reaching its full potential. This dissertation extends the definition and introduces a unified framework for mining and analyzing diverse types of subjective information. We have identified four components of a subjective experience: an individual who holds it, a target that elicits it (e.g., a movie, or an event), a set of expressions that describe it (e.g., "excellent", "exciting"), and a classification or assessment that characterize it (e.g., positive vs. negative). Accordingly, this dissertation makes contributions in developing novel and general techniques for the tasks of identifying and extracting these components.
We first explore the task of extracting sentiment expressions from social media posts. We propose an optimization-based approach that extracts a diverse set of sentiment-bearing expressions, including formal and slang words/phrases, for a given target from an unlabeled corpus. Instead of associating the overall sentiment with a given text, this method assesses the more fine-grained target-dependent polarity of each sentiment expression. Unlike pattern-based approaches which often fail to capture the diversity of sentiment expressions due to the informal nature of language usage and writing style in social media posts, the proposed approach is capable of identifying sentiment phraseMining and Analyzing Subjective Experiences in User-generated Content
Mining and Analyzing Subjective Experiences in User-generated ContentArtificial Intelligence Institute at UofSC
Understanding users’ latent intents behind search queries is essential for satisfying a user’s search needs. Search intent mining can help search engines to enhance its ranking of search results, enabling new search features like instant answers, personalization, search result diversification, and the recommendation of more relevant ads. Consequently, there has been increasing attention on studying how to effectively mine search intents by analyzing search engine query logs. While state-of-the-art techniques can identify the domain of the queries (e.g. sports, movies, health), identifying domain-specific intent is still an open problem. Among all the topics available on the Internet, health is one of the most important in terms of impact on the user and it is one of the most frequently searched areas. This dissertation presents a knowledge-driven approach for domain-specific search intent mining with a focus on health-related search queries.
First, we identified 14 consumer-oriented health search intent classes based on inputs from focus group studies and based on analyses of popular health websites, literature surveys, and an empirical study of search queries. We defined the problem of classifying millions of health search queries into zero or more intent classes as a multi-label classification problem. Popular machine learning approaches for multi-label classification tasks (namely, problem transformation and algorithm adaptation methods) were not feasible due to the limitation of label data creations and health domain constraints. Another challenge in solving the search intent identification problem was mapping terms used by laymen to medical terms. To address these challenges, we developed a semantics-driven, rule-based search intent mining approach leveraging rich background knowledge encoded in Unified Medical Language System (UMLS) and a crowd sourced encyclopedia (Wikipedia). The approach can identify search intent in a disease-agnostic manner and has been evaluated on three major diseases.
While users often turn to search engines to learn about health conditions, a surprising amount of health information is also shared and consumed via social media, such as public social platforms like Twitter. Although Twitter is an excellent information source, the identification of informative tweets from the deluge of tweets is the major challenge. We used a hybrid approach consisting of supervised machine learning, rule-based classifiers, and biomedical domain knowledge to facilitate the retrieval of relevant and reliable health information shared on Twitter in real time. Furthermore, we extended our search intent mining algorithm to classify health-related tweets into health categories. Finally, we performed a large-scale study to compare health search intents and features that contribute in the expression of search intent from 100+ million search queries from smarts devices (smartphones/tablets) and personal computers (desktops/laptops)Ashutosh Jadhav PhD Defense: Knowledge Driven Search Intent Mining
Ashutosh Jadhav PhD Defense: Knowledge Driven Search Intent MiningArtificial Intelligence Institute at UofSC
Video of the talk: https://www.youtube.com/watch?v=7k-u_TUew3o
Abstract: Social media has experienced immense growth in recent times. These platforms are becoming increasingly common for information seeking and consumption, and as part of its growing popularity, information overload pose a significant challenge to users. For instance, Twitter alone generates around 500 million tweets per day and it is impractical for users to have to parse through such an enormous stream to find information that are interesting to them. This situation necessitates efficient personalized filtering mechanisms for users to consume relevant, interesting information from social media.
Building a personalized filtering system involves understanding users interests and utilizing these interests to deliver relevant information to users. These tasks primarily include analyzing and processing social media text which is challenging due to its shortness in length, and the real-time nature of the medium. The challenges include: (1) Lack of semantic context: Social Media posts are on an average short in length, which provides limited semantic context to perform textual analysis. This is particularly detrimental for topic identification which is a necessary task for mining users interests; (2) Dynamically changing vocabulary: Most social media websites such as Twitter and Facebook generate posts that are of current (timely) interests to the users. Due to this real-time nature, information relevant to dynamic topics of interest evolve reflecting the changes in the real world. This in turn changes the vocabulary associated with these dynamic topics of interest making it harder to filter relevant information; (3) Scalability: The number of users on social media platforms are significantly large, which is difficult for centralized systems to scale to deliver relevant information to users. This dissertation is devoted to exploring semantic techniques and Semantic Web technologies to address the above mentioned challenges in building a personalized information filtering system for social media. Particularly, the necessary semantics (knowledge) is derived from crowd sourced knowledge bases such as Wikipedia to improve context for understanding short-text and dynamic topics on social media.
Personalized and Adaptive Semantic Information Filtering for Social Media - P...
Personalized and Adaptive Semantic Information Filtering for Social Media - P...Artificial Intelligence Institute at UofSC
There is a rapid intertwining of sensors and mobile devices into the fabric of our lives. This has resulted in unprecedented growth in the number of observations from the physical and social worlds reported in the cyber world. Sensing and computational components embedded in the physical world is termed as Cyber-Physical System (CPS). Current science of CPS is yet to effectively integrate citizen observations in CPS analysis. We demonstrate the role of citizen observations in CPS and propose a novel approach to perform a holistic analysis of machine and citizen sensor observations. Specifically, we demonstrate the complementary, corroborative, and timely aspects of citizen sensor observations compared to machine sensor observations in Physical-Cyber-Social (PCS) Systems.
Physical processes are inherently complex and embody uncertainties. They manifest as machine and citizen sensor observations in PCS Systems. We propose a generic framework to move from observations to decision-making and actions in PCS systems consisting of: (a) PCS event extraction, (b) PCS event understanding, and (c) PCS action recommendation. We demonstrate the role of Probabilistic Graphical Models (PGMs) as a unified framework to deal with uncertainty, complexity, and dynamism that help translate observations into actions. Data driven approaches alone are not guaranteed to be able to synthesize PGMs reflecting real-world dependencies accurately. To overcome this limitation, we propose to empower PGMs using the declarative domain knowledge. Specifically, we propose four techniques: (a) automatic creation of massive training data for Conditional Random Fields (CRFs) using domain knowledge of entities used in PCS event extraction, (b) Bayesian Network structure refinement using causal knowledge from Concept Net used in PCS event understanding, (c) knowledge-driven piecewise linear approximation of nonlinear time series dynamics using Linear Dynamical Systems (LDS) used in PCS event understanding, and the (d) transforming knowledge of goals and actions into a Markov Decision Process (MDP) model used in PCS action recommendation.
We evaluate the benefits of the proposed techniques on real-world applications involving traffic analytics and Internet of Things (IoT).Knowledge-empowered Probabilistic Graphical Models for Physical-Cyber-Social ...
Knowledge-empowered Probabilistic Graphical Models for Physical-Cyber-Social ...Artificial Intelligence Institute at UofSC
PhD defense held at Kno.e.sis Center, Wright State University, December 03, 2013.Pablo Mendes' Defense: Adaptive Semantic Annotation of Entity and Concept Men...
Pablo Mendes' Defense: Adaptive Semantic Annotation of Entity and Concept Men...Artificial Intelligence Institute at UofSC
Social media provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens share information, express opinions, and engage in discussions. Often such a Online Citizen Sensor Community (CSC) has stated or implied goals related to workflows of organizational actors with defined roles and responsibilities. For example, a community of crisis response volunteers, for informing the prioritization of responses for resource needs (e.g., medical) to assist the managers of crisis response organizations. However, in CSC, there are challenges related to information overload for organizational actors, including finding reliable information providers and finding the actionable information from citizens. This threatens awareness and articulation of workflows to enable cooperation between citizens and organizational actors. CSCs supported by Web 2.0 social media platforms offer new opportunities and pose new challenges. This work addresses issues of ambiguity in interpreting unconstrained natural language (e.g., ‘wanna help’ appearing in both types of messages for asking and offering help during crises), sparsity of user and group behaviors (e.g., expression of specific intent), and diversity of user demographics (e.g., medical or technical professional) for interpreting user-generated data of citizen sensors. Interdisciplinary research involving social and computer sciences is essential to address these socio-technical issues in CSC, and allow better accessibility to user-generated data at higher level of information abstraction for organizational actors. This study presents a novel web information processing framework focused on actors and actions in cooperation, called Identify-Match-Engage (IME), which fuses top-down and bottom-up computing approaches to design a cooperative web information system between citizens and organizational actors. It includes a.) identification of action related seeking-offering intent behaviors from short, unstructured text documents using both declarative and statistical knowledge based classification model, b.) matching of intentions about seeking and offering, and c.) engagement models of users and groups in CSC to prioritize whom to engage, by modeling context with social theories using features of users, their generated content, and their dynamic network connections in the user interaction networks. The results show an improvement in modeling efficiency from the fusion of top-down knowledge-driven and bottom-up data-driven approaches than from conventional bottom-up approaches alone for modeling intent and engagement. Several applications of this work include use of the engagement interface tool during recent crises to enable efficient citizen engagement for spreading critical information of prioritized needs to ensure donation of only required supplies by the citizens. The engagement interface application also won the United Nations ICT agency ITU's Young Innovator 2014 award.Hemant Purohit PhD Defense: Mining Citizen Sensor Communities for Cooperation...
Hemant Purohit PhD Defense: Mining Citizen Sensor Communities for Cooperation...Artificial Intelligence Institute at UofSC
Literature-Based Discovery (LBD) refers to the process of uncovering hidden connections that are implicit in scientific literature. Numerous hypotheses have been generated from scientific literature, which influenced innovations in diagnosis, treatment, preventions and overall public health. However, much of the existing research on discovering hidden connections among concepts have used distributional statistics and graph-theoretic measures to capture implicit associations. Such metrics do not explicitly capture the semantics of hidden connections. ...
While effective in some situations, the practice of relying on domain expertise, structured background knowledge and heuristics to complement distributional and graph-theoretic approaches, has serious limitations. ..
This dissertation proposes an innovative context-driven, automatic subgraph creation method for finding hidden and complex associations among concepts, along multiple thematic dimensions. It outlines definitions for context and shared context, based on implicit and explicit (or formal) semantics, which compensate for deficiencies in statistical and graph-based metrics. It also eliminates the need for heuristics a priori. An evidence-based evaluation of the proposed framework showed that 8 out of 9 existing scientific discoveries could be recovered using this approach. Additionally, insights into the meaning of associations could be obtained using provenance provided by the system. In a statistical evaluation to determine the interestingness of the generated subgraphs, it was observed that an arbitrary association is mentioned in only approximately 4 articles in MEDLINE, on average. These results suggest that leveraging implicit and explicit context, as defined in this dissertation, is an advancement of the state-of-the-art in LBD research.
Ph.D. Committee: Drs. Amit Sheth (Advisor), TK Prasad, Michael Raymer,
Ramakanth Kavuluru (UKY), Thomas C. Rindflesch (NLM) and Varun Bhagwan (Yahoo! Labs)
Relevant Publications (more at: http://knoesis.wright.edu/students/delroy/)
D. Cameron, R. Kavuluru, T. C. Rindflesch, O. Bodenreider, A. P. Sheth, K. Thirunarayan. Leveraging Distributional Semantics for Domain Agnostic Literature-Based Discovery (under preparation)
D. Cameron, O. Bodenreider, H. Yalamanchili, T. Danh, S. Vallabhaneni, K. Thirunarayan, A. P. Sheth, T. C. Rindflesch. A Graph-based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications. Journal of Biomedical Informatics (JBI13), 46(2): 238–251, 2013
D. Cameron, R. Kavuluru, O. Bodenreider, P. N. Mendes, A. P. Sheth, K. Thirunarayan. Semantic Predications for Complex Information Needs in Biomedical Literature International Bioinformatics and Biomedical Conference (BIBM11), pp. 512–519, 2011 (acceptance rate=19.4%)
D. Cameron, P. N. Mendes, A. P. Sheth, V. Chan. Semantics-empowered Text Exploration for Knowledge Discovery. ACM Southeast Conference (ACMSE10), 14, 2010Delroy Cameron's Dissertation Defense: A Contenxt-Driven Subgraph Model for L...
Delroy Cameron's Dissertation Defense: A Contenxt-Driven Subgraph Model for L...Artificial Intelligence Institute at UofSC
This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analy- sis, and reasoning will be discussed. The tutorial will de- scribe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions re- lated to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, do- main knowledge integration and management, querying large- scale IoT data, and AI applications for automated knowledge extraction from real world data.
Related: Semantic Sensor Web: http://knoesis.org/projects/ssw
Physical-Cyber-Social Computing: http://wiki.knoesis.org/index.php/PCSData Processing and Semantics for Advanced Internet of Things (IoT) Applicati...
Data Processing and Semantics for Advanced Internet of Things (IoT) Applicati...Artificial Intelligence Institute at UofSC
Brief overview of the project. More at Project Web site:
http://wiki.knoesis.org/index.php/Context-Aware_Harassment_Detection_on_Social_MediaContext Aware Harassment Detection in Social Media [Overview]
Context Aware Harassment Detection in Social Media [Overview]Artificial Intelligence Institute at UofSC
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Dissertation Defense:
" Mining and Analyzing Subjective Experiences in User Generated Content "
By Lu Chen
Tuesday, April 9, 2016
Dissertation Committee: Dr. Amit Sheth, Advisor, Dr. T. K. Prasad, Dr. Keke Chen, Dr. Ingmar Weber, and Dr. Justin Martineau,
Pictures: https://www.facebook.com/Kno.e.sis/photos/?tab=album&album_id=1225911137443732
Video: https://youtu.be/tzLEUB-hggQ
Lu's Home page: http://knoesis.wright.edu/researchers/luchen/
ABSTRACT
Web 2.0 and social media enable people to create, share and discover information instantly anywhere, anytime. A great amount of this information is subjective information -- the information about people's subjective experiences, ranging from feelings of what is happening in our daily lives to opinions on a wide variety of topics. Subjective information is useful to individuals, businesses, and government agencies to support decision making in areas such as product purchase, marketing strategy, and policy making. However, much useful subjective information is buried in ever-growing user generated data on social media platforms, it is still difficult to extract high quality subjective information and make full use of it with current technologies.
Current subjectivity and sentiment analysis research has largely focused on classifying the text polarity -- whether the expressed opinion regarding a specific topic in a given text is positive, negative, or neutral. This narrow definition does not take into account the other types of subjective information such as emotion, intent, and preference, which may prevent their exploitation from reaching its full potential. This dissertation extends the definition and introduces a unified framework for mining and analyzing diverse types of subjective information. We have identified four components of a subjective experience: an individual who holds it, a target that elicits it (e.g., a movie, or an event), a set of expressions that describe it (e.g., "excellent", "exciting"), and a classification or assessment that characterize it (e.g., positive vs. negative). Accordingly, this dissertation makes contributions in developing novel and general techniques for the tasks of identifying and extracting these components.
We first explore the task of extracting sentiment expressions from social media posts. We propose an optimization-based approach that extracts a diverse set of sentiment-bearing expressions, including formal and slang words/phrases, for a given target from an unlabeled corpus. Instead of associating the overall sentiment with a given text, this method assesses the more fine-grained target-dependent polarity of each sentiment expression. Unlike pattern-based approaches which often fail to capture the diversity of sentiment expressions due to the informal nature of language usage and writing style in social media posts, the proposed approach is capable of identifying sentiment phraseMining and Analyzing Subjective Experiences in User-generated Content
Mining and Analyzing Subjective Experiences in User-generated ContentArtificial Intelligence Institute at UofSC
Understanding users’ latent intents behind search queries is essential for satisfying a user’s search needs. Search intent mining can help search engines to enhance its ranking of search results, enabling new search features like instant answers, personalization, search result diversification, and the recommendation of more relevant ads. Consequently, there has been increasing attention on studying how to effectively mine search intents by analyzing search engine query logs. While state-of-the-art techniques can identify the domain of the queries (e.g. sports, movies, health), identifying domain-specific intent is still an open problem. Among all the topics available on the Internet, health is one of the most important in terms of impact on the user and it is one of the most frequently searched areas. This dissertation presents a knowledge-driven approach for domain-specific search intent mining with a focus on health-related search queries.
First, we identified 14 consumer-oriented health search intent classes based on inputs from focus group studies and based on analyses of popular health websites, literature surveys, and an empirical study of search queries. We defined the problem of classifying millions of health search queries into zero or more intent classes as a multi-label classification problem. Popular machine learning approaches for multi-label classification tasks (namely, problem transformation and algorithm adaptation methods) were not feasible due to the limitation of label data creations and health domain constraints. Another challenge in solving the search intent identification problem was mapping terms used by laymen to medical terms. To address these challenges, we developed a semantics-driven, rule-based search intent mining approach leveraging rich background knowledge encoded in Unified Medical Language System (UMLS) and a crowd sourced encyclopedia (Wikipedia). The approach can identify search intent in a disease-agnostic manner and has been evaluated on three major diseases.
While users often turn to search engines to learn about health conditions, a surprising amount of health information is also shared and consumed via social media, such as public social platforms like Twitter. Although Twitter is an excellent information source, the identification of informative tweets from the deluge of tweets is the major challenge. We used a hybrid approach consisting of supervised machine learning, rule-based classifiers, and biomedical domain knowledge to facilitate the retrieval of relevant and reliable health information shared on Twitter in real time. Furthermore, we extended our search intent mining algorithm to classify health-related tweets into health categories. Finally, we performed a large-scale study to compare health search intents and features that contribute in the expression of search intent from 100+ million search queries from smarts devices (smartphones/tablets) and personal computers (desktops/laptops)Ashutosh Jadhav PhD Defense: Knowledge Driven Search Intent Mining
Ashutosh Jadhav PhD Defense: Knowledge Driven Search Intent MiningArtificial Intelligence Institute at UofSC
Video of the talk: https://www.youtube.com/watch?v=7k-u_TUew3o
Abstract: Social media has experienced immense growth in recent times. These platforms are becoming increasingly common for information seeking and consumption, and as part of its growing popularity, information overload pose a significant challenge to users. For instance, Twitter alone generates around 500 million tweets per day and it is impractical for users to have to parse through such an enormous stream to find information that are interesting to them. This situation necessitates efficient personalized filtering mechanisms for users to consume relevant, interesting information from social media.
Building a personalized filtering system involves understanding users interests and utilizing these interests to deliver relevant information to users. These tasks primarily include analyzing and processing social media text which is challenging due to its shortness in length, and the real-time nature of the medium. The challenges include: (1) Lack of semantic context: Social Media posts are on an average short in length, which provides limited semantic context to perform textual analysis. This is particularly detrimental for topic identification which is a necessary task for mining users interests; (2) Dynamically changing vocabulary: Most social media websites such as Twitter and Facebook generate posts that are of current (timely) interests to the users. Due to this real-time nature, information relevant to dynamic topics of interest evolve reflecting the changes in the real world. This in turn changes the vocabulary associated with these dynamic topics of interest making it harder to filter relevant information; (3) Scalability: The number of users on social media platforms are significantly large, which is difficult for centralized systems to scale to deliver relevant information to users. This dissertation is devoted to exploring semantic techniques and Semantic Web technologies to address the above mentioned challenges in building a personalized information filtering system for social media. Particularly, the necessary semantics (knowledge) is derived from crowd sourced knowledge bases such as Wikipedia to improve context for understanding short-text and dynamic topics on social media.
Personalized and Adaptive Semantic Information Filtering for Social Media - P...
Personalized and Adaptive Semantic Information Filtering for Social Media - P...Artificial Intelligence Institute at UofSC
There is a rapid intertwining of sensors and mobile devices into the fabric of our lives. This has resulted in unprecedented growth in the number of observations from the physical and social worlds reported in the cyber world. Sensing and computational components embedded in the physical world is termed as Cyber-Physical System (CPS). Current science of CPS is yet to effectively integrate citizen observations in CPS analysis. We demonstrate the role of citizen observations in CPS and propose a novel approach to perform a holistic analysis of machine and citizen sensor observations. Specifically, we demonstrate the complementary, corroborative, and timely aspects of citizen sensor observations compared to machine sensor observations in Physical-Cyber-Social (PCS) Systems.
Physical processes are inherently complex and embody uncertainties. They manifest as machine and citizen sensor observations in PCS Systems. We propose a generic framework to move from observations to decision-making and actions in PCS systems consisting of: (a) PCS event extraction, (b) PCS event understanding, and (c) PCS action recommendation. We demonstrate the role of Probabilistic Graphical Models (PGMs) as a unified framework to deal with uncertainty, complexity, and dynamism that help translate observations into actions. Data driven approaches alone are not guaranteed to be able to synthesize PGMs reflecting real-world dependencies accurately. To overcome this limitation, we propose to empower PGMs using the declarative domain knowledge. Specifically, we propose four techniques: (a) automatic creation of massive training data for Conditional Random Fields (CRFs) using domain knowledge of entities used in PCS event extraction, (b) Bayesian Network structure refinement using causal knowledge from Concept Net used in PCS event understanding, (c) knowledge-driven piecewise linear approximation of nonlinear time series dynamics using Linear Dynamical Systems (LDS) used in PCS event understanding, and the (d) transforming knowledge of goals and actions into a Markov Decision Process (MDP) model used in PCS action recommendation.
We evaluate the benefits of the proposed techniques on real-world applications involving traffic analytics and Internet of Things (IoT).Knowledge-empowered Probabilistic Graphical Models for Physical-Cyber-Social ...
Knowledge-empowered Probabilistic Graphical Models for Physical-Cyber-Social ...Artificial Intelligence Institute at UofSC
PhD defense held at Kno.e.sis Center, Wright State University, December 03, 2013.Pablo Mendes' Defense: Adaptive Semantic Annotation of Entity and Concept Men...
Pablo Mendes' Defense: Adaptive Semantic Annotation of Entity and Concept Men...Artificial Intelligence Institute at UofSC
Social media provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens share information, express opinions, and engage in discussions. Often such a Online Citizen Sensor Community (CSC) has stated or implied goals related to workflows of organizational actors with defined roles and responsibilities. For example, a community of crisis response volunteers, for informing the prioritization of responses for resource needs (e.g., medical) to assist the managers of crisis response organizations. However, in CSC, there are challenges related to information overload for organizational actors, including finding reliable information providers and finding the actionable information from citizens. This threatens awareness and articulation of workflows to enable cooperation between citizens and organizational actors. CSCs supported by Web 2.0 social media platforms offer new opportunities and pose new challenges. This work addresses issues of ambiguity in interpreting unconstrained natural language (e.g., ‘wanna help’ appearing in both types of messages for asking and offering help during crises), sparsity of user and group behaviors (e.g., expression of specific intent), and diversity of user demographics (e.g., medical or technical professional) for interpreting user-generated data of citizen sensors. Interdisciplinary research involving social and computer sciences is essential to address these socio-technical issues in CSC, and allow better accessibility to user-generated data at higher level of information abstraction for organizational actors. This study presents a novel web information processing framework focused on actors and actions in cooperation, called Identify-Match-Engage (IME), which fuses top-down and bottom-up computing approaches to design a cooperative web information system between citizens and organizational actors. It includes a.) identification of action related seeking-offering intent behaviors from short, unstructured text documents using both declarative and statistical knowledge based classification model, b.) matching of intentions about seeking and offering, and c.) engagement models of users and groups in CSC to prioritize whom to engage, by modeling context with social theories using features of users, their generated content, and their dynamic network connections in the user interaction networks. The results show an improvement in modeling efficiency from the fusion of top-down knowledge-driven and bottom-up data-driven approaches than from conventional bottom-up approaches alone for modeling intent and engagement. Several applications of this work include use of the engagement interface tool during recent crises to enable efficient citizen engagement for spreading critical information of prioritized needs to ensure donation of only required supplies by the citizens. The engagement interface application also won the United Nations ICT agency ITU's Young Innovator 2014 award.Hemant Purohit PhD Defense: Mining Citizen Sensor Communities for Cooperation...
Hemant Purohit PhD Defense: Mining Citizen Sensor Communities for Cooperation...Artificial Intelligence Institute at UofSC
Literature-Based Discovery (LBD) refers to the process of uncovering hidden connections that are implicit in scientific literature. Numerous hypotheses have been generated from scientific literature, which influenced innovations in diagnosis, treatment, preventions and overall public health. However, much of the existing research on discovering hidden connections among concepts have used distributional statistics and graph-theoretic measures to capture implicit associations. Such metrics do not explicitly capture the semantics of hidden connections. ...
While effective in some situations, the practice of relying on domain expertise, structured background knowledge and heuristics to complement distributional and graph-theoretic approaches, has serious limitations. ..
This dissertation proposes an innovative context-driven, automatic subgraph creation method for finding hidden and complex associations among concepts, along multiple thematic dimensions. It outlines definitions for context and shared context, based on implicit and explicit (or formal) semantics, which compensate for deficiencies in statistical and graph-based metrics. It also eliminates the need for heuristics a priori. An evidence-based evaluation of the proposed framework showed that 8 out of 9 existing scientific discoveries could be recovered using this approach. Additionally, insights into the meaning of associations could be obtained using provenance provided by the system. In a statistical evaluation to determine the interestingness of the generated subgraphs, it was observed that an arbitrary association is mentioned in only approximately 4 articles in MEDLINE, on average. These results suggest that leveraging implicit and explicit context, as defined in this dissertation, is an advancement of the state-of-the-art in LBD research.
Ph.D. Committee: Drs. Amit Sheth (Advisor), TK Prasad, Michael Raymer,
Ramakanth Kavuluru (UKY), Thomas C. Rindflesch (NLM) and Varun Bhagwan (Yahoo! Labs)
Relevant Publications (more at: http://knoesis.wright.edu/students/delroy/)
D. Cameron, R. Kavuluru, T. C. Rindflesch, O. Bodenreider, A. P. Sheth, K. Thirunarayan. Leveraging Distributional Semantics for Domain Agnostic Literature-Based Discovery (under preparation)
D. Cameron, O. Bodenreider, H. Yalamanchili, T. Danh, S. Vallabhaneni, K. Thirunarayan, A. P. Sheth, T. C. Rindflesch. A Graph-based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications. Journal of Biomedical Informatics (JBI13), 46(2): 238–251, 2013
D. Cameron, R. Kavuluru, O. Bodenreider, P. N. Mendes, A. P. Sheth, K. Thirunarayan. Semantic Predications for Complex Information Needs in Biomedical Literature International Bioinformatics and Biomedical Conference (BIBM11), pp. 512–519, 2011 (acceptance rate=19.4%)
D. Cameron, P. N. Mendes, A. P. Sheth, V. Chan. Semantics-empowered Text Exploration for Knowledge Discovery. ACM Southeast Conference (ACMSE10), 14, 2010Delroy Cameron's Dissertation Defense: A Contenxt-Driven Subgraph Model for L...
Delroy Cameron's Dissertation Defense: A Contenxt-Driven Subgraph Model for L...Artificial Intelligence Institute at UofSC
This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analy- sis, and reasoning will be discussed. The tutorial will de- scribe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions re- lated to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, do- main knowledge integration and management, querying large- scale IoT data, and AI applications for automated knowledge extraction from real world data.
Related: Semantic Sensor Web: http://knoesis.org/projects/ssw
Physical-Cyber-Social Computing: http://wiki.knoesis.org/index.php/PCSData Processing and Semantics for Advanced Internet of Things (IoT) Applicati...
Data Processing and Semantics for Advanced Internet of Things (IoT) Applicati...Artificial Intelligence Institute at UofSC
Brief overview of the project. More at Project Web site:
http://wiki.knoesis.org/index.php/Context-Aware_Harassment_Detection_on_Social_MediaContext Aware Harassment Detection in Social Media [Overview]
Context Aware Harassment Detection in Social Media [Overview]Artificial Intelligence Institute at UofSC
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