A Natural Language Interface to Ontology-Based Knowledge Bases

The aim of the Semantic Web is to improve the access, management, and retrieval of information on the Web-based. On this understanding, ontologies are considered a technology that supports all aforementioned tasks. However, current approaches for information retrieval on ontology-based knowledge bases are intended to be used by experienced users. To address this gap, Natural Language Processing (NLP) is deemed a very intuitive approach from a non-experienced user‘s perspective, because the formality of a knowledge base is hidden, as well as the executable query language. In this work, we present ONLI, a natural language interface for DBpedia, a community effort to structure Wikipedia’s content based on an ontological approach. ONLI combines NLP techniques in order to analyze user’s question and populate an ontological model, which is responsible for describing question’s context. From this model, ONLI requests the answer through a set of heuristic SPARQL-based query patterns. Finally, we describe the current version of the ONLI system, as well as an evaluation to assess its effectiveness in finding the correct answer.