NDLI: Random Semantic Tensor Ensemble for Scalable Knowledge Graph Link Prediction

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Random Semantic Tensor Ensemble for Scalable Knowledge Graph Link Prediction

Content Provider	ACM Digital Library
Author	Brauer, Falk Luu, Anh Tuan Hui, Siu Cheung Tay, Yi
Abstract	Link prediction on knowledge graphs is useful in numerous application areas such as semantic search, question answering, entity disambiguation, enterprise decision support, recommender systems and so on. While many of these applications require a reasonably quick response and may operate on data that is constantly changing, existing methods often lack speed and adaptability to cope with these requirements. This is aggravated by the fact that knowledge graphs are often extremely large and may easily contain millions of entities rendering many of these methods impractical. In this paper, we address the weaknesses of current methods by proposing Random Semantic Tensor Ensemble (RSTE), a scalable ensemble-enabled framework based on tensor factorization. Our proposed approach samples a knowledge graph tensor in its graph representation and performs link prediction via ensembles of tensor factorization. Our experiments on both publicly available datasets and real world enterprise/sales knowledge bases have shown that our approach is not only highly scalable, parallelizable and memory efficient, but also able to increase the prediction accuracy significantly across all datasets.
Starting Page	751
Ending Page	760
Page Count	10
File Format	PDF
ISBN	9781450346757
DOI	10.1145/3018661.3018695
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-02-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Semantic search Tensor factorization Knowledge graphs Ensemble methods
Content Type	Text
Resource Type	Article

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