UNIQORN: Unified Question Answering over RDF Knowledge Graphs and
Natural Language Text
- URL: http://arxiv.org/abs/2108.08614v7
- Date: Mon, 10 Jul 2023 18:30:18 GMT
- Title: UNIQORN: Unified Question Answering over RDF Knowledge Graphs and
Natural Language Text
- Authors: Soumajit Pramanik, Jesujoba Oluwadara Alabi, Rishiraj Saha Roy,
Gerhard Weikum
- Abstract summary: Question answering over knowledge graphs and other RDF data has been greatly advanced.
This paper presents a method for complex questions that can seamlessly operate over a mixture of RDF datasets and text corpora.
- Score: 26.59527112409611
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Question answering over knowledge graphs and other RDF data has been greatly
advanced, with a number of good techniques 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, techniques from the
IR and NLP communities have addressed QA over text, but such systems barely
utilize semantic data and knowledge. This paper presents a method for complex
questions that can seamlessly operate over a mixture of RDF datasets and text
corpora, or individual sources, in a unified framework. Our method, called
UNIQORN, builds a context graph on-the-fly, by retrieving question-relevant
evidences from the RDF data and/or a text corpus, using fine-tuned BERT models.
The resulting graph typically contains all question-relevant evidences but also
a lot of noise. UNIQORN copes with this input by a graph algorithm for Group
Steiner Trees, that identifies the best answer candidates in the context graph.
Experimental results on several benchmarks of complex questions with multiple
entities and relations, show that UNIQORN significantly outperforms
state-of-the-art methods for heterogeneous QA. The graph-based methodology
provides user-interpretable evidence for the complete answering process.
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