Abstract

BackgroundThis article provides an overview of the first BioASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BioASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies.ResultsThe 2013 BioASQ competition comprised two tasks, Task 1a and Task 1b. In Task 1a participants were asked to automatically annotate new PubMed documents with MeSH headings. Twelve teams participated in Task 1a, with a total of 46 system runs submitted, and one of the teams performing consistently better than the MTI indexer used by NLM to suggest MeSH headings to curators. Task 1b used benchmark datasets containing 29 development and 282 test English questions, along with gold standard (reference) answers, prepared by a team of biomedical experts from around Europe and participants had to automatically produce answers. Three teams participated in Task 1b, with 11 system runs. The BioASQ infrastructure, including benchmark datasets, evaluation mechanisms, and the results of the participants and baseline methods, is publicly available.ConclusionsA publicly available evaluation infrastructure for biomedical semantic indexing and QA has been developed, which includes benchmark datasets, and can be used to evaluate systems that: assign MeSH headings to published articles or to English questions; retrieve relevant RDF triples from ontologies, relevant articles and snippets from PubMed Central; produce “exact” and paragraph-sized “ideal” answers (summaries). The results of the systems that participated in the 2013 BioASQ competition are promising. In Task 1a one of the systems performed consistently better from the NLM’s MTI indexer. In Task 1b the systems received high scores in the manual evaluation of the “ideal” answers; hence, they produced high quality summaries as answers. Overall, BioASQ helped obtain a unified view of how techniques from text classification, semantic indexing, document and passage retrieval, question answering, and text summarization can be combined to allow biomedical experts to obtain concise, user-understandable answers to questions reflecting their real information needs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0564-6) contains supplementary material, which is available to authorized users.

Highlights

  • This article provides an overview of the first BIOASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013

  • National Library of Medicine (NLM) has reported recently that the Medical Text Indexer [28] (MTI) indexer was enhanced with ideas from the winners of Task 1a, and that the BIOASQ challenge has been a tremendous benefit for NLM by expanding their knowledge of other indexing systems [33]

  • We can observe that during the first batch the MTIFL baseline achieved the best performance in terms of the MiF measure, but was matched to the performance of the RMAIP system in terms of the Lowest Common Ancestor F-measure (LCaF) measure

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Summary

Introduction

This article provides an overview of the first BIOASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. Participants are required to semantically index documents from large-scale biomedical repositories (e.g., MEDLINE) and to assemble information from multiple heterogeneous sources (e.g., scientific articles, ontologies) in order to compose answers to real-life biomedical English questions. Existing search engines (e.g., PUBMED, GOPUBMED [2,3]) only partially address this need They focus on a limited range of resources (e.g., only MEDLINE articles and concepts from GENE ONTOLOGY or MESH), whereas multiple sources (e.g., including specialised drug databases and ontologies) often need to be combined. BIOASQ helps in the direction of establishing an evaluation framework for biomedical semantic indexing and QA systems It does so by developing realistic, high-quality benchmark datasets and adopting (or refining) existing evaluation measures for its challenge tasks

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