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Template-based information access, in which templates are constructed for keywords, is a recent development of linked data information retrieval. However, most such approaches suffer from ineffective template management. Because linked data has a structured data representation, we assume the data’s inside statistics can effectively influence template management. In this work, we use this influence for template
creation, template ranking, and scaling. Our proposal can effectively be used for automatic linked data information retrieval and can be incorporated with other techniques such as ontology inclusion and sophisticated matching to further improve performance.
An automated template selection framework for keyword query over linked data
An automated template selection framework for keyword query over linked dataNational Inistitute of Informatics (NII), Tokyo, Japann
Temporal features, such as date and time or time of an event, always expose some concise semantics over any
kind of information retrieval, and so over linked data information retrieval. On one hand, we see, contemporary
research tries to adapt linked data information retrieval with easy and familiar keyword-based retrieval to hide
the complexity of data’s underlying technologies. On the other hand, we find, linked data information retrieval
perspective, most of them overlook the power of temporal feature inclusion. Considering the both, this study,
investigates the importance of temporal feature inclusion over linked data information retrieval. We propose a
keyword-based linked data information retrieval framework which can incorporate temporal features and can give
more concise results. Our investigation justify the significance of temporal feature inclusion over linked data
retrieval.Inclusion of Temporal Semantics over Keyword-based Linked Data Retrieval
Inclusion of Temporal Semantics over Keyword-based Linked Data RetrievalNational Inistitute of Informatics (NII), Tokyo, Japann
Recommended
Template-based information access, in which templates are constructed for keywords, is a recent development of linked data information retrieval. However, most such approaches suffer from ineffective template management. Because linked data has a structured data representation, we assume the data’s inside statistics can effectively influence template management. In this work, we use this influence for template
creation, template ranking, and scaling. Our proposal can effectively be used for automatic linked data information retrieval and can be incorporated with other techniques such as ontology inclusion and sophisticated matching to further improve performance.
An automated template selection framework for keyword query over linked data
An automated template selection framework for keyword query over linked dataNational Inistitute of Informatics (NII), Tokyo, Japann
Temporal features, such as date and time or time of an event, always expose some concise semantics over any
kind of information retrieval, and so over linked data information retrieval. On one hand, we see, contemporary
research tries to adapt linked data information retrieval with easy and familiar keyword-based retrieval to hide
the complexity of data’s underlying technologies. On the other hand, we find, linked data information retrieval
perspective, most of them overlook the power of temporal feature inclusion. Considering the both, this study,
investigates the importance of temporal feature inclusion over linked data information retrieval. We propose a
keyword-based linked data information retrieval framework which can incorporate temporal features and can give
more concise results. Our investigation justify the significance of temporal feature inclusion over linked data
retrieval.Inclusion of Temporal Semantics over Keyword-based Linked Data Retrieval
Inclusion of Temporal Semantics over Keyword-based Linked Data RetrievalNational Inistitute of Informatics (NII), Tokyo, Japann
Temporal features, such as date and time or time of an event, employ concise semantics for any kind of information retrieval, and therefore for linked data information retrieval. However, we have found that
most linked data information retrieval techniques pay little attention on the power of temporal feature inclusion. We propose a keyword-based linked data information retrieval framework, called TLDRet, that can incorporate temporal features and give more concise results. Preliminary
evaluation of our system shows promising performance.
TLDRet: A Temporal Semantic Facilitated Linked Data Retrieval Framework
TLDRet: A Temporal Semantic Facilitated Linked Data Retrieval FrameworkNational Inistitute of Informatics (NII), Tokyo, Japann
Information access over linked data requires to determine
subgraph(s), in linked data's underlying graph, that correspond to the required information need. Usually, an information access framework is able to retrieve richer information by checking of a large number of possible subgraphs. However, on the ecking of a large number of possible subgraphs increases information access complexity. This makes information access frameworks less e ective. A large number of contemporary linked data information access frameworks reduce the complexity by introducing di erent heuristics but they su er on retrieving richer information. Or, some frameworks do not care about the complexity. However, a practically usable framework should retrieve richer information with lower complexity. In linked data information access, we hypothesize that pre-processed data statistics of linked data can be used to eciently check a large number of possible subgraphs. This will help to retrieve comparatively richer information with lower data access complexity. Preliminary evaluation of our proposed hypothesis shows promising performance.inteSearch: An Intelligent Linked Data Information Access Framework
inteSearch: An Intelligent Linked Data Information Access FrameworkNational Inistitute of Informatics (NII), Tokyo, Japann
Keyword-based linked data information retrieval is an easy choice for general purpose users, but implementation of such approach is a challenge because mere keyword does not hold semantics. Some studies have incorporated templates in an e ort to bridge this gap, but most such pproaches have proven ine ective because of inecient template management. Because linked data can be resented in a structured format, we can assume that the data's internal statistics can be used to e ectively in
uence template management. In this work, we explore
the use of this in uence for template creation, ranking, and scaling. Then, we demonstrate how our proposal for automatic linked data information retrieval can be used alongside familiar keyword-based information retrieval methods, and can also be incorporated alongside other techniques, such as ontology inclusion and sophisticated matching, to achieve increased levels of performance. BoTLRet: A Template-based Linked Data Information Retrieval
BoTLRet: A Template-based Linked Data Information RetrievalNational Inistitute of Informatics (NII), Tokyo, Japann
Content Words (CWs) are important segments of the text. In text mining, we utilize them for various purposes such as topic identification, document summarization, question answering etc. Usually, the identification of CWs requires various language dependent tools. However, such tools are not available for many languages and developing of them for all languages is costly. On the other hand, because of recent growth of text contents in various languages, language independent text mining carries great potentiality. To mine text automatically, the language tool independent CWs finding is a requirement. In this research, we devise a framework that identifies text segments into CWs in a language independent way. We identify some structural features that relate text segments into CWs. We devise the features over a large text corpus and apply machine learning-based classification that classifies the segments into CWs. The proposed framework only uses large text corpus and some training examples, apart from these, it does not require any language specific tool. We conduct experiments of our framework for three different languages: English, Vietnamese and Indonesian, and found that it works with more than 83% accuracy.LiCord: Language Independent Content Word Finder
LiCord: Language Independent Content Word FinderNational Inistitute of Informatics (NII), Tokyo, Japann
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Temporal features, such as date and time or time of an event, employ concise semantics for any kind of information retrieval, and therefore for linked data information retrieval. However, we have found that
most linked data information retrieval techniques pay little attention on the power of temporal feature inclusion. We propose a keyword-based linked data information retrieval framework, called TLDRet, that can incorporate temporal features and give more concise results. Preliminary
evaluation of our system shows promising performance.
TLDRet: A Temporal Semantic Facilitated Linked Data Retrieval Framework
TLDRet: A Temporal Semantic Facilitated Linked Data Retrieval FrameworkNational Inistitute of Informatics (NII), Tokyo, Japann
Information access over linked data requires to determine
subgraph(s), in linked data's underlying graph, that correspond to the required information need. Usually, an information access framework is able to retrieve richer information by checking of a large number of possible subgraphs. However, on the ecking of a large number of possible subgraphs increases information access complexity. This makes information access frameworks less e ective. A large number of contemporary linked data information access frameworks reduce the complexity by introducing di erent heuristics but they su er on retrieving richer information. Or, some frameworks do not care about the complexity. However, a practically usable framework should retrieve richer information with lower complexity. In linked data information access, we hypothesize that pre-processed data statistics of linked data can be used to eciently check a large number of possible subgraphs. This will help to retrieve comparatively richer information with lower data access complexity. Preliminary evaluation of our proposed hypothesis shows promising performance.inteSearch: An Intelligent Linked Data Information Access Framework
inteSearch: An Intelligent Linked Data Information Access FrameworkNational Inistitute of Informatics (NII), Tokyo, Japann
Keyword-based linked data information retrieval is an easy choice for general purpose users, but implementation of such approach is a challenge because mere keyword does not hold semantics. Some studies have incorporated templates in an e ort to bridge this gap, but most such pproaches have proven ine ective because of inecient template management. Because linked data can be resented in a structured format, we can assume that the data's internal statistics can be used to e ectively in
uence template management. In this work, we explore
the use of this in uence for template creation, ranking, and scaling. Then, we demonstrate how our proposal for automatic linked data information retrieval can be used alongside familiar keyword-based information retrieval methods, and can also be incorporated alongside other techniques, such as ontology inclusion and sophisticated matching, to achieve increased levels of performance. BoTLRet: A Template-based Linked Data Information Retrieval
BoTLRet: A Template-based Linked Data Information RetrievalNational Inistitute of Informatics (NII), Tokyo, Japann
Content Words (CWs) are important segments of the text. In text mining, we utilize them for various purposes such as topic identification, document summarization, question answering etc. Usually, the identification of CWs requires various language dependent tools. However, such tools are not available for many languages and developing of them for all languages is costly. On the other hand, because of recent growth of text contents in various languages, language independent text mining carries great potentiality. To mine text automatically, the language tool independent CWs finding is a requirement. In this research, we devise a framework that identifies text segments into CWs in a language independent way. We identify some structural features that relate text segments into CWs. We devise the features over a large text corpus and apply machine learning-based classification that classifies the segments into CWs. The proposed framework only uses large text corpus and some training examples, apart from these, it does not require any language specific tool. We conduct experiments of our framework for three different languages: English, Vietnamese and Indonesian, and found that it works with more than 83% accuracy.LiCord: Language Independent Content Word Finder
LiCord: Language Independent Content Word FinderNational Inistitute of Informatics (NII), Tokyo, Japann
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