Mining information from sentences through Semantic Web data and Information Extraction tasks

• Published in: Journal of information science Vol. 48; no. 1; pp. 3 - 20
• Main Authors: Martinez-Rodriguez, Jose L., Lopez-Arevalo, Ivan, Rios-Alvarado, Ana B.
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.02.2022; Bowker-Saur Ltd
• Subjects: Information retrieval; Knowledge bases (artificial intelligence); Natural language (computers); Representations; Semantic web; Semantics; Sentences; RDF representation; Semantic Web; Information Extraction; information representation; linked data
• DOI: 10.1177/0165551520934387
• ISSN: 0165-5515

The Semantic Web provides guidelines for the representation of information about real-world objects (entities) and their relations (properties). This is helpful for the dissemination and consumption of information by people and applications. However, the information is mainly contained within natural language sentences, which do not have a structure or linguistic descriptions ready to be directly processed by computers. Thus, the challenge is to identify and extract the elements of information that can be represented. Hence, this article presents a strategy to extract information from sentences and its representation with Semantic Web standards. Our strategy involves Information Extraction tasks and a hybrid semantic similarity measure to get entities and relations that are later associated with individuals and properties from a Knowledge Base to create RDF triples (Subject–Predicate–Object structures). The experiments demonstrate the feasibility of our method and that it outperforms the accuracy provided by a pattern-based method from the literature.