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Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
Abstract:
Question answering over knowledge graphs and other RDF data has been greatly
advanced, with a number of good systems providing crisp answers for natural
language questions or telegraphic queries. Some of these systems incorporate
textual sources as additional evidence for the answering process, but cannot
compute answers that are present in text alone. Conversely, systems from the IR
and NLP communities have addressed QA over text, but barely utilize semantic
data and knowledge. This paper presents the first QA system that can seamlessly
operate over RDF datasets and text corpora, or both together, in a unified
framework. Our method, called UNIQORN, builds a context graph on the fly, by
retrieving question-relevant triples from the RDF data and/or the text corpus,
where the latter case is handled by automatic information extraction. The
resulting graph is typically rich but highly noisy. UNIQORN copes with this
input by advanced graph algorithms for Group Steiner Trees, that identify the
best answer candidates in the context graph. Experimental results on several
benchmarks of complex questions with multiple entities and relations, show that
UNIQORN, an unsupervised method with only five parameters, produces results
comparable to the state-of-the-art on KGs, text corpora, and heterogeneous
sources. The graph-based methodology provides user-interpretable evidence for
the complete answering process.
