Ontology Enrichment by Extracting Hidden Assertional Knowledge from Text
This work addresses ontology enrichment for knowledge representation systems, but it appears incremental as it builds on prior works and focuses on specific informational domains.
The paper tackles the problem of extracting hidden assertional knowledge from text to enrich ontologies, using large RDF repositories and inductive reasoning, and demonstrates its soundness through a case study.
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.