Fact Checking via Evidence Patterns

We tackle fact checking using Knowledge Graphs (KGs) as a source of background knowledge. Our approach leverages the KG schema to generate candidate evidence patterns, that is, schema-level paths that capture the semantics of a target fact in alternative ways. Patterns verified in the data are used to both assemble semantic evidence for a fact and provide a numerical assessment of its truthfulness. We present efficient algorithms to generate and verify evidence patterns, and assemble evidence. We also provide a translation of the core of our algorithms into the SPARQL query language. Not only our approach is faster than the state of the art and offers comparable accuracy, but it can also use any SPARQL-enabled KG.

[1]  Julien Leblay.  A Declarative Approach to Data-Driven Fact Checking , 2017, AAAI.

[2]  Michael I. Jordan,et al.  Advances in Neural Information Processing Systems 30 , 1995 .

[3]  Pankaj K. Agarwal,et al.  Toward Computational Fact-Checking , 2014, Proc. VLDB Endow..

[4]  Claudio Gutiérrez,et al.  NautiLOD: A Formal Language for the Web of Data Graph , 2015, TWEB.

[5]  R.J.G. van den Berg,et al.  Knowledge-based systems : tools or toys? , 1987 .

[6]  Enrico Franconi,et al.  The Logic of Extensional RDFS , 2013, SEMWEB.

[7]  Giuseppe Pirrò,et al.  REWOrD: Semantic Relatedness in the Web of Data , 2012, AAAI.

[8]  Tim Weninger,et al.  Discriminative predicate path mining for fact checking in knowledge graphs , 2015, Knowl. Based Syst..

[9]  Gerhard Weikum,et al.  Exception-Enriched Rule Learning from Knowledge Graphs , 2016, SEMWEB.

[10]  Marcelo Arenas,et al.  nSPARQL: A navigational language for RDF , 2010, J. Web Semant..

[11]  Enrico Motta,et al.  Proceedings of the 8th International Semantic Web Conference , 2009 .

[12]  Nesime Tatbul,et al.  Proceedings of the VLDB Endowment , 2011 .

[13]  Filippo Menczer,et al.  Finding Streams in Knowledge Graphs to Support Fact Checking , 2017, 2017 IEEE International Conference on Data Mining (ICDM).

[14]  Mariano P. Consens,et al.  Extended Property Paths: Writing More SPARQL Queries in a Succinct Way , 2015, AAAI.

[15]  Jacob Ratkiewicz,et al.  Truthy: mapping the spread of astroturf in microblog streams , 2010, WWW.

[16]  J. Drife,et al.  The third edition , 2014 .

[17]  Johan Bollen,et al.  Computational Fact Checking from Knowledge Networks , 2015, PloS one.

[18]  Ponnurangam Kumaraguru,et al.  TweetCred: Real-Time Credibility Assessment of Content on Twitter , 2014, SocInfo.

[19]  Chengkai Li,et al.  Toward Automated Fact-Checking: Detecting Check-worthy Factual Claims by ClaimBuster , 2017, KDD.

[20]  Jason Weston,et al.  Translating Embeddings for Modeling Multi-relational Data , 2013, NIPS.

[21]  Zhiyuan Liu,et al.  Learning Entity and Relation Embeddings for Knowledge Graph Completion , 2015, AAAI.

[22]  Fabian M. Suchanek,et al.  Fast rule mining in ontological knowledge bases with AMIE+\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+$$\end{docu , 2015, The VLDB Journal.

[23]  Arkaitz Zubiaga,et al.  Detection and Resolution of Rumours in Social Media , 2017, ACM Comput. Surv..