Discriminative predicate path mining for fact checking in knowledge graphs

Shi, Bao‐Sen; Weninger, Tim

doi:10.1016/j.knosys.2016.04.015

Cited by 134 publications

(93 citation statements)

References 36 publications

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“…Traditional methods [7,14,16,21] were still based on manual detection, and the cost was considerable. Recently, some people have begun to study automatic KG error detection methods [8,23,31,36]. In particular, embedding-based methods [5,22,39] have gained a signi cant amount of a ention.…”

Section: Related Workmentioning

confidence: 99%

Triple Trustworthiness Measurement for Knowledge Graph

Jia

Xiang

Chen

et al. 2019

The World Wide Web Conference

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e Knowledge graph (KG) uses the triples to describe the facts in the real world. It has been widely used in intelligent analysis and applications. However, possible noises and con icts are inevitably introduced in the process of constructing. And the KG based tasks or applications assume that the knowledge in the KG is completely correct and inevitably bring about potential deviations. In this paper, we establish a knowledge graph triple trustworthiness measurement model that quantify their semantic correctness and the true degree of the facts expressed. e model is a crisscrossing neural network structure. It synthesizes the internal semantic information in the triples and the global inference information of the KG to achieve the trustworthiness measurement and fusion in the three levels of entity level, relationship level, and KG global level. We analyzed the validity of the model output con dence values, and conducted experiments in the real-world dataset FB15K (from Freebase) for the knowledge graph error detection task. e experimental results showed that compared with other models, our model achieved signi cant and consistent improvements.

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Section: Related Workmentioning

confidence: 99%

Triple Trustworthiness Measurement for Knowledge Graph

Jia

Xiang

Chen

et al. 2019

The World Wide Web Conference

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“…The Web mining cannot provide semantic explanations and also suffers from the cases where there is no enough evidence to obtain an answer. These limitations apply also for other ML fact checking systems [4,20,2,22] and motivate our choice to use a unified framework to combine both sources of signals with a probabilistic reasoner.…”

Section: Preliminariesmentioning

confidence: 99%

“…Given a KG K and a claim f , several approaches have been developed to estimate if f is a valid claim in K. In some of these methods, facts in the KG are leveraged to create features, such as paths [20,4] or embeddings [2,22], which are then used by classifiers to label as true or false a given test claim. Other methods rely on searching for occurrences of the given claim on Web pages [5,18].…”

Section: Introductionmentioning

confidence: 99%

Explainable Fact Checking with Probabilistic Answer Set Programming

Ahmadi

Lee

Papotti

et al. 2019

Proceedings of the Conference for Truth and Trust Online 2019

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One challenge in fact checking is the ability to improve the transparency of the decision. We present a fact checking method that uses reference information in knowledge graphs (KGs) to assess claims and explain its decisions. KGs contain a formal representation of knowledge with semantic descriptions of entities and their relationships. We exploit such rich semantics to produce interpretable explanations for the fact checking output. As information in a KG is inevitably incomplete, we rely on logical rule discovery and on Web text mining to gather the evidence to assess a given claim. Uncertain rules and facts are turned into logical programs and the checking task is modeled as an inference problem in a probabilistic extension of answer set programs. Experiments show that the probabilistic inference enables the efficient labeling of claims with interpretable explanations, and the quality of the results is higher than state of the art baselines.

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“…The verification task has sometimes been also framed as a fact-checking task, in which fact-checkable claims are checked against a database for accuracy. Most work on factchecking claims has built knowledge graphs out of knowledge bases, such as Wikidata, to check the validity of claims [100,101,102,103]. Use of fact-checking techniques can be of limited help in the context of breaking news events, where much of the information is new and may not be available in knowledge bases.…”

Section: Validation and Verification Of Contentmentioning

confidence: 99%

Mining social media for newsgathering: A review

Zubiaga

2019

Online Social Networks and Media

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Social media is becoming an increasingly important data source for learning about breaking news and for following the latest developments of ongoing news. This is in part possible thanks to the existence of mobile devices, which allows anyone with access to the Internet to post updates from anywhere, leading in turn to a growing presence of citizen journalism. Consequently, social media has become a go-to resource for journalists during the process of newsgathering. Use of social media for newsgathering is however challenging, and suitable tools are needed in order to facilitate access to useful information for reporting. In this paper, we provide an overview of research in data mining and natural language processing for mining social media for newsgathering. We discuss five different areas that researchers have worked on to mitigate the challenges inherent to social media newsgathering: news discovery, curation of news, validation and verification of content, newsgathering dashboards, and other tasks. We outline the progress made so far in the field, summarise the current challenges as well as discuss future directions in the use of computational journalism to assist with social media newsgathering. This review is relevant to computer scientists researching news in social media as well as for interdisciplinary researchers interested in the intersection of computer science and journalism.

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Discriminative predicate path mining for fact checking in knowledge graphs

Cited by 134 publications

References 36 publications

Triple Trustworthiness Measurement for Knowledge Graph

Triple Trustworthiness Measurement for Knowledge Graph

Explainable Fact Checking with Probabilistic Answer Set Programming

Mining social media for newsgathering: A review

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