TIGER: Temporally Improved Graph Entity Linker

• URL: http://arxiv.org/abs/2410.09128v1
• Date: Fri, 11 Oct 2024 09:44:33 GMT
• Title: TIGER: Temporally Improved Graph Entity Linker
• Authors: Pengyu Zhang, Congfeng Cao, Paul Groth,
• Abstract summary: textbfTIGER: a textbfTemporally textbfImproved textbfGraph textbfEntity Linketextbfr. We introduce textbfTIGER: a textbfTemporally textbfImproved textbfGraph textbfEntity Linketextbfr. We enhance the learned representation, making entities
• Score: 6.111040278075022
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge graphs change over time, for example, when new entities are introduced or entity descriptions change. This impacts the performance of entity linking, a key task in many uses of knowledge graphs such as web search and recommendation. Specifically, entity linking models exhibit temporal degradation - their performance decreases the further a knowledge graph moves from its original state on which an entity linking model was trained. To tackle this challenge, we introduce \textbf{TIGER}: a \textbf{T}emporally \textbf{I}mproved \textbf{G}raph \textbf{E}ntity Linke\textbf{r}. By incorporating structural information between entities into the model, we enhance the learned representation, making entities more distinguishable over time. The core idea is to integrate graph-based information into text-based information, from which both distinct and shared embeddings are based on an entity's feature and structural relationships and their interaction. Experiments on three datasets show that our model can effectively prevent temporal degradation, demonstrating a 16.24\% performance boost over the state-of-the-art in a temporal setting when the time gap is one year and an improvement to 20.93\% as the gap expands to three years. The code and data are made available at \url{https://github.com/pengyu-zhang/TIGER-Temporally-Improved-Graph-Entity-Linker}.

Related papers

• CYCLE: Cross-Year Contrastive Learning in Entity-Linking [8.108904258003411]
  textbfCYCLE: textbfCross-textbfYear textbfContrastive textbfLearning for textbfEntity-Linking. We introduce textbfCYCLE: textbfCross-textbfYear textbfContrastive textbfLearning for textbfEntity-Linking.
  arXiv  Detail & Related papers  (2024-10-11T09:41:54Z)
• Inference over Unseen Entities, Relations and Literals on Knowledge Graphs [1.7474352892977463]
  knowledge graph embedding models have been successfully applied in the transductive setting to tackle various challenging tasks. We propose the attentive byte-pair encoding layer (BytE) to construct a triple embedding from a sequence of byte-pair encoded subword units of entities and relations. BytE leads to massive feature reuse via weight tying, since it forces a knowledge graph embedding model to learn embeddings for subword units instead of entities and relations directly.
  arXiv  Detail & Related papers  (2024-10-09T10:20:54Z)
• Hypergraph based Understanding for Document Semantic Entity Recognition [65.84258776834524]
  We build a novel hypergraph attention document semantic entity recognition framework, HGA, which uses hypergraph attention to focus on entity boundaries and entity categories at the same time. Our results on FUNSD, CORD, XFUNDIE show that our method can effectively improve the performance of semantic entity recognition tasks.
  arXiv  Detail & Related papers  (2024-07-09T14:35:49Z)
• Relation Rectification in Diffusion Model [64.84686527988809]
  We introduce a novel task termed Relation Rectification, aiming to refine the model to accurately represent a given relationship it initially fails to generate. We propose an innovative solution utilizing a Heterogeneous Graph Convolutional Network (HGCN) The lightweight HGCN adjusts the text embeddings generated by the text encoder, ensuring the accurate reflection of the textual relation in the embedding space.
  arXiv  Detail & Related papers  (2024-03-29T15:54:36Z)
• Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval [11.533614615010643]
  We propose GER to capture more fine-grained information as complementary to sentence embeddings. We learn the fine-grained information about mention/entity by aggregating information from these knowledge units. Experimental results on popular benchmarks demonstrate that our proposed GER framework performs better than previous state-of-the-art models.
  arXiv  Detail & Related papers  (2022-11-20T14:37:53Z)
• HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning [59.38797474903334]
  This paper proposes the textbfHistorical textbfStructure textbfMatching (textbfHiSMatch) model. It applies two structure encoders to capture the semantic information contained in the historical structures of the query and candidate entities. Experiments on six benchmark datasets demonstrate the significant improvement of the proposed HiSMatch model, with up to 5.6% performance improvement in MRR, compared to the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-10-18T09:39:26Z)
• Scene Graph Modification as Incremental Structure Expanding [61.84291817776118]
  We focus on scene graph modification (SGM), where the system is required to learn how to update an existing scene graph based on a natural language query. We frame SGM as a graph expansion task by introducing the incremental structure expanding (ISE) We construct a challenging dataset that contains more complicated queries and larger scene graphs than existing datasets.
  arXiv  Detail & Related papers  (2022-09-15T16:26:14Z)
• Visual FUDGE: Form Understanding via Dynamic Graph Editing [2.012425476229879]
  The proposed FUDGE model formulates this problem on a graph of text elements. It uses a Graph Convolutional Network to predict changes to the graph. FUDGE is state-of-the-art on the historical NAF dataset.
  arXiv  Detail & Related papers  (2021-05-17T23:18:39Z)
• Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion [2.580765958706854]
  We propose a model named AggrE, which conducts efficient aggregations on entity context and relation context in multi-hops. Experiment results show that AggrE is competitive to existing models.
  arXiv  Detail & Related papers  (2020-12-13T09:20:42Z)
• Graph Information Bottleneck [77.21967740646784]
  Graph Neural Networks (GNNs) provide an expressive way to fuse information from network structure and node features. Inheriting from the general Information Bottleneck (IB), GIB aims to learn the minimal sufficient representation for a given task. We show that our proposed models are more robust than state-of-the-art graph defense models.
  arXiv  Detail & Related papers  (2020-10-24T07:13:00Z)
• 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.