A framework for ontology-based question answering with application to parasite immunology

Abstract

BackgroundLarge quantities of biomedical data are being produced at a rapid pace for a variety of organisms. With ontologies proliferating, data is increasingly being stored using the RDF data model and queried using RDF based querying languages. While existing systems facilitate the querying in various ways, the scientist must map the question in his or her mind to the interface used by the systems. The field of natural language processing has long investigated the challenges of designing natural language based retrieval systems. Recent efforts seek to bring the ability to pose natural language questions to RDF data querying systems while leveraging the associated ontologies. These analyze the input question and extract triples (subject, relationship, object), if possible, mapping them to RDF triples in the data. However, in the biomedical context, relationships between entities are not always explicit in the question and these are often complex involving many intermediate concepts.ResultsWe present a new framework, OntoNLQA, for querying RDF data annotated using ontologies which allows posing questions in natural language. OntoNLQA offers five steps in order to answer natural language questions. In comparison to previous systems, OntoNLQA differs in how some of the methods are realized. In particular, it introduces a novel approach for discovering the sophisticated semantic associations that may exist between the key terms of a natural language question, in order to build an intuitive query and retrieve precise answers. We apply this framework to the context of parasite immunology data, leading to a system called AskCuebee that allows parasitologists to pose genomic, proteomic and pathway questions in natural language related to the parasite, Trypanosoma cruzi. We separately evaluate the accuracy of each component of OntoNLQA as implemented in AskCuebee and the accuracy of the whole system. AskCuebee answers 68 % of the questions in a corpus of 125 questions, and 60 % of the questions in a new previously unseen corpus. If we allow simple corrections by the scientists, this proportion increases to 92 %.ConclusionsWe introduce a novel framework for question answering and apply it to parasite immunology data. Evaluations of translating the questions to RDF triple queries by combining machine learning, lexical similarity matching with ontology classes, properties and instances for specificity, and discovering associations between them demonstrate that the approach performs well and improves on previous systems. Subsequently, OntoNLQA offers a viable framework for building question answering systems in other biomedical domains.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-015-0029-x) contains supplementary material, which is available to authorized users.

A web-based system architecture for ontology-based data integration in the domain of IT benchmarking

“…Because the focus of their research is in transforming semantic structures identified in unstructured data sources (documents) to an RDF store that is accessible via natural language questions, the mapping of ontological concepts to (external) data sources is beyond the scope of their proposed framework. The same constraint holds true for OntoNLQA (Asiaee et al 2015), which was introduced to query RDF data annotated using ontologies to allow posing questions in natural language. In the clinical and clinical research contexts, Mate et al (2015) introduced a system for linking information of different systems using declarative transformation rules for ontologies of the source system and the target system.…”

Systematic review of question answering over knowledge bases

“…Entity linking BERT transformer (60). Distant supervision (7). Joint entity and relation linking (41)…”

“…Asiaee et al. applied a KBQA solution to parasite immunology [7], and Hamon et al. created a querying platform for linked biomedical data [8].…”

Federating Scholarly Infrastructures with GraphQL

“…Towards this goal, we leverage the Open Research Knowledge Graph 3 (ORKG) [20] and present a GraphQL-based endpoint 4 to access its data i.e., machine actionable descriptions of scholarly knowledge and knowledge comparisons. Furthermore, we build on this endpoint and propose a GraphQL-based federated endpoint that allows unified access to data from other scholarly infrastructures.…”