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}}
wsdm 2017

Utilizing Knowledge Bases in Text-centric Information Retrieval (WSDM 2017)

The past decade has witnessed the emergence of several publicly available and proprietary knowledge graphs (KGs). The increasing depth and breadth of content in KGs makes them not only rich sources of structured knowledge by themselves but also valuable resources for search systems. A surge of recent developments in entity linking and retrieval methods gave rise to a new line of research that aims at utilizing KGs for text-centric retrieval applications, making this an ideal time to pause and report current findings to the community, summarizing successful approaches, and soliciting new ideas. This tutorial is the first to disseminate the progress in this emerging field to researchers and practitioners.