Fast and Accurate Entity Linking via Graph Embedding

Parravicini, Alberto; Patra, Rhicheek; Bartolini, Davide B.; Santambrogio, Marco D.

doi:10.1145/3327964.3328499

Cited by 21 publications

(12 citation statements)

References 20 publications

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“…The proximity of vector representations links words from the text with corresponding words from knowledge bases. The proposal was made in [29] to represent a graph using vector representations of low dimensionality encoding the graph's topology. The advantage of this approach is that such representations can include information about related concepts embedded in the knowledge graph structure, in contrast to other means of analysis.…”

Section: Entity Linking In a New Knowledge Basementioning

confidence: 99%

An Unsupervised Approach to Structuring and Analyzing Repetitive Semantic Structures in Free Text of Electronic Medical Records

Koshman

Funkner

Kovalchuk

2022

JPM

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Electronic medical records (EMRs) include many valuable data about patients, which is, however, unstructured. Therefore, there is a lack of both labeled medical text data in Russian and tools for automatic annotation. As a result, today, it is hardly feasible for researchers to utilize text data of EMRs in training machine learning models in the biomedical domain. We present an unsupervised approach to medical data annotation. Syntactic trees are produced from initial sentences using morphological and syntactical analyses. In retrieved trees, similar subtrees are grouped using Node2Vec and Word2Vec and labeled using domain vocabularies and Wikidata categories. The usage of Wikidata categories increased the fraction of labeled sentences 5.5 times compared to labeling with domain vocabularies only. We show on a validation dataset that the proposed labeling method generates meaningful labels correctly for 92.7% of groups. Annotation with domain vocabularies and Wikidata categories covered more than 82% of sentences of the corpus, extended with timestamp and event labels 97% of sentences got covered. The obtained method can be used to label EMRs in Russian automatically. Additionally, the proposed methodology can be applied to other languages, which lack resources for automatic labeling and domain vocabulary.

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Section: Entity Linking In a New Knowledge Basementioning

confidence: 99%

An Unsupervised Approach to Structuring and Analyzing Repetitive Semantic Structures in Free Text of Electronic Medical Records

Koshman

Funkner

Kovalchuk

2022

JPM

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“…NED in this context aims to match the author names to unique (unambiguous) author identifiers [7,[9][10][11].…”

Section: Related Workmentioning

confidence: 99%

FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings

et al. 2021

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Data often have a relational nature that is most easily expressed in a network form, with its main components consisting of nodes that represent real objects and links that signify the relations between these objects. Modeling networks is useful for many purposes, but the efficacy of downstream tasks is often hampered by data quality issues related to their construction. In many constructed networks, ambiguity may arise when a node corresponds to multiple concepts. Similarly, a single entity can be mistakenly represented by several different nodes. In this paper, we formalize both the node disambiguation (NDA) and node deduplication (NDD) tasks to resolve these data quality issues. We then introduce FONDUE, a framework for utilizing network embedding methods for data-driven disambiguation and deduplication of nodes. Given an undirected and unweighted network, FONDUE-NDA identifies nodes that appear to correspond to multiple entities for subsequent splitting and suggests how to split them (node disambiguation), whereas FONDUE-NDD identifies nodes that appear to correspond to same entity for merging (node deduplication), using only the network topology. From controlled experiments on benchmark networks, we find that FONDUE-NDA is substantially and consistently more accurate with lower computational cost in identifying ambiguous nodes, and that FONDUE-NDD is a competitive alternative for node deduplication, when compared to state-of-the-art alternatives.

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“…Такая информация помогает понять, какое положение сущность занимает в графе, какими отношениями она связана с другими сущностями и др. Например, в статье [18] авторы строят векторные представления ребер графа, полученного из Dbpedia, с помощью алгоритма DeepWalk [19]. В работе [20] авторы используют алгоритм TransE [21] для векторизации сущностей в графе.…”

Section: обзор методовunclassified

Automatic Linking of Terms from Scientific Texts with Knowledge Base Entities

Mezentseva

Bruches

Batura

2021

jour

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Due to the growth of the number of scientific publications, the tasks related to scientific article processing become more actual. Such texts have a special structure, lexical and semantic content that should be taken into account while processing. Using information from knowledge bases can significantly improve the quality of text processing systems. This paper is dedicated to the entity linking task for scientific articles in Russian, where we consider scientific terms as entities. During our work, we annotated a corpus with scientific texts, where each term was linked with an entity from a knowledge base. Also, we implemented an algorithm for entity linking and evaluated it on the corpus. The algorithm consists of two stages: candidate generation for an input term and ranking this set of candidates to choose the best match. We used string matching of an input term and an entity in a knowledge base to generate a set of candidates. To rank the candidates and choose the most relevant entity for a term, information about the number of links to other entities within the knowledge base and to other sites is used. We analyzed the obtained results and proposed possible ways to improve the quality of the algorithm, for example, using information about the context and a knowledge base structure. The annotated corpus is publicly available and can be useful for other researchers.

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Fast and Accurate Entity Linking via Graph Embedding

Cited by 21 publications

References 20 publications

An Unsupervised Approach to Structuring and Analyzing Repetitive Semantic Structures in Free Text of Electronic Medical Records

An Unsupervised Approach to Structuring and Analyzing Repetitive Semantic Structures in Free Text of Electronic Medical Records

FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings

Automatic Linking of Terms from Scientific Texts with Knowledge Base Entities

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