The DOI matches its Crossref record.
Harvesting facts from textual web sources by constrained label propagation
Abstract
There have been major advances on automatically constructing large knowledge bases by extracting relational facts from Web and text sources. However, the world is dynamic: periodic events like sports competitions need to be interpreted with their respective timepoints, and facts such as coaching a sports team, holding political or business positions, and even marriages do not hold forever and should be augmented by their respective timespans. This paper addresses the problem of automatically harvesting temporal facts with such extended time-awareness. We employ pattern-based gathering techniques for fact candidates and construct a weighted pattern-candidate graph. Our key contribution is a system called PRAVDA based on a new kind of label propagation algorithm with a judiciously designed loss function, which iteratively processes the graph to label good temporal facts for a given set of target relations. Our experiments with online news and Wikipedia articles demonstrate the accuracy of this method.
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A few recent works have explored temporal information for the automatic development of temporal knowledge bases.
“A few recent works [Wang et al. 2010, Hoffart et al. 2011, Wang et al. 2011, Wang et al. 2012, Talukdar et al. 2012, and Kuzey and Weikum 2012] have explored such temporal information for the automatic development of temporal knowledge bases.”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Wang et al. 2011
Wang et al. 2011 proposed PRAVDA to automatically harvest temporal facts from textual web sources, especially from news articles and biography texts.
“[Wang et al. 2011] proposed PRAVDA to automatically harvest temporal facts from textual web sources, especially from news articles and biography texts.”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Wang et al. 2011
PRAVDA uses a pattern-based approach to extract the temporal candidates of facts.
“It uses a patternbased approach to extract the temporal candidates of facts.”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Wang et al. 2011
Label propagation, a semi-supervised learning algorithm, computes the confidence scores of the candidate facts.
“Then label propagation, a semi-supervised learning algorithm, computes the confidence scores of the candidate facts.”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Wang et al. 2011
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Cited by
Sorted by citations
Survey of Temporal Information Retrieval and Related Applications
Campos, R., Dias, G., Jorge, A. M., & Jatowt, A. (2015) · ACM Computing Surveys · doi:10.1145/2619088 · Cited by 262
Metadata
- Authors
- Wang, Y., Yang, B., Qu, L., Spaniol, M., & Weikum, G.
- Affiliations
- Max-Planck-Institut für Informatik, Saarbrücken, Germany
- Venue
- Proceedings of the 20th ACM international conference on Information and knowledge management
- Publisher
- ACM
- Published
- 2011-10-24
- Pages
- 837-846
- Cited by
- 37
- References
- 0
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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