Mining defining contexts to help structuring differential ontologies

TLDR

Knowledge‑rich defining contexts can aid ontology modellers in constructing ontologies. The study presents a corpus‑based experiment for constructing differential ontologies organized by semantic similarity and differential features. The authors use lexico‑syntactic pattern mining to locate defining contexts, extract terms and relations, detect co‑hyponyms, and evaluate the approach on French corpora about childhood and dietetics. Extraction achieved 50 % precision and ~40 % recall for definitions, 48 % precision for semantic relations, and 23.5 % precision for co‑hyponyms, highlighting room for improvement.

Abstract

In this paper, we present an experiment dealing with corpus-based construction of “differential ontologies”, which are organised according to semantic similarity and differential features. We argue that knowledge-rich defining contexts can be useful to help an ontology modeller in his task. We present a method, based on lexico-syntactic patterns, to spot such contexts in a corpus, then identify the terms they relate (definiendum and genus or “characteristics”) and the semantic relation that links them. We also show how potential co-hyponyms can be detected on the basis of shared words in their definiens. We evaluate the extracted defining sentences, semantic relations and co-hyponyms on a test corpus focusing on childhood and on an evaluation corpus about dietetics (both corpora are French). Definition extraction obtains 50% precision and recall of approximately 40%. Semantic relation identification reaches an average of 48% precision, and co-hyponyms 23.5%. We discuss the results of these experiments and conclude on perspectives for future work.