Abstract
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 best captures the semantic similarity between a relationship instance and a template, whilst providing more contextual information.
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Notes
- 1.
- 2.
CVT entities are special entities in Freebase that are used to model attributes of relationships (e.g., date of marriage).
- 3.
We tag the sentences with POS tags and ignore unigram surface forms that are verbs.
- 4.
A manual evaluation of this algorithm on a held-out, random sample of 100 sentences in our dataset revealed an average of 93% precision and 85% recall per sentence.
- 5.
For example, the path \(p_1\) is a subsequence of \(y'_2\).
- 6.
Note that there might be multiple instantiations (e.g., Deadpool is also a science fiction film) and selecting the optimal one depends on the application—we leave this for future work.
- 7.
For this method we use 20% of the training data as validation data. The same test data is used for all methods.
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Acknowledgments
This research was supported by the Netherlands Institute for Sound and Vision and the Netherlands Organisation for Scientific Research (NWO) under project nr. CI-14-25. All content represents the opinion of the authors, which is not necessarily shared or endorsed by their respective employers and/or sponsors.
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Voskarides, N., Meij, E., de Rijke, M. (2017). Generating Descriptions of Entity Relationships. In: Jose, J., et al. Advances in Information Retrieval. ECIR 2017. Lecture Notes in Computer Science(), vol 10193. Springer, Cham. https://doi.org/10.1007/978-3-319-56608-5_25
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