Large-scale ontologies are attracting attention in various fields such as information retrieval, data integration and question answering. Because it takes many costs for human experts to build and maintain ontologies, much attention has been come to the work on ontology learning from semi-structured information resources, such as Wikipedia. YAGO and DBpedia are famous examples of such researches aimed at (semi) automatic construction from Wikipedia. However, in Wikipedia, there are still many information that YAGO and DBpedia can not extract, such as texual definition, headings, list articles and list structures. Also, YAGO mainly extracts class hierarchies and class instance relations and does not extract property types such as symmetric, transitive, functional and inverse functional. DBpedia has property domains (rdfs:domain), property ranges (rdfs:range), and some property types. However, they are described manually, which causes problems in terms of maintenance and update. In this paper, we present construction methods of ontology which complement YAGO and DBpedia. We use Wikipedia categories, Infobox, Infobox templates, list articles, texual definition, headings and list structures as Wikipedia resources. The ontologies include Is-a relations (rdfs:subClassOf), class instance relations (rdf:type) and triple. We compare them with overseas Wikipedia ontologies such as YAGO and DBpedia. Also we show that we can extract relations which YAGO and DBpedia can not extract.
|Number of pages||14|
|Journal||Transactions of the Japanese Society for Artificial Intelligence|
|Publication status||Published - 2020|
- Ontology learning
ASJC Scopus subject areas
- Artificial Intelligence