Abstract
This work studies the combination of a document retrieval and a relation extraction system for the purpose of identifying query-relevant relational facts. On the TREC Web collection, we assess extracted facts separately for correctness and relevance. Despite some TREC topics not being covered by the relation schema, we find that this approach reveals relevant facts, and in particular those not yet known in the knowledge base DBpedia. The study confirms that mention frequency, document relevance, and entity relevance are useful indicators for fact relevance. Still, the task remains an open research problem.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Dataset and additional information is available at http://relrels.dwslab.de.
- 2.
- 3.
- 4.
- 5.
We chose \(\ge 3\) in order to be above the median of the number of sentences per fact, which is 2.
References
Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: DBpedia — A crystallization point for the web of data. J. Web Semant. 7(3), 154–165 (2009)
Blanco, R., Zaragoza, H.: Finding support sentences for entities. In: Proceedings of SIGIR 2010, pp. 339–346 (2010)
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: Proceedings of AAAI 2010, pp. 1306–1313 (2010)
Dalton, J., Dietz, L., Allan, J.: Entity query feature expansion using knowledge base links. In: Proceedings of SIGIR-2014, pp. 365–374 (2014)
Fader, A., Soderland, S., Etzioni, O.: Identifying relations for open information extraction. In: Proceedings of EMNLP 2011, pp. 1535–1545 (2011)
Gabrilovich, E., Ringgaard, M., Subramanya, A.: FACC1: Freebase annotation of ClueWeb corpora, Version 1 (2013)
Roth, B., Barth, T., Chrupała, G., Gropp, M., Klakow, D.: Relationfactory: A fast, modular and effective system for knowledge base population. In: Proceedings of EACL 2014, p. 89 (2014)
Schuhmacher, M., Dietz, L., Ponzetto, S.P.: Ranking entities for web queries through text and knowledge. In: Proceedings of CIKM 2015 (2015)
Voskarides, N., Meij, E., Tsagkias, M., de Rijke, M., Weerkamp, W.: Learning to explain entity relationships in knowledge graphs. In: Proceedings of ACL 2015, pp. 564–574 (2015)
Acknowledgements
This work was in part funded by the Deutsche Forschungsgemeinschaft within the JOIN-T project (research grant PO 1900/1-1), in part by DARPA under agreement number FA8750-13-2-0020, through the Elitepostdoc program of the BW-Stiftung, an Amazon AWS grant in education, and by the Center for Intelligent Information Retrieval. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsor. We are also thankful for the support of Amina Kadry and the helpful comments of the anonymous reviewers.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Schuhmacher, M., Roth, B., Ponzetto, S.P., Dietz, L. (2016). Finding Relevant Relations in Relevant Documents. In: Ferro, N., et al. Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science(), vol 9626. Springer, Cham. https://doi.org/10.1007/978-3-319-30671-1_49
Download citation
DOI: https://doi.org/10.1007/978-3-319-30671-1_49
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30670-4
Online ISBN: 978-3-319-30671-1
eBook Packages: Computer ScienceComputer Science (R0)