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Adaptive Semantic Annotation of Entity and Concept Mentions in Text

Mendes, Pablo N.
http://rave.ohiolink.edu/etdc/view?acc_num=wright1401665504

Abstract Details

2014, Doctor of Philosophy (PhD), Wright State University, Computer Science and Engineering PhD.
The recent years have seen an increase in interest for knowledge repositories that are useful across applications, in contrast to the creation of ad hoc or application-specific databases. These knowledge repositories figure as a central provider of unambiguous identifiers and semantic relationships between entities. As such, these shared entity descriptions serve as a common vocabulary to exchange and organize information in different formats and for different purposes. Therefore, there has been remarkable interest in systems that are able to automatically tag textual documents with identifiers from shared knowledge repositories so that the content in those documents is described in a vocabulary that is unambiguously understood across applications. Tagging textual documents according to these knowledge bases is a challenging task. It involves recognizing the entities and concepts that have been mentioned in a particular passage and attempting to resolve eventual ambiguity of language in order to choose one of many possible meanings for a phrase. There has been substantial work on recognizing and disambiguating entities for specialized applications, or constrained to limited entity types and particular types of text. In the context of shared knowledge bases, since each application has potentially very different needs, systems must have unprecedented breadth and flexibility to ensure their usefulness across applications. Documents may exhibit different language and discourse characteristics, discuss very diverse topics, or require the focus on parts of the knowledge repository that are inherently harder to disambiguate. In practice, for developers looking for a system to support their use case, is often unclear if an existing solution is applicable, leading those developers to trial-and-error and ad hoc usage of multiple systems in an attempt to achieve their objective. In this dissertation, I propose a conceptual model that unifies related techniques in this space under a common multi-dimensional framework that enables the elucidation of strengths and limitations of each technique, supporting developers in their search for a suitable tool for their needs. Moreover, the model serves as the basis for the development of flexible systems that have the ability of supporting document tagging for different use cases. I describe such an implementation, DBpedia Spotlight, along with extensions that we performed to the knowledge base DBpedia to support this implementation. I report evaluations of this tool on several well known data sets, and demonstrate applications to diverse use cases for further validation.
Amit P. Sheth, Ph.D. (Advisor)
Krishnaprasad Thirunarayan, Ph.D. (Committee Member)
Shajoun Wang, Ph.D. (Committee Member)
Sören Auer, Ph.D. (Committee Member)
130 p.
Computer Science
semantic annotation; semantic tagging; named entity recognition; name resolution; entity disambiguation; entity linking; keyphrase extraction; word sense disambiguation; entity classification; entity extraction; adaptive; flexible

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Citations

  • Mendes, P. N. (2014). Adaptive Semantic Annotation of Entity and Concept Mentions in Text [Doctoral dissertation, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1401665504

    APA Style (7th edition)

  • Mendes, Pablo. Adaptive Semantic Annotation of Entity and Concept Mentions in Text. 2014. Wright State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1401665504.

    MLA Style (8th edition)

  • Mendes, Pablo. "Adaptive Semantic Annotation of Entity and Concept Mentions in Text." Doctoral dissertation, Wright State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=wright1401665504

    Chicago Manual of Style (17th edition)

wright1401665504
2,141
© 2013, some rights reserved.Creative Commons License Adaptive Semantic Annotation of Entity and Concept Mentions in Text by Pablo N. Mendes is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Wright State University and OhioLINK.