Abstract
Fact-checking is a crucial task for accurately populating, updating and curating knowledge graphs. Manually validating candidate facts is time-consuming. Prior work on automating this task focuses on estimating truthfulness using numerical scores which are not human-interpretable. Others extract explicit mentions of the candidate fact in the text as an evidence for the candidate fact, which can be hard to directly spot. In our work, we introduce ExFaKT, a framework focused on generating human-comprehensible explanations for candidate facts. ExFaKT uses background knowledge encoded in the form of Horn clauses to rewrite the fact in question into a set of other easier-to-spot facts. The final output of our framework is a set of semantic traces for the candidate fact from both text and knowledge graphs. The experiments demonstrate that our rewritings significantly increase the recall of fact-spotting while preserving high precision. Moreover, we show that the explanations effectively help humans to perform fact-checking and can also be exploited for automating this task.
| Language | English |
|---|---|
| Title of host publication | WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 87-95 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781450359405 |
| DOIs | |
| Publication status | Published - 30 Jan 2019 |
| Event | 12th ACM International Conference on Web Search and Data Mining, WSDM 2019 - Melbourne, Australia Duration: 11 Feb 2019 → 15 Feb 2019 |
Conference
| Conference | 12th ACM International Conference on Web Search and Data Mining, WSDM 2019 |
|---|---|
| Country | Australia |
| City | Melbourne |
| Period | 11/02/19 → 15/02/19 |
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Keywords
- Explainable evidence
- Fact-checking
- Knowledge graph
- Reasoning
Cite this
}
Exfakt : A framework for explaining facts over knowledge graphs and text. / Gad-Elrab, Mohamed H.; Urbani, Jacopo; Stepanova, Daria; Weikum, Gerhard.
WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, Inc, 2019. p. 87-95.Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Exfakt
T2 - A framework for explaining facts over knowledge graphs and text
AU - Gad-Elrab, Mohamed H.
AU - Urbani, Jacopo
AU - Stepanova, Daria
AU - Weikum, Gerhard
PY - 2019/1/30
Y1 - 2019/1/30
N2 - Fact-checking is a crucial task for accurately populating, updating and curating knowledge graphs. Manually validating candidate facts is time-consuming. Prior work on automating this task focuses on estimating truthfulness using numerical scores which are not human-interpretable. Others extract explicit mentions of the candidate fact in the text as an evidence for the candidate fact, which can be hard to directly spot. In our work, we introduce ExFaKT, a framework focused on generating human-comprehensible explanations for candidate facts. ExFaKT uses background knowledge encoded in the form of Horn clauses to rewrite the fact in question into a set of other easier-to-spot facts. The final output of our framework is a set of semantic traces for the candidate fact from both text and knowledge graphs. The experiments demonstrate that our rewritings significantly increase the recall of fact-spotting while preserving high precision. Moreover, we show that the explanations effectively help humans to perform fact-checking and can also be exploited for automating this task.
AB - Fact-checking is a crucial task for accurately populating, updating and curating knowledge graphs. Manually validating candidate facts is time-consuming. Prior work on automating this task focuses on estimating truthfulness using numerical scores which are not human-interpretable. Others extract explicit mentions of the candidate fact in the text as an evidence for the candidate fact, which can be hard to directly spot. In our work, we introduce ExFaKT, a framework focused on generating human-comprehensible explanations for candidate facts. ExFaKT uses background knowledge encoded in the form of Horn clauses to rewrite the fact in question into a set of other easier-to-spot facts. The final output of our framework is a set of semantic traces for the candidate fact from both text and knowledge graphs. The experiments demonstrate that our rewritings significantly increase the recall of fact-spotting while preserving high precision. Moreover, we show that the explanations effectively help humans to perform fact-checking and can also be exploited for automating this task.
KW - Explainable evidence
KW - Fact-checking
KW - Knowledge graph
KW - Reasoning
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U2 - 10.1145/3289600.3290996
DO - 10.1145/3289600.3290996
M3 - Conference contribution
SP - 87
EP - 95
BT - WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining
PB - Association for Computing Machinery, Inc
ER -