An Italian Question Answering System Based on Grammars Automatically Generated from Ontology Lexica
Authors: Gennaro Nolano, Mohammad Fazleh Elahi, Maria Pia di Buono, Basil Ell, Philipp Cimiano (Università di Napoli L'Orientale, Bielefeld University, University of Oslo, 2021)
Topic: Lexical and Semantic Resources and Analysis

Abstract
The paper presents an Italian question answering system over linked data. We use a model-based approach to question answering based on an ontology lexicon in lemon format. The system exploits an automatically generated lexicalized grammar that can then be used to interpret and transform questions into SPARQL queries. We apply the approach for the Italian language and implement a question answering system that can answer more than 1.6 million questions over the DBpedia knowledge graph.

Introduction
As the amount of linked data published on the Web keeps increasing, there is an expanding demand for multilingual tools and user interfaces that simplify the access and browsing of data by end-users, so that information can be explored in an intuitive way. This need is what motivated the development of tools such as Question Answering (QA) systems, whose main aim is to make users be able to explore complex datasets and an ever growing amount of data in an intuitive way, through natural language.  While the default approach for many NLP tasks has recently been represented by machine learning systems, the use of such approaches <cit.> for QA over RDF data suffers from lack of controllability, making the governance and incremental improvement of the system challenging, not to mention the initial effort of collecting and providing training data for a specific language.    An alternative is the so-called model-based approach to QA, in which a model is first used to specify how concepts and relations are realized in natural language, and then this specification is employed to interpret questions from users. One such system is the one proposed by <cit.>, which makes use of a lexicon in lemon format <cit.> to specify how the vocabulary elements of an ontology or knowledge graph (e.g., entities and relations from a Knowledge Graph) are realized in natural language.  The previous work on this approach shows how, leveraging on lemon lexica, question answering grammars can be automatically generated, and those can, in turn, be used to interpret questions and then parse them into SPARQL queries. A QA web application developed in previous work <cit.> has further shown that such QA systems can scale to large numbers of questions and that the performance of the system is practically real-time from an end-user perspective.   In this work we describe the extension to the Italian language of the model-based approach described in <cit.> and the QA system described in <cit.>. By doing so, we develop a QA system that can answer more than 1.6 million Italian questions over the DBpedia knowledge graph[https://www.dbpedia.org/].   todo: this part has to be removed if we do no evaluationFurthermore, we present results showing that a large percentage of the Italian questions of the QALD-7 corpus <cit.> can be rephrased in terms of Italian questions that our grammar can cover.     The approach and the system will be of interest to a wide community of researchers in the areas of Semantic Web, NLP, and especially for those working on Italian question answering. TODO: we can probably remove the previous sentence, unless the conference requires such a statement.

Conclusion
We presented an approach to developing Italian QA systems over linked data that relies on the automatic generation of grammars from corresponding lemon lexica describing how elements of the dataset are realized in natural language. The approach is controllable, since the introduction of a lexical entry increases the question coverage in a fully predictable way. Our proof-of-concept implementation over DBpedia covers 1.6 million questions generated from 249 lemon entries. In future work, we intend to further automatize grammar generation by using LexExMachina <cit.>, which induces lexicon entries bridging the gap between ontology and natural language from a corpus in an unsupervised manner.