Linking Entities to Unseen Knowledge Bases with Arbitrary Schemas

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 834–844 June 6–11, 2021. ©2021 Association for Computational Linguistics 834 Linking Entities to Unseen Knowledge Bases with Arbitrary Schemas Yogarshi Vyas Miguel Ballesteros Amazon AI {yogarshi, ballemig}@amazon.com Abstract In entity linking, mentions of named entities in raw text are disambiguated against a knowl- edge base (KB). This work focuses on linking to unseen KBs that do not have training data and whose schema is unknown during training. Our approach relies on methods to flexibly con- vert entities with several attribute-value pairs from arbitrary KBs into flat strings, which we use in conjunction with state-of-the-art mod- els for zero-shot linking. We further improve the generalization of our model using two reg- ularization schemes based on shuffling of en- tity attributes and handling of unseen attributes. Experiments on English datasets where models are trained on the CoNLL dataset, and tested on the TAC-KBP 2010 dataset show that our mod- els are 12% (absolute) more accurate than base- line models that simply flatten entities from the target KB. Unlike prior work, our approach also allows for seamlessly combining multiple training datasets. We test this ability by adding both a completely different dataset (Wikia), as well as increasing amount of training data from the TAC-KBP 2010 training set. Our models are more accurate across the board compared to baselines. 1 Introduction Entity linking consists of linking mentions of enti- ties found in text against canonical entities found in a target knowledge base (KB). Early work in this area was motivated by the availability of large KBs with millions of entities (Bunescu and Pa¸sca, 2006). Most subsequent work has followed this tradition of linking to a handful of large, publicly available KBs such as Wikipedia, DBPedia (Auer et al., 2007) or the KBs used in the now decade-old TAC-KBP challenges (McNamee and Dang, 2009; Ji et al., 2010). As a result, previous work always assumes complete knowledge of the schema of the target KB that entity linking models are trained for, i.e. how many and which attributes are used to represent entities in the KB. This allows training su- pervised machine learning models that exploit the schema along with labeled data that link mentions to this a priori known KB. However, this strong assumption breaks down in scenarios which require linking to KBs that are not known at training time. For example, a company might want to automat- ically link mentions of its products to an internal KB of products that has a rich schema with several attributes such as product category, description, di- mensions, etc. It is very unlikely that the company will have training data of this nature, i.e. mentions of products linked to its database. Our focus is on linking entities to unseen KBs with arbitrary schemas. One solution is to annotate data that can be used to train specialized models for each target KB of interest, but this is not scal- …

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