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Title: PurposeNet
Researcher: P Kiran Mayee
Guide(s): Rajeev Sangal
University: International Institute of Information Technology, Hyderabad
Completed Date: 14-07-2014
Abstract: Several attempts have been made in the past for the creation of a comprehensive and structured ontology to enable better and efficient semantic analysis for web related common sense reasoning tasks such as Question Answering, Summarization, Word Sense Disambiguation, Analogy making, and so on. But, these approaches lack in a strong design paradigm for representation of facts about objects in general and artifacts in specific. Also, whereas the human cognition is purpose based, none of the KBs (knowledgebases) built so far have taken the purpose perspective into account. To address these lacunae in existing KBs, PurposeNet a comprehensive KB that addresses all possible information requirements about artifacts with purpose as the underlying principle for the design has been engineered. The features have been further designated as either a set of descriptions of the various morphological and anatomical characteristics of the artifact, or a set of actions that the artifact participates in during the course of its manufacture, use, and final disposal. Each of these actions has been described using a common generic frame, which is the special feature of our KB. The design is compact, with several directly and indirectly recursive relations among its constituents, which is the basis for networking in the ontology. The design has been implemented on the transport domain using Web Ontology Language. A set of 10,00,000 assertions have been created using the ontology. The ontology design has been validated and the implemented ontology has been evaluated. It addresses the issue of detection and extraction methodologies for the relations prescribed in the artifact design. One relation of the artifact namely, its purpose has been taken as an example case for which various machine learning and pattern-matching methodologies described have been applied for detection with encouraging results. Relation extraction is also implemented using two proven methods typed dependency parse and surface pattern extraction.
Pagination: xviii,250
Appears in Departments:Computer Science and Engineering

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04_acknowledgment.pdf473.38 kBAdobe PDFView/Open
05_abstract.pdf526.65 kBAdobe PDFView/Open
06_contents.pdf570.31 kBAdobe PDFView/Open
07_list of figures and tables.pdf614.24 kBAdobe PDFView/Open
08_chapter 1.pdf671.34 kBAdobe PDFView/Open
09_chapter 2.pdf867.99 kBAdobe PDFView/Open
10_chapter 3.pdf1.26 MBAdobe PDFView/Open
11_chapter 4.pdf1.09 MBAdobe PDFView/Open
12_chapter 5.pdf1.28 MBAdobe PDFView/Open
13_chapter 6.pdf1.15 MBAdobe PDFView/Open
14_chapter 7.pdf611.57 kBAdobe PDFView/Open
15_bibliography.pdf580.62 kBAdobe PDFView/Open
16_publications.pdf491.98 kBAdobe PDFView/Open
17_appendix a.pdf481 kBAdobe PDFView/Open
18_appendix b.pdf606.34 kBAdobe PDFView/Open
19_appendix c.pdf936.42 kBAdobe PDFView/Open
20_appendix d.pdf608.5 kBAdobe PDFView/Open

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