Learning relation axioms from text: An automatic Web-based approach

Even though expressive ontology representation languages like OWL have been proposed in the last years, most available ontologies only focus on taxonomical and, in a few cases, non-taxonomical knowledge. A way to add expressivity to an ontology is to include axioms that describe the basic logical properties of the modeled relationships. However, due to the manual knowledge acquisition bottleneck, axioms are usually missing in handcrafted ontologies. Moreover, automatic or semi-automatic ontology learning approaches aimed to acquire axioms from textual resources to enrich ontologies are scarce. This paper presents a novel methodology to learn axioms associated to non-taxonomic relationships modeled in an ontology in an automatic and unsupervised way, using the Web as corpus of textual resources. The proposed method is based on the use of specially tailored linguistic patterns, the exploitation of Web search engines as massive information retrieval tools, the application of shallow natural language processing and the assessment of semantic relatedness by means of Web-scale co-occurrence statistics. The paper describes the learning algorithm and presents promising results on the assessment of the following axioms: functional, inverse, symmetrical, transitive, reflexive and inverse functional properties.