Rivindu Perera

A Multi-Strategy Approach for Lexicalizing Linked Open Data

This paper aims at exploiting Linked Data for generating natural text, often referred to as lexicalization. We propose a framework that can generate patterns which can be used to lexicalize Linked Data triples. Linked Data is structured knowledge organized in the form of triples consisting of a subject, a predicate and an object. We use DBpedia as the Linked Data source which is not only free but is currently the fastest growing data source organized as Linked Data. The proposed framework utilizes the Open Information Extraction (OpenIE) to extract relations from natural text and these relations are then aligned with triples to identify lexicalization patterns. We also exploit lexical semantic resources which encode knowledge on lexical, semantic and syntactic information about entities. Our framework uses VerbNet and WordNet as semantic resources. The extracted patterns are ranked and categorized based on the DBpedia ontology class hierarchy. The pattern collection is then sorted based on the score assigned and stored in an index embedded database for use in the framework as well as for future lexical resource. The framework was evaluated for syntactic accuracy and validity by measuring the Mean Reciprocal Rank (MRR) of the first correct pattern. The results indicated that framework can achieve 70.36% accuracy and a MRR value of 0.72 for five DBpedia ontology classes generating 101 accurate lexicalization patterns.