Entity linking meets deep learning: Techniques and solutions
Entity linking (EL) is the process of linking entity mentions appearing in web text with their
corresponding entities in a knowledge base. EL plays an important role in the fields of …
corresponding entities in a knowledge base. EL plays an important role in the fields of …
Topic analysis and development in knowledge graph research: A bibliometric review on three decades
Abstract Knowledge graph as a research topic is increasingly popular to represent structural
relations between entities. Recent years have witnessed the release of various open-source …
relations between entities. Recent years have witnessed the release of various open-source …
Neural entity linking: A survey of models based on deep learning
This survey presents a comprehensive description of recent neural entity linking (EL)
systems developed since 2015 as a result of the “deep learning revolution” in natural …
systems developed since 2015 as a result of the “deep learning revolution” in natural …
A survey on incremental update for neural recommender systems
P Zhang, S Kim - arxiv preprint arxiv:2303.02851, 2023 - arxiv.org
Recommender Systems (RS) aim to provide personalized suggestions of items for users
against consumer over-choice. Although extensive research has been conducted to address …
against consumer over-choice. Although extensive research has been conducted to address …
Multimodal entity linking: a new dataset and a baseline
In this paper, we introduce a new Multimodal Entity Linking (MEL) task on the multimodal
data. The MEL task discovers entities in multiple modalities and various forms within large …
data. The MEL task discovers entities in multiple modalities and various forms within large …
Medical entity disambiguation using graph neural networks
Medical knowledge bases (KBs), distilled from biomedical literature and regulatory actions,
are expected to provide high-quality information to facilitate clinical decision making. Entity …
are expected to provide high-quality information to facilitate clinical decision making. Entity …
Out-of-distribution generalized dynamic graph neural network with disentangled intervention and invariance promotion
Dynamic graph neural networks (DyGNNs) have demonstrated powerful predictive abilities
by exploiting graph structural and temporal dynamics. However, the existing DyGNNs fail to …
by exploiting graph structural and temporal dynamics. However, the existing DyGNNs fail to …
Improving broad-coverage medical entity linking with semantic type prediction and large-scale datasets
Objectives Biomedical natural language processing tools are increasingly being applied for
broad-coverage information extraction—extracting medical information of all types in a …
broad-coverage information extraction—extracting medical information of all types in a …
A lightweight neural model for biomedical entity linking
Biomedical entity linking aims to map biomedical mentions, such as diseases and drugs, to
standard entities in a given knowledge base. The specific challenge in this context is that the …
standard entities in a given knowledge base. The specific challenge in this context is that the …
Multi-grained multimodal interaction network for entity linking
Multimodal entity linking (MEL) task, which aims at resolving ambiguous mentions to a
multimodal knowledge graph, has attracted wide attention in recent years. Though large …
multimodal knowledge graph, has attracted wide attention in recent years. Though large …