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Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data

APWeb-WAIM 2019: Web and Big Data pp 414–430Cite as

Coherence and Salience-Based Multi-Document Relationship Mining

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11641))

Abstract

In today’s interconnected world, there is an endless 24/7 stream of new articles appearing online. Faced with these overwhelming amounts of data, it is often helpful to consider only the key entities and concepts and their relationships. This is challenging, as relevant connections may be spread across a number of disparate articles and sources. In this paper, we propose a unified framework to aid users in quickly discerning salient connections and facts from a set of related documents, and presents the resulting information in a graph-based visualization. Specifically, given a set of relevant documents as input, we firstly extract candidate facts from above sources by exploiting Open Information Extraction (Open IE) approaches. Then, we design a Two-Stage Candidate Triple Filtering (TCTF) approach based on a self-training framework to maintain only coherent facts associated with the specified document topic from the candidates and connect them in the form of an initial graph. We further construct this graph by a heuristic to ensure the final conceptual graph only consist of facts likely to represent meaningful and salient relationships, which users may explore graphically. The experiments on two real-world datasets illustrate that our extraction approach achieves 2.4% higher on the average of F-score over several OpenIE baselines. We also further present an empirical evaluation of the quality of the final generated conceptual graph towards different topics on its coverage rate of topic entities and concepts, confidence score, and the compatibility of involved facts. Experimental results show the effectiveness of our proposed approach.

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Notes

  1. 1.

    https://safetyapp.shinyapps.io/GoWvis/.

  2. 2.

    http://tagesnetzwerk.de.

  3. 3.

    http://www.newsleak.io/.

  4. 4.

    During the extraction, we applied a few straightforward transformations to correct two types of common errors such as wrong boundaries and uninformative extraction, which were caused by the syntactic analysis in extraction approaches.

  5. 5.

    https://en.wikipedia.org/wiki/Tf%E2%80%93idf.

  6. 6.

    https://github.com/letiantian/TextRank4ZH/blob/master/README.md.

  7. 7.

    As for a triple extracted by OLLIE, ClausIE, and MinIE, only when its confidence is greater than 0.85 can it be judged that the triple is a correct extraction.

  8. 8.

    The similarity scores between two candidate fact \(f_{i}\), \(f_{j}\) is computed as \(sim(f_{i}, f_{j}) = \gamma \dot{s}_{k} + (1-\gamma ) \dot{l}_{k}\), where \(s_{k}\), \(l_{k}\) denote the semantic similarity and literal similarity scores between the facts, respectively. We compute \(s_{k}\) using the Align, Disambiguate and Walk algorithm [2], while \(l_{k}\) are computed using the Jaccard index. \(\gamma =0.8\) denotes the relative degree to which the semantic similarity contributes to the overall similarity score, as opposed to the literal similarity.

  9. 9.

    Kappa implementation: https://gist.github.com/ShinNoNoir/9687179.

  10. 10.

    https://duc.nist.gov/.

  11. 11.

    http://research.signalmedia.co/newsir16/signal-dataset.html.

  12. 12.

    A triple is annotated as correct if the following conditions are met: (i) it is entailed by its corresponding clause; (ii) it is reasonable or meaningful without any context and (iii) when these three annotators mark it correct simultaneously (The inter-annotator agreement was 82% (\(\kappa \)  =  0.60)).

  13. 13.

    An entity or concept is regarded as topic concept when it occurs in the topic words list.

  14. 14.

    For popular OpenIE systems such as OLLIE, ClausIE, and MinIE, we use the confidence value computed by each system itself as the confidence score of each of facts.

  15. 15.

    We mark top-2 performance results in F-score in bold face.

  16. 16.

    The random forest classifier has an average absolute 87% higher on the F-score metric for different topics when the model has converged.

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Acknowledgments

This paper was partially supported by National Natural Science Foundation of China (Nos.61572111 and 61876034), and a Fundamental Research Fund for the Central Universities of China (No.ZYGX2016Z003).

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Authors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Yongpan Sheng & Zenglin Xu

Authors
  1. Yongpan Sheng
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  2. Zenglin Xu
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Correspondence to Zenglin Xu .

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Editors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Jie Shao

  2. Hong Kong Polytechnic University, Hong Kong, China

    Man Lung Yiu

  3. The University of Tokyo, Tokyo, Japan

    Masashi Toyoda

  4. Zhejiang University, Hangzhou, China

    Dongxiang Zhang

  5. National University of Singapore, Singapore, Singapore

    Wei Wang

  6. Peking University, Beijing, China

    Bin Cui

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Sheng, Y., Xu, Z. (2019). Coherence and Salience-Based Multi-Document Relationship Mining. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11641. Springer, Cham. https://doi.org/10.1007/978-3-030-26072-9_30

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  • DOI: https://doi.org/10.1007/978-3-030-26072-9_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26071-2

  • Online ISBN: 978-3-030-26072-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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