Constructing and Interpreting Causal Knowledge Graphs from News
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Abstract (arXiv)
Many financial jobs rely on news to learn about causal events in the past and present, to make informed decisions and predictions about the future. With the ever-increasing amount of news available online, there is a need to automate the extraction of causal events from unstructured texts. In this work, we propose a methodology to construct causal knowledge graphs (KGs) from news using two steps: (1) Extraction of Causal Relations, and (2) Argument Clustering and Representation into KG. We aim to build graphs that emphasize on recall, precision and interpretability. For extraction, although many earlier works already construct causal KGs from text, most adopt rudimentary pattern-based methods. We close this gap by using the latest BERT-based extraction models alongside pattern-based ones. As a result, we achieved a high recall, while still maintaining a high precision. For clustering, we utilized a topic modelling approach to cluster our arguments, so as to increase the connectivity of our graph. As a result, instead of 15,686 disconnected subgraphs, we were able to obtain 1 connected graph that enables users to infer more causal relationships from. Our final KG effectively captures and conveys causal relationships, validated through experiments, multiple use cases and user feedback.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 45 references that are in the map. First posted on arXiv 16 May 2023.
Builds on
11 of 45 references.
References · 11
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,168
- 2020Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!cited by 73
- 2022Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topicscited by 35
- 2021Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statementcited by 21
- 2020KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervisioncited by 20
- 2022ERGO: Event Relational Graph Transformer for Document-level Event Causality Identificationcited by 19
- 2021LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identificationcited by 16
- 2022UniCausal: Unified Benchmark and Repository for Causal Text Miningcited by 9
- 20221Cademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam-Search-based Position Selectorcited by 3
- 20221Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality Classification of Socio-Political Event Datacited by 3
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