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PolygonE: Modeling N-ary Relational Data as Gyro-Polygons in Hyperbolic Space

Authors

Shiyao Yan

Aerospace Information Research Institute, Chinese Academy of Sciences Key Laboratory of Network Information System Technology(NIST), Aerospace Information Research Institute School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences


Zequn Zhang

Aerospace Information Research Institute, Chinese Academy of Sciences Key Laboratory of Network Information System Technology(NIST), Aerospace Information Research Institute


Xian Sun

Aerospace Information Research Institute, Chinese Academy of Sciences Key Laboratory of Network Information System Technology(NIST), Aerospace Information Research Institute


Guangluan Xu

Aerospace Information Research Institute, Chinese Academy of Sciences Key Laboratory of Network Information System Technology(NIST), Aerospace Information Research Institute


Shuchao Li

Aerospace Information Research Institute, Chinese Academy of Sciences Key Laboratory of Network Information System Technology(NIST), Aerospace Information Research Institute


Qing Liu

Aerospace Information Research Institute, Chinese Academy of Sciences Key Laboratory of Network Information System Technology(NIST), Aerospace Information Research Institute


Nayu Liu

Aerospace Information Research Institute, Chinese Academy of Sciences Key Laboratory of Network Information System Technology(NIST), Aerospace Information Research Institute School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences


Shensi Wang

Aerospace Information Research Institute, Chinese Academy of Sciences Key Laboratory of Network Information System Technology(NIST), Aerospace Information Research Institute School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences


Proceedings:

No. 4: AAAI-22 Technical Tracks 4

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Data Mining and Knowledge Management

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Abstract:

N-ary relational knowledge base (KBs) embedding aims to map binary and beyond-binary facts into low-dimensional vector space simultaneously. Existing approaches typically decompose n-ary relational facts into subtuples (entity pairs, triples or quintuples, etc.), and they generally model n-ary relational KBs in Euclidean space. However, n-ary relational facts are semantically and structurally intact, decomposition leads to the loss of global information and undermines the semantical and structural integrity. Moreover, compared to the binary relational KBs, n-ary ones are characterized by more abundant and complicated hierarchy structures, which could not be well expressed in Euclidean space. To address the issues, we propose a gyro-polygon embedding approach to realize n-ary fact integrity keeping and hierarchy capturing, termed as PolygonE. Specifically, n-ary relational facts are modeled as gyro-polygons in the hyperbolic space, where we denote entities in facts as vertexes of gyro-polygons and relations as entity translocation operations. Importantly, we design a fact plausibility measuring strategy based on the vertex-gyrocentroid geodesic to optimize the relation-adjusted gyro-polygon. Extensive experiments demonstrate that PolygonE shows SOTA performance on all benchmark datasets, generalizability to binary data, and applicability to arbitrary arity fact. Finally, we also visualize the embedding to help comprehend PolygonE's awareness of hierarchies.

DOI:

10.1609/aaai.v36i4.20351


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36