English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Paper

UNIQORN: Unified Question Answering over RDF Knowledge Graphs and Natural Language Text

MPS-Authors

Pramanik,  Soumajit
Databases and Information Systems, MPI for Informatics, Max Planck Society;

Alabi,  Jesujoba
Databases and Information Systems, MPI for Informatics, Max Planck Society;

/persons/resource/persons185343

Saha Roy,  Rishiraj
Databases and Information Systems, MPI for Informatics, Max Planck Society;

/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (public)

arXiv:2108.08614.pdf
(Preprint), 2MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Pramanik, S., Alabi, J., Saha Roy, R., & Weikum, G. (2021). UNIQORN: Unified Question Answering over RDF Knowledge Graphs and Natural Language Text. Retrieved from https://arxiv.org/abs/2108.08614.


Cite as: http://hdl.handle.net/21.11116/0000-0009-6365-6
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.