Full metadata
Title
Semantic Information Extraction From Natural Language Using a Learning and Rule-Based Approach
Description
Open Information Extraction (OIE) is a subset of Natural Language Processing (NLP) that constitutes the processing of natural language into structured and machine-readable data. This thesis uses data in Resource Description Framework (RDF) triple format that comprises of a subject, predicate, and object. The extraction of RDF triples from natural language is an essential step towards importing data into web ontologies as part of the linked open data cloud on the Semantic web. There have been a number of related techniques for extraction of triples from plain natural language text including but not limited to ClausIE, OLLIE, Reverb, and DeepEx. This proposed study aims to reduce the dependency on conventional machine learning models since they require training datasets, and the models are not easily customizable or explainable. By leveraging a context-free grammar (CFG) based model, this thesis aims to address some of these issues while minimizing the trade-offs on performance and accuracy. Furthermore, a deep-dive is conducted to analyze the strengths and limitations of the proposed approach.
Date Created
2023
Contributors
- Singh, Varun (Author)
- Bansal, Srividya (Thesis advisor)
- Bansal, Ajay (Committee member)
- Mehlhase, Alexandra (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
74 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.190879
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: M.S., Arizona State University, 2023
Field of study: Software Engineering
System Created
- 2023-12-14 01:41:28
System Modified
- 2023-12-14 01:41:34
- 10 months 4 weeks ago
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