Abstract
Knowledge Graphs (KGs) aim at semantically representing the world’s knowledge in the form of machine-readable graphs composed of subject-relation-object triples (facts). However, most previous KGs only consider the relationship between individual entities, ignoring connections between facts and entities, which are commonly used to depict useful information about the properties of facts. To this end, we formally introduce FactKG, a new KG form which incorporates fact nodes and extends relations from entity-level to fact-level. This new structure challenges some previous KG techniques. One of the key challenges to FactKG is how to learn compatible representation of entities and facts. In this paper, we mainly focus on the embedding task of FactKG. We contribute a benchmark WD16K with additional fact-relevant relations, and a framework FactE, which can represent facts, entities and relations in the same space via attention. Experiments demonstrate that FactE not only significantly outperforms state-of-the-art models but also brings remarkable benefits for disambiguation of 1-N relations, revealing its potential usefulness.
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Notes
- 1.
Fact-to-fact triples representing the relations between two facts (e.g., cause and effect) are not provided in Wikidata. We leave it for future work.
- 2.
Our codes and dataset can be available in http://github.com/davidlvxin/facte.
- 3.
We use the 20190506 snapshot of Wikidata.
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Acknowledgments
This work is supported by the National Key Research and Development Program of China (2020AAA0106501), the grants from the Institute for Guo Qiang, Tsinghua University (2019GQB0003) and Beijing Academy of Artificial Intelligence, and the NSFC Youth Project (62006136).
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Lv, X., Shi, J., Cao, S., Hou, L., Li, J. (2022). Triple-as-Node Knowledge Graph and Its Embeddings. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13245. Springer, Cham. https://doi.org/10.1007/978-3-031-00123-9_8
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