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How Can Ontologies Give You Clue for Truth-Discovery? An Exploratory Study
Abstract
The main aim of truth-finding methods is to identify the most reliable and trustworthy data among a set of facts. Since existing methods assume a single true value, they cannot deal with numerous real-world use cases in which a set of true values exists for a given fact, even for functional predicate (e.g. Picasso is born in Màlaga and in Spain). This paper studies how traditional truth-finding methods can be adapted to this setting. After introducing a new definition of true value and discussing associated implications, we propose an approach that can be used to identify true values among a set of non-conflicting claims; it takes advantage of belief functions to incorporate knowledge about value relationships in the form of a partial ordering of claimed values. By reducing the error rate up to 30% adapting classical approaches, the effectiveness and suitability of our proposal is clearly highlighted through empirical experiments performed on DBpedia.
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Hamed, A. A., Zachara-Szymanska, M., & Wu, X. (2024) · iScience · doi:10.1016/j.isci.2024.108782 · Cited by 38
Metadata
- Authors
- Beretta, V., Harispe, S., Ranwez, S., & Mougenot, I.
- Affiliations
- IMT Mines Alès (FR)
- Université de Montpellier (FR)
- Venue
- Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics
- Publisher
- ACM
- Published
- 2016-06-13
- Pages
- 1-12
- OpenAlex
- W2497433576
- Cited by
- 8
- References
- 0
- Citations by year
- 2024: 1 · 2022: 1 · 2019: 1 · 2018: 4 · 2017: 1
- FWCI
- 1.08
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Web Intelligence, Mining and Semantics (publishedVersion)
- HAL (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Physical Sciences › Computer Science › Information Systems › Data Quality and Management
- Keywords
- truth discovery, ontology (information science), belief functions, DBpedia, knowledge base quality
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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