Neural entity linking: A survey of models based on deep learning

Sevgili, Özge; Shelmanov, Artem; Архипов, М. В.; Panchenko, Alexander; Biemann, Chris

doi:10.3233/sw-222986

Cited by 77 publications

(48 citation statements)

References 159 publications

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“…We analyse different recent and older surveys on EL and highlight specific areas which are not covered as well as our survey's novelties (see also Section 8). While some very recent surveys exist [2,80,100], they do not consider the different underlying Knowledge Graphs as a significant factor affecting the performance of EL approaches. Furthermore, barely any approaches included in other surveys are working on Wikidata and take the particular characteristics of Wikidata into account (see Section 7).…”

Section: Survey Methodologymentioning

confidence: 99%

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Survey on English Entity Linking on Wikidata

Cedric¹,

Lehmann²,

Usbeck³

2021

Preprint

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Wikidata is a frequently updated, community-driven, and multilingual knowledge graph. Hence, Wikidata is an attractive basis for Entity Linking, which is evident by the recent increase in published papers. This survey focuses on four subjects:(1) Which Wikidata Entity Linking datasets exist, how widely used are they and how are they constructed? (2) Do the characteristics of Wikidata matter for the design of Entity Linking datasets and if so, how? (3) How do current Entity Linking approaches exploit the specific characteristics of Wikidata? (4) Which Wikidata characteristics are unexploited by existing Entity Linking approaches? This survey reveals that current Wikidata-specific Entity Linking datasets do not differ in their annotation scheme from schemes for other knowledge graphs like DBpedia. Thus, the potential for multilingual and time-dependent datasets, naturally suited for Wikidata, is not lifted. Furthermore, we show that most Entity Linking approaches use Wikidata in the same way as any other knowledge graph missing the chance to leverage Wikidata-specific characteristics to increase quality. Almost all approaches employ specific properties like labels and sometimes descriptions but ignore characteristics such as the hyperrelational structure. Hence, there is still room for improvement, for example, by including hyper-relational graph embeddings or type information. Many approaches also include information from Wikipedia, which is easily combinable with Wikidata and provides valuable textual information, which Wikidata lacks.

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Section: Survey Methodologymentioning

confidence: 99%

“…The extensive survey by Sevgili et al [100] is giving an overview of all neural approaches from 2015 to 2020. It compares 30 different approaches on nine different datasets.…”

Section: Related Workmentioning

confidence: 99%

Survey on English Entity Linking on Wikidata

Cedric¹,

Lehmann²,

Usbeck³

2021

Preprint

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“…This generally involves defining a similarity measure between a mention and a candidate entity, as well as some criterion that promotes the coherence of entities across multiple mentions (e.g., to ensure that the mention "Tesla" is not linked to the inventor if the rest of the document is about the car manufacturer). While older approaches generally used ad hoc measures of "semantic relatedness" and the like [93], more recent work often uses deep neural models to obtain vector representations of mentions (including their context) and entities [94,95]. Alternatively, some studies emphasize simple and fast-to-compute methods suitable for very large-scale wikification.…”

Section: Computational Linguistics: Extracting Emotions and Smellsmentioning

confidence: 99%

Harvesting Context and Mining Emotions Related to Olfactory Cultural Heritage

Massri

Novalija

Mladenić

et al. 2022

MTI

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This paper presents an Artificial Intelligence approach to mining context and emotions related to olfactory cultural heritage narratives, particularly to fairy tales. We provide an overview of the role of smell and emotions in literature, as well as highlight the importance of olfactory experience and emotions from psychology and linguistic perspectives. We introduce a methodology for extracting smells and emotions from text, as well as demonstrate the context-based visualizations related to smells and emotions implemented in a novel smell tracker tool. The evaluation is performed using a collection of fairy tales from Grimm and Andersen. We find out that fairy tales often connect smell with the emotional charge of situations. The experimental results show that we can detect smells and emotions in fairy tales with an F1 score of 91.62 and 79.2, respectively.

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“…Before focusing on specific relevant methods found in the literature for each of these components, and for the sake of completeness, we point the reader towards [18,19,1], where the first two surveys comprehensively review EL methods based on Deep Learning, while the latter presents an overview of recent advances in tasks for lifting Natural Language texts to KGs.…”

Section: Entity Linkingmentioning

confidence: 99%

Enriching a Fashion Knowledge Graph from Product Textual Descriptions

Barroca¹,

Shivkumar²,

Ferreira³

et al. 2022

Preprint

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Knowledge Graphs offer a very useful and powerful structure for representing information, consequently, they have been adopted as the backbone for many applications in e-commerce scenarios. In this paper, we describe an application of existing techniques for enriching the large-scale Fashion Knowledge Graph (FKG) that we build at Farfetch. In particular, we apply techniques for named entity recognition (NER) and entity linking (EL) in order to extract and link rich metadata from product textual descriptions to entities in the FKG. Having a complete and enriched FKG as an e-commerce backbone can have a highly valuable impact on downstream applications such as search and recommendations. However, enriching a Knowledge Graph in the fashion domain has its own challenges. Data representation is different from a more generic KG, like Wikidata and Yago, as entities (e.g. product attributes) are too specific to the domain, and long textual descriptions are not readily available. Data itself is also scarce, as labelling datasets to train supervised models is a very laborious task. Even more, fashion products display a high variability and require an intricate ontology of attributes to link to. We use a transfer learning based approach to train an NER module on a small amount of manually labeled data, followed by an EL module that links the previously identified named entities to the appropriate entities within the FKG. Experiments using a pre-trained model show that it is possible to achieve 89.75% accuracy in NER even with a small manually labeled dataset. Moreover, the EL module, despite relying on simple rulebased or ML models (due to lack of training data), is able to link relevant attributes to products, thus automatically enriching the FKG.

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Neural entity linking: A survey of models based on deep learning

Cited by 77 publications

References 159 publications

Survey on English Entity Linking on Wikidata

Survey on English Entity Linking on Wikidata

Harvesting Context and Mining Emotions Related to Olfactory Cultural Heritage

Enriching a Fashion Knowledge Graph from Product Textual Descriptions

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