Zhou, T;
Li, Z;
Cheng, G;
Wang, J;
Wei, Y;
(2020)
GREASE: A Generative Model for Relevance Search over Knowledge Graphs.
In:
Proceedings of the 13th International Conference on Web Search and Data Mining.
(pp. pp. 780-788).
The Association for Computing Machinery
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Abstract
Relevance search is to find top-ranked entities in a knowledge graph (KG) that are relevant to a query entity. Relevance is ambiguous, particularly over a schema-rich KG like DBpedia which supports a wide range of different semantics of relevance based on numerous types of relations and attributes. As users may lack the expertise to formalize the desired semantics, supervised methods have emerged to learn the hidden user-defined relevance from user-provided examples. Along this line, in this paper we propose a novel generative model over KGs for relevance search, named GREASE. The model applies to meta-path based relevance where a meta-path characterizes a particular type of semantics of relating the query entity to answer entities. It is also extended to support properties that constrain answer entities. Extensive experiments on two large-scale KGs demonstrate that GREASE has advanced the state of the art in effectiveness, expressiveness, and efficiency.
| Type: | Proceedings paper |
|---|---|
| Title: | GREASE: A Generative Model for Relevance Search over Knowledge Graphs |
| Event: | 13th International Conference on Web Search and Data Mining |
| Location: | Houston (TX), USA |
| Dates: | 3rd-7th February 2020 |
| ISBN-13: | 978-1-4503-6822-3 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3336191.3371772 |
| Publisher version: | https://doi.org/10.1145/3336191.3371772 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | relevance search; knowledge graph; meta-path; generative model |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10093928 |
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