CauseNet: Towards a Causality Graph Extracted from the Web

Causal knowledge is seen as one of the key ingredients to advance artificial intelligence. Yet, few knowledge bases comprise causal knowledge to date, possibly due to significant efforts required for validation. Notwithstanding this challenge, we compile CauseNet, a large-scale knowledge base of claimed causal relations between causal concepts. By extraction from different semi- and unstructured web sources, we collect more than 11 million causal relations with an estimated extraction precision of 83% and construct the first large-scale and open-domain causality graph. We analyze the graph to gain insights about causal beliefs expressed on the web and we demonstrate its benefits in basic causal question answering. Future work may use the graph for causal reasoning, computational argumentation, multi-hop question answering, and more.

[1]  Daniel Jurafsky,et al.  Distant supervision for relation extraction without labeled data , 2009, ACL.

[2]  Christopher D. Manning,et al.  Improved Pattern Learning for Bootstrapped Entity Extraction , 2014, CoNLL.

[3]  Kira Radinsky,et al.  Learning causality for news events prediction , 2012, WWW.

[4]  Michael Perrone,et al.  Answering Binary Causal Questions Through Large-Scale Text Mining: An Evaluation Using Cause-Effect Pairs from Human Experts , 2019, IJCAI.

[5]  Zornitsa Kozareva.  Cause-Effect Relation Learning , 2012, TextGraphs@ACL.

[6]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[7]  Luis Gravano,et al.  Snowball: extracting relations from large plain-text collections , 2000, DL '00.

[8]  Ryen W. White,et al.  Cyberchondria: Studies of the escalation of medical concerns in Web search , 2009, TOIS.

[9]  Timothy Dozat,et al.  Universal Dependency Parsing from Scratch , 2019, CoNLL.

[10]  Roland Vollgraf,et al.  Contextual String Embeddings for Sequence Labeling , 2018, COLING.

[11]  Lipika Dey,et al.  Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks , 2018, SIGDIAL Conference.

[12]  Peter Jansen,et al.  Creating Causal Embeddings for Question Answering with Minimal Supervision , 2016, EMNLP.

[13]  Firoz Mahmud,et al.  Website Classification Using Word Based Multiple N -Gram Models and Random Search Oriented Feature Parameters , 2018, 2018 21st International Conference of Computer and Information Technology (ICCIT).

[14]  James P. Bagrow,et al.  Efficient Crowd Exploration of Large Networks , 2018, Proc. ACM Hum. Comput. Interact..

[15]  Fabian M. Suchanek,et al.  YAGO3: A Knowledge Base from Multilingual Wikipedias , 2015, CIDR.

[16]  Markus Krötzsch,et al.  Wikidata , 2014, Commun. ACM.

[17]  Christian Bizer,et al.  DBpedia spotlight: shedding light on the web of documents , 2011, I-Semantics '11.

[18]  Gosse Bouma,et al.  Minimally-supervised learning of domain-specific causal relations using an open-domain corpus as knowledge base , 2013, Data Knowl. Eng..

[19]  Razvan C. Bunescu,et al.  A Shortest Path Dependency Kernel for Relation Extraction , 2005, HLT.

[20]  Qi Li,et al.  Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddings , 2019, Neurocomputing.

[21]  Simone Paolo Ponzetto,et al.  BabelNet: Building a Very Large Multilingual Semantic Network , 2010, ACL.

[22]  Preslav Nakov,et al.  SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals , 2009, SEW@NAACL-HLT.

[23]  Jianfeng Gao,et al.  A Human Generated MAchine Reading COmprehension Dataset , 2018 .

[24]  Patrick Pantel,et al.  Espresso: Leveraging Generic Patterns for Automatically Harvesting Semantic Relations , 2006, ACL.

[25]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[26]  Christopher D. Manning,et al.  Enhanced English Universal Dependencies: An Improved Representation for Natural Language Understanding Tasks , 2016, LREC.

[27]  B. Levine.  Causal models. , 2009, Epidemiology.

[28]  Jens Lehmann,et al.  DBpedia and the live extraction of structured data from Wikipedia , 2012, Program.

[29]  Catherine Havasi,et al.  ConceptNet 5.5: An Open Multilingual Graph of General Knowledge , 2016, AAAI.

[30]  Pengfei Li,et al.  Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts , 2019, Expert Syst. Appl..

[31]  Chikara Hashimoto.  Weakly Supervised Multilingual Causality Extraction from Wikipedia , 2019, EMNLP/IJCNLP.

[32]  Pieter W. Adriaans,et al.  Learning Relations from Biomedical Corpora Using Dependency Trees , 2006, KDECB.

[33]  Preschool Children's Assumptions about Cause and Effect: Temporal Ordering. , 1979 .

[34]  Menno Hulswit.  A Short History of Causation , 2004 .

[35]  Roxana Gîrju,et al.  Automatic Detection of Causal Relations for Question Answering , 2003, ACL 2003.

[36]  Sergey Brin,et al.  Extracting Patterns and Relations from the World Wide Web , 1998, WebDB.

[37]  Aron Culotta,et al.  Dependency Tree Kernels for Relation Extraction , 2004, ACL.

[38]  Jens Lehmann,et al.  DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.

[39]  Christopher Ré,et al.  A machine-compiled database of genome-wide association studies , 2019, Nature Communications.