A HMM-based Approach to Question Answering against Linked Data

Abstract. In this paper, we present a QA system enabling NL questions against Linked Data, designed and adopted by the Tor Vergata University AI group in the QALD-3 evaluation. The system integrates lexical semantic modeling and statistical inference within a complex architecture that decomposes the NL inter-pretation task into a cascade of three different stages: (1) The selection of key ontological information from the question (i.e. predicate, arguments and proper-ties), (2) the location of such salient information in the ontology through the joint disambiguation of the different candidates and (3) the compilation of the final SPARQL query. This architecture characterizes a novel approach for the task and exploits a graphical model (i.e. an Hidden Markov Model) to select the proper ontological triples according to the graph nature of RDF. In particular, for each query an HMM model is produced whose Viterbi solution is the comprehensive joint disambiguation across the sentence elements. The combination of these ap-proaches achieved interesting results in the QALD competition. The RTV is in fact within the group of participants performing slightly below the best system, but with smaller requirements and on significantly poorer input information.