ExFaKT: A Framework for Explaining Facts over Knowledge Graphs and Text

By Mohamed H. Gad-Elrab, Daria Stepanova, Jacopo Urbani, Gerhard Weikum. 2019

83 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research, Information Extraction, Knowledge Graphs & Representation

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Citations in the corpus

ProoFVer: Natural Logic Theorem Proving for Fact Verification (2021, neutral): “, 2021) or perform rule discovery (Ahmadi et al., 2019; Gad-Elrab et al., 2019).”
Explainable Automated Fact-Checking for Public Health Claims (2020, background): “…form of human evaluation, e.g., enlisting annotators to score the quality of explanations with respect to some properties (Atanasova et al., 2020; Gad-Elrab et al., 2019) or through the use of an established evaluation metric in the case where explanation generation is modelled as another task…”
Explainable Fact Checking with Probabilistic Answer Set Programming (2019, background): “Recently, other proposals have studied the problem of explainable fact checking with rules, but they focus on manually crafted constraints [6, 12], while our system relies on rules only.”