Embedding-based models of Knowledge Graphs (KGs) can be used to predict the existence of missing links by ranking the entities according to some likelihood scores. An exhaustive computation of all likelihood scores is very expensive if the KG is large. To counter this problem, we propose a technique to reduce the search space by identifying smaller subsets of promising entities. Our technique first creates embeddings of subgraphs using the embeddings from the model. Then, it ranks the subgraphs with some proposed ranking functions and considers only the entities in the top k subgraphs. Our experiments show that our technique is able to reduce the search space significantly while maintaining a good recall.
CITATION STYLE
Joshi, U., & Urbani, J. (2020). Searching for Embeddings in a Haystack: Link Prediction on Knowledge Graphs with Subgraph Pruning. In The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020 (pp. 2817–2823). Association for Computing Machinery, Inc. https://doi.org/10.1145/3366423.3380043
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