ECIR 2017

Generating descriptions of entity relationships

Large-scale knowledge graphs (KGs) store relationships between entities that are increasingly being used to improve the user experience in search applications. The structured nature of the data in KGs is typically not suitable to show to an end user and applications that utilize KGs therefore benefit from human-readable textual descriptions of KG relationships. We present a method that automatically generates textual descriptions of entity relationships by combining textual and KG information. Our method creates sentence templates for a particular relationship and then generates a textual description of a relationship instance by selecting the best template and filling it with appropriate entities. Experimental results show that a supervised variation of our method outperforms other variations as it captures the semantic similarity between a relationship instance and a template best, whilst providing more contextual information.

  • [PDF] N. Voskarides, E. Meij, and M. de Rijke, “Generating descriptions of entity relationships,” in Ecir 2017: 39th european conference on information retrieval, 2017.
    [Bibtex]
    @inproceedings{ECIR:2017:voskarides,
    Author = {Voskarides, Nikos and Meij, Edgar and de Rijke, Maarten},
    Booktitle = {ECIR 2017: 39th European Conference on Information Retrieval},
    Date-Added = {2017-01-10 21:27:37 +0000},
    Date-Modified = {2017-01-10 21:27:58 +0000},
    Month = {April},
    Publisher = {Springer},
    Series = {LNCS},
    Title = {Generating descriptions of entity relationships},
    Year = {2017}}

Learning to Explain Entity Relationships in Knowledge Graphs

We study the problem of explaining relationships between pairs of knowledge graph entities with human-readable descriptions. Our method extracts and enriches sentences that refer to an entity pair from a corpus and ranks the sentences according to how well they describe the relationship between the entities. We model this task as a learning to rank problem for sentences and employ a rich set of features. When evaluated on a large set of manually annotated sentences, we find that our method significantly improves over state-of-the-art baseline models.

  • [PDF] N. Voskarides, E. Meij, M. Tsagkias, M. de Rijke, and W. Weerkamp, “Learning to explain entity relationships in knowledge graphs,” in Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 1: long papers), 2015, pp. 564-574.
    [Bibtex]
    @inproceedings{ACL:2015:Voskarides,
    Author = {Voskarides, Nikos and Meij, Edgar and Tsagkias, Manos and de Rijke, Maarten and Weerkamp, Wouter},
    Booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
    Date-Added = {2015-08-06 13:08:02 +0000},
    Date-Modified = {2015-08-06 13:08:14 +0000},
    Location = {Beijing, China},
    Pages = {564--574},
    Publisher = {Association for Computational Linguistics},
    Title = {Learning to Explain Entity Relationships in Knowledge Graphs},
    Url = {http://aclweb.org/anthology/P15-1055},
    Year = {2015},
    Bdsk-Url-1 = {http://aclweb.org/anthology/P15-1055}}