End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings

• URL: http://arxiv.org/abs/2002.11143v1
• Date: Tue, 25 Feb 2020 19:07:54 GMT
• Title: End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings
• Authors: Rostislav Nedelchev, Debanjan Chaudhuri, Jens Lehmann and Asja Fischer
• Abstract summary: We propose the first end-to-end neural network approach that employs KG as well as word embeddings to perform joint relation and entity classification of simple questions. An empirical evaluation shows that the proposed approach achieves a performance comparable to state-of-the-art entity linking.
• Score: 20.4826750211045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entity linking - connecting entity mentions in a natural language utterance to knowledge graph (KG) entities is a crucial step for question answering over KGs. It is often based on measuring the string similarity between the entity label and its mention in the question. The relation referred to in the question can help to disambiguate between entities with the same label. This can be misleading if an incorrect relation has been identified in the relation linking step. However, an incorrect relation may still be semantically similar to the relation in which the correct entity forms a triple within the KG; which could be captured by the similarity of their KG embeddings. Based on this idea, we propose the first end-to-end neural network approach that employs KG as well as word embeddings to perform joint relation and entity classification of simple questions while implicitly performing entity disambiguation with the help of a novel gating mechanism. An empirical evaluation shows that the proposed approach achieves a performance comparable to state-of-the-art entity linking while requiring less post-processing.

Related papers

• Entity Disambiguation via Fusion Entity Decoding [68.77265315142296]
  We propose an encoder-decoder model to disambiguate entities with more detailed entity descriptions. We observe +1.5% improvements in end-to-end entity linking in the GERBIL benchmark compared with EntQA.
  arXiv  Detail & Related papers  (2024-04-02T04:27:54Z)
• Relation-Aware Question Answering for Heterogeneous Knowledge Graphs [37.38138785470231]
  Existing retrieval-based approaches solve this task by concentrating on the specific relation at different hops. We claim they fail to utilize information from head-tail entities and the semantic connection between relations to enhance the current relation representation. Our approach achieves a significant performance gain over the prior state-of-the-art.
  arXiv  Detail & Related papers  (2023-12-19T08:01:48Z)
• V-Coder: Adaptive AutoEncoder for Semantic Disclosure in Knowledge Graphs [4.493174773769076]
  We propose a new adaptive AutoEncoder, called V-Coder, to identify relations inherently connecting entities from different domains. The evaluation on real-world datasets shows that the V-Coder is able to recover links from corrupted data.
  arXiv  Detail & Related papers  (2022-07-22T14:51:46Z)
• Exploiting Global Semantic Similarities in Knowledge Graphs by Relational Prototype Entities [55.952077365016066]
  An empirical observation is that the head and tail entities connected by the same relation often share similar semantic attributes. We propose a novel approach, which introduces a set of virtual nodes called textittextbfrelational prototype entities. By enforcing the entities' embeddings close to their associated prototypes' embeddings, our approach can effectively encourage the global semantic similarities of entities.
  arXiv  Detail & Related papers  (2022-06-16T09:25:33Z)
• Exploiting Transitivity Constraints for Entity Matching in Knowledge Graphs [1.7080853582489066]
  We show that an ad-hoc enforcement of transitivity on identified set of entity pairs may decrease precision dramatically. We propose a methodology that starts with a given similarity measure, generates a set of entity pairs that are identified as referring to the same real-world objects, and applies the cluster editing algorithm to enforce transitivity without adding many spurious links.
  arXiv  Detail & Related papers  (2021-04-22T10:57:01Z)
• RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment [14.287681294725438]
  We propose a novel framework based on Relation-aware Graph Attention Networks to capture the interactions between entities and relations. Our framework adopts the self-attention mechanism to spread entity information to the relations and then aggregate relation information back to entities.
  arXiv  Detail & Related papers  (2021-03-01T06:30:51Z)
• Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training [49.9995628166064]
  We propose CTEG, a model equipped with two mechanisms to learn to decouple easily-confused relations. On the one hand, an EGA mechanism is introduced to guide the attention to filter out information causing confusion. On the other hand, a Confusion-Aware Training (CAT) method is proposed to explicitly learn to distinguish relations.
  arXiv  Detail & Related papers  (2020-10-21T11:07:53Z)
• Joint Semantics and Data-Driven Path Representation for Knowledge Graph Inference [60.048447849653876]
  We propose a novel joint semantics and data-driven path representation that balances explainability and generalization in the framework of KG embedding. Our proposed model is evaluated on two classes of tasks: link prediction and path query answering task.
  arXiv  Detail & Related papers  (2020-10-06T10:24:45Z)
• Autoregressive Entity Retrieval [55.38027440347138]
  Entities are at the center of how we represent and aggregate knowledge. The ability to retrieve such entities given a query is fundamental for knowledge-intensive tasks such as entity linking and open-domain question answering. We propose GENRE, the first system that retrieves entities by generating their unique names, left to right, token-by-token in an autoregressive fashion.
  arXiv  Detail & Related papers  (2020-10-02T10:13:31Z)
• Relational Message Passing for Knowledge Graph Completion [78.47976646383222]
  We propose a relational message passing method for knowledge graph completion. It passes relational messages among edges iteratively to aggregate neighborhood information. Results show our method outperforms stateof-the-art knowledge completion methods by a large margin.
  arXiv  Detail & Related papers  (2020-02-17T03:33:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.