Improving entity linking with graph networks

Author: ibod

August undefined, 2024

Witryna1 sty 2024 · The task of entity linking with knowledge graphs aims at linking mentions in text to their correct entities in a knowledge graph like DBpedia or YAGO2. Most of … The collective disambiguation approaches usually model the inter-entity coherence between linked entities and jointly disambiguate all mentions, which is very time consuming. Meanwhile, sequential decision approach disambiguates the mention independently in linear time but may ignore the coherence … Zobacz więcej In this section, we use GCN to capture global semantic meaning of entities and transfer latent relations between entities. In the first step, we get the feature matrix X which is built with words embeddings and entities … Zobacz więcej In order to solve ambiguous mention problem, we first propose our local model by incorporating external knowledge effectively with multi-hop attention. As Fig. 1 shows, we identity the true referent entity of the … Zobacz więcej

CoGCN: Combining co‐attention with graph convolutional network …

Witryna14 kwi 2024 · With the above analysis, in this paper, we propose a Class-Dynamic and Hierarchy-Constrained Network (CDHCN) for effectively entity linking.Unlike traditional label embedding methods [] embedded entity types statistically, we argue that the entity type representation should be dynamic as the meanings of the same entity type for … Witryna14 kwi 2024 · Link prediction for knowledge graphs is the task of predicting missing relationships between entities. Previous work on link prediction has focused on … can cbd cause stomach issues

Dynamic Graph Convolutional Networks for Entity Linking

Witryna23 lut 2024 · Graph Completion 1322: Improving Entity Linking by Modeling Latent Entity Type Information Shuang Chen; Jinpeng Wang; Feng Jiang; Chin-Yew Lin Harbin Institute of Technology; Microsoft Research Asia; 3019: Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction Zhanqiu Zhang; Jianyu Cai; … Witryna3 kwi 2024 · Recently, graph neural networks (GNNs) have proven to be very effective and provide state-of-the-art results for many real-world applications with graph-structured data. In this paper, we introduce ED-GNN based on three representative GNNs (GraphSAGE, R-GCN, and MAGNN) for medical entity disambiguation. We … Witryna20 paź 2024 · 1 Altmetric. Metrics. As one of the most important components in knowledge graph construction, entity linking has been drawing more and more … fishing report clark hill lake georgia

Knowledge-Graph-Tutorials-and-Papers/Entity …

Improving Entity Linking with Graph Networks

Witryna10 wrz 2024 · We propose a graph neural network-based coreference resolution method that can capture the entity-centric information by encouraging the sharing of … Witryna28 lip 2024 · Entity Linking (EL) ( Shen et al.,2015) is devoted to the disambiguation of mentions of named enti- ties such as persons, locations, and organizations. Basically, EL aims to resolve such... can cbd cause sweatingWitrynaNetworks (NN) for solving its entity linking challenges. We develop a novel ap-proach called Arjun, rst of its kind to recognise entities from the textual content ... FALCON [18] introduces the concept of using knowledge graph context for improving entity linking performance over DBpedia. Falcon creates a local KG fusing information from ... fishing report clarks hill ga

"Witryna1 dzień temu · Improving Neural Entity Disambiguation with Graph Embeddings - ACL Anthology Improving Neural Entity Disambiguation with Graph Embeddings Abstract … " - Improving entity linking with graph networks

Improving entity linking with graph networks

Witryna22 sie 2024 · Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods. Such approaches work by learning KG representations so that entity alignment can be performed by measuring the similarities between entity … Witryna12 lip 2024 · Entity Linking is essential in many NLP tasks such as improving the performances of knowledge network construction, knowledge fusion, information …

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WitrynaEntity linking aims to assign a unique identity to entities mentioned in text given a predefined Knowledge Base. Previous works address this task based on the local or … Witryna3 paź 2024 · Therefore, we observe the impacts of the link-based entity graph and embedding-based entity graph on the linking result. In Table 4, GCNLJ applies …

Witryna1 sty 2024 · The task of entity linking with knowledge graphs aims at linking mentions in text to their correct entities in a knowledge graph like DBpedia or YAGO2. Most of existing methods rely on... WitrynaInspired by the eﬀectiveness of using GCN to model the global signal,we present HEterogeneous Graph-based Entity Linker (HEGEL), a novel global EL framework designed to model the interactions among heterogeneous information from diﬀerent sources by constructing a document-level informative heterogeneous graph and …

Witryna25 lip 2024 · To link entities with ambiguity (e.g., authors), we propose heterogeneous graph attention networks to model different types of entities. Our extensive experiments and systematical analysis demonstrate that LinKG can achieve linking accuracy with an F1-score of 0.9510, significantly outperforming the state-of-the-art. Witryna1 cze 2024 · Medical entity disambiguation is an NLP task aimed at normalizing KG entity nodes, and the authors of [58] approached this problem as one of classification using Graph Neural Network. Overall ...

Witryna19 paź 2024 · EL models usually ignore such readily available entity attributes. In this paper, we examine the role of knowledge graph context on an attentive neural network approach for entity linking on Wikidata.

Witryna27 lip 2024 · Entity linking (EL) is the task of determining the identity of textual entity mentions given a predefined knowledge base (KB). Plenty of existing efforts have been made on this task using either “local” information (contextual information of the mention in the text), or “global” information (relations among candidate entities). can cbd cause weight gainWitryna28 sie 2024 · Here is two of the above list of spans that have the best score according to the example knowledge base: So it guessed "new york" is concept and "big apple" is also a concept. input = 'new york is the big apple'.split () def spans (lst): if len (lst) == 0: yield None for index in range (1, len (lst)): for span in spans (lst [index:]): if span ... fishing report clark hill lakeWitryna14 kwi 2024 · In recent years, research on knowledge graphs (KGs) has received considerable attention in both academia and industry communities. KGs usually store … fishing report cherry grove pier scWitryna18 lip 2024 · In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing … can cbdc be stoppedWitryna8 kwi 2024 · Abstract. In this work, we focus on the problem of entity alignment in Knowledge Graphs (KG) and we report on our experiences when applying a Graph … fishing report cooper lake txWitryna24 wrz 2024 · Entity linking (EL) is a fundamental task in natural language processing. Based on neural networks, existing systems pay more attention to the construction of … fishing report coloradoWitryna17 mar 2024 · NER can take advantage of the new advances in graphs and deep learning to apply to the dependency tree and explore its effects in the process of NER. Named Entity Recognition NER is used for the extraction of the entities from the given text such as identifying the names of a quantity, product name, person name etc. fishing report cpw