A domain-independent process for automatic ontology population from text

Highlights

• We systematize the problem of Automatic Ontology Population.

• We propose a domain-independent process for Automatic Ontology Population.

• Our process overcomes the limitation of others of dependence of a domain.

• We conduct four experiments using a legal and a tourism corpora.

• Good effectiveness against its peers and adaptability are its main advantages.

Abstract

Ontology Population looks for instantiating the constituent elements of an ontology, like properties and non-taxonomic relationships. Manual population by domain experts and knowledge engineers is an expensive and time consuming task. Fast ontology population is critical for the success of knowledge-based applications. Thus, automatic or semi-automatic approaches are needed. This work proposes a generic process approaching the Automatic Ontology Population problem by specifying its phases and the techniques used to perform the activities on each phase. The main contribution of the work here described is a domain-independent process for the automatic population of ontologies from text that applies natural language processing and information extraction techniques to acquire and classify ontology instances. This is a new approach for automatic ontology population that uses an ontology to automatically generate rules to extract instances from text and classify them in ontology classes. These rules can be generated from ontologies of any domain, making the proposed process domain-independent and therefore, allowing the instantiation of ontologies quickly and at a low cost. Four experiments using a legal and a tourism corpora were conducted in order to evaluate the proposed process. Results indicate that this approach can extract and classify instances with high effectiveness with the additional advantage of domain independence. Some techniques representing the state of the art of this field are also described along with the solutions they adopt for each phase of the Automatic Ontology Population process with their advantages and limitations.