Answering graph pattern queries have been highly dependent on a technique—i.e., subgraph matching, however, this approach is ineffective when knowledge graphs include incorrect or incomplete information. In this paper, we present a method called PAGE that answers graph pattern queries via knowledge graph embedding methods. PAGE computes the energy (or uncertainty) of candidate answers with the learned embeddings and chooses the lower-energy candidates as answers. Our method has the two advantages: (1) PAGE is able to find latent answers hard to be found via subgraph matching and (2) presents a robust metric that enables us to compute the plausibility of an answer. In evaluations with two popular knowledge graphs, Freebase and NELL, PAGE demonstrated the performance increase by up to 28% compared to baseline KGE methods.
|Title of host publication||Big Data – BigData 2018 - 7th International Congress, Held as Part of the Services Conference Federation, SCF 2018, Proceedings|
|Editors||Latifur Khan, Liang-Jie Zhang, Kisung Lee, Francis Y. Chin, C. L. Chen|
|Number of pages||13|
|Publication status||Published - 2018|
|Event||7th International Congress on Big Data, BigData 2018 Held as Part of the Services Conference Federation, SCF 2018 - Seattle, United States|
Duration: 2018 Jun 25 → 2018 Jun 30
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||7th International Congress on Big Data, BigData 2018 Held as Part of the Services Conference Federation, SCF 2018|
|Period||18/6/25 → 18/6/30|
Bibliographical notePublisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)