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Discovering disjoint object property pairs in knowledge graphs using Probabilistic Soft Logic

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Abstract

Although Knowledge Graphs (KGs) have turned out to become a popular and powerful tool in the industry world, the major focus of most researchers has been only on adding more and more triples to the A-Boxes of the KGs. An often overlooked but important part of a KG is its T-Box. If the T-Box contains incorrect statements or if certain correct statements are absent in it, it can lead to inconsistent knowledge in the KG or to information loss respectively. In this paper, we propose a novel system, DOPLEX, based on Probabilistic Soft Logic (PSL) to detect disjointness between pairs of object properties present in the KG. Current approaches mainly rely on checking the absence of common triples and miss out on exploiting the semantics of property names. In the proposed system, in addition to checking common triples, PSL is used to determine if property names imply disjointness. We particularly focus on knowledge graphs that are auto-extracted from large text corpora. Our evaluation demonstrates that the proposed approach discovers disjoint property pairs with better precision when compared to the state-of-the-art system without compromising much on the number of disjoint pairs discovered. Towards the end of the paper, we discuss the disjointness of properties in the context of time and propose a new notion called temporal-non-disjointness and discuss its importance and characteristics. We also present an approach for the discovery of property pairs that are potentially temporally non-disjoint.

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Data availability

The data generated during and/or analysed during the current study are available in the project web page: https://sites.google.com/site/ontoworks/projects.

Notes

  1. The actual property name in NELL is countryleadbyperson. We use camel case for property names in NELL to improve readability.

  2. Insight obtained through NELL’s discussion forum.

  3. http://rtw.ml.cmu.edu/rtw/kbbrowser/pred:writerwrotebook.

  4. http://rtw.ml.cmu.edu/rtw/kbbrowser/pred:riverflowsthroughcity.

  5. http://rtw.ml.cmu.edu/rtw/kbbrowser/pred:personbornincountry.

  6. http://rtw.ml.cmu.edu/rtw/kbbrowser/pred:countryleadbyperson.

  7. http://rtw.ml.cmu.edu/rtw/kbbrowser/pred:countryhascitizen.

  8. https://conceptnet.io/.

  9. https://sites.google.com/site/ontoworks/projects.

  10. https://pypi.org/project/language-tool-python/.

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  1. Department of Computer Science and Engineering, IIT Madras, Chennai, Tamil Nadu, 600036, India

    S. Subhashree & P. Sreenivasa Kumar

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Subhashree, S., Kumar, P.S. Discovering disjoint object property pairs in knowledge graphs using Probabilistic Soft Logic. Knowl Inf Syst 65, 899–919 (2023). https://doi.org/10.1007/s10115-022-01773-7

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