From natural language questions to SPARQL queries: a pattern-based approach

Linked Data knowledge bases are valuable sources of knowledge which give insights, reveal facts about various relationships and provide a large amount of metadata in well-structured form. Although the format of semantic information – namely as RDF(S) – is kept simple by representing each fact as a triple of subject, property and object, the access to the knowledge is only available using SPARQL queries on the data. Therefore, Question Answering (QA) systems provide a user-friendly way to access any type of knowledge base and especially for Linked Data sources to get insight into the semantic information. As RDF(S) knowledge bases are usually structured in the same way and provide per se semantic metadata about the contained information, we provide a novel approach that is independent from the underlying knowledge base. Thus, the main contribution of our proposed approach constitutes the simple replaceability of the underlying knowledge base. The algorithm is based on general question and query patterns and only accesses the knowledge base for the actual query generation and execution. This paper presents the proposed approach and an evaluation in comparison to state-of-the-art Linked Data approaches for challenges of QA systems

Application of Definability to Query Answering over Knowledge Bases

Answering object queries (i.e. instance retrieval) is a central task in ontology based data access (OBDA). Performing this task involves reasoning with respect to a knowledge base K (i.e. ontology) over some description logic (DL) dialect L. As the expressive power of L grows, so does the complexity of reasoning with respect to K. Therefore, eliminating the need to reason with respect to a knowledge base K is desirable. In this work, we propose an optimization to improve performance of answering object queries by eliminating the need to reason with respect to the knowledge base and, instead, utilizing cached query results when possible. In particular given a DL dialect L, an object query C over some knowledge base K and a set of cached query results S={S1, ..., Sn} obtained from evaluating past queries, we rewrite C into an equivalent query D, that can be evaluated with respect to an empty knowledge base, using cached query results S' = {Si1, ..., Sim}, where S' is a subset of S. The new query D is an interpolant for the original query C with respect to K and S. To find D, we leverage a tool for enumerating interpolants of a given sentence with respect to some theory. We describe a procedure that maps a knowledge base K, expressed in terms of a description logic dialect of first order logic, and object query C into an equivalent theory and query that are input into the interpolant enumerating tool, and resulting interpolants into an object query D that can be evaluated over an empty knowledge base. We show the efficacy of our approach through experimental evaluation on a Lehigh University Benchmark (LUBM) data set, as well as on a synthetic data set, LUBMMOD, that we created by augmenting an LUBM ontology with additional axioms

MT techniques in a retrieval system of semantically enriched patents

This paper focuses on how automatic translation techniques integrated in a patent retrieval system increase its capabilities and make possible extended features and functionalities. We describe 1) a novel methodology for natural language to SPARQL translation based on a grammar– ontology interoperability automation and a query grammar for the patents domain; 2) a devised strategy for statisticalbased translation of patents that allows to transfer semantic annotations to the target language; 3) a built-in knowledge representation infrastructure that uses multilingual semantic annotations; and 4) an online application that offers a multilingual search interface over structural knowledge databases (domain ontologies) and multilingual documents (biomedical patents) that have been automatically translated.Peer ReviewedPostprint (published version

An ontological approach to creating an Andean Weaving Knowledge Base

Andean textiles are products of one of the richest, oldest and continuous weaving traditions in the world. Understanding the knowledge and practice of textile production as a form of cultural heritage is particularly relevant in the Andean context due to erosion of clothing traditions, reuse of traditional textiles on commodities targeted at the tourism market, and loss of knowledge embedded in textile production. ``Weaving Communities of Practice'' was a pilot project that aimed to create a knowledge base of Andean weaving designed to contribute to curatorial practice and heritage policy. The research team gathered data on the chain of activities, instruments, resources, peoples, places and knowledge involved in the production of textiles, relating to over 700 textile samples. A major part of the project has been the modelling and representation of the knowledge of domain experts and information about the textile objects themselves in the form of an OWL ontology, and the development of a suite of search facilities to be supported by the ontology. This paper describes the research challenges faced in developing the ontology and search facilities, the methodology adopted, the design and implementation of the system, and the design and outcomes of a user evaluation of the system undertaken with a group of domain experts