Ontology Enrichment by Extracting Hidden Assertional Knowledge from Text

TL;DR

This paper proposes a novel method for enriching ontologies by extracting hidden assertional knowledge from text using RDF repositories and inductive reasoning, with a case study demonstrating its effectiveness.

Contribution

It introduces a new approach combining RDF data and inductive reasoning for ontology enrichment from unstructured text, addressing limitations of current Linked Data sources.

Findings

Successful extraction of non-taxonomic relations from text.

Enhanced ontology enrichment through web mining techniques.

Potential for improved relation extraction from unstructured data.

Abstract

In this position paper we present a new approach for discovering some special classes of assertional knowledge in the text by using large RDF repositories, resulting in the extraction of new non-taxonomic ontological relations. Also we use inductive reasoning beside our approach to make it outperform. Then, we prepare a case study by applying our approach on sample data and illustrate the soundness of our proposed approach. Moreover in our point of view current LOD cloud is not a suitable base for our proposal in all informational domains. Therefore we figure out some directions based on prior works to enrich datasets of Linked Data by using web mining. The result of such enrichment can be reused for further relation extraction and ontology enrichment from unstructured free text documents.