TY - GEN
T1 - Semantic Information Extraction from Natural Language Using a Learning and Rule-Based Approach
AU - Singh, Varun
AU - Bansal, Srividya K.
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - 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 study uses data in Resource Description Framework (RDF) triple format that comprises a subject, predicate, and object. The extraction of RDF triples from natural language is an essential step toward 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 paper 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.
AB - 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 study uses data in Resource Description Framework (RDF) triple format that comprises a subject, predicate, and object. The extraction of RDF triples from natural language is an essential step toward 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 paper 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.
KW - Entity extraction
KW - Information extraction
KW - Natural language processing
KW - RDF triples
UR - https://www.scopus.com/pages/publications/105015990441
UR - https://www.scopus.com/pages/publications/105015990441#tab=citedBy
U2 - 10.1007/978-981-96-4273-1_19
DO - 10.1007/978-981-96-4273-1_19
M3 - Conference contribution
SN - 9789819642724
T3 - Lecture Notes in Networks and Systems
SP - 247
EP - 261
BT - Intelligent Systems and Security - Proceedings of ICISS 2024
A2 - Giri, Debasis
A2 - Ho, Anthony T. S.
A2 - Ochiai, Hideki
A2 - Mandal, Mrinal
A2 - Sinha, Koushik
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st International Conference on Intelligent Systems and Security, ICISS 2024
Y2 - 20 December 2024 through 22 December 2024
ER -