Disease Normalization with Graph Embeddings

Pujary, Dhruba; Thorne, Camilo; Aziz, Wilker

doi:10.1007/978-3-030-55187-2_18

Cited by 9 publications

(7 citation statements)

References 16 publications

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“…Graph embeddings are also often used for other taxonomyrelated tasks, e.g. entity linking [66]. As for the taxonomy enrichment task, we are only aware of a recent approach TaxoExpan [71] which applies position-enhanced graph neural networks (GCN [35] and GAT [82]) that we also evaluate on our datasets.…”

Section: Graph-based Representations For Taxonomiesmentioning

confidence: 99%

Taxonomy enrichment with text and graph vector representations

Nikishina

Tikhomirov

Logacheva

et al. 2022

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Knowledge graphs such as DBpedia, Freebase or Wikidata always contain a taxonomic backbone that allows the arrangement and structuring of various concepts in accordance with hypo-hypernym (“class-subclass”) relationship. With the rapid growth of lexical resources for specific domains, the problem of automatic extension of the existing knowledge bases with new words is becoming more and more widespread. In this paper, we address the problem of taxonomy enrichment which aims at adding new words to the existing taxonomy. We present a new method which allows achieving high results on this task with little effort. It uses the resources which exist for the majority of languages, making the method universal. We extend our method by incorporating deep representations of graph structures like node2vec, Poincaré embeddings, GCN etc. that have recently demonstrated promising results on various NLP tasks. Furthermore, combining these representations with word embeddings allows us to beat the state of the art. We conduct a comprehensive study of the existing approaches to taxonomy enrichment based on word and graph vector representations and their fusion approaches. We also explore the ways of using deep learning architectures to extend taxonomic backbones of knowledge graphs. We create a number of datasets for taxonomy extension for English and Russian. We achieve state-of-the-art results across different datasets and provide an in-depth error analysis of mistakes.

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Section: Graph-based Representations For Taxonomiesmentioning

confidence: 99%

Taxonomy enrichment with text and graph vector representations

Nikishina

Tikhomirov

Logacheva

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Graph embeddings are also often used for other taxonomy-related tasks, e.g. entity linking [60]. As for the taxonomy enrichment task, we are only aware of a recent approach TaxoExpan [61] which applies position-enhanced graph neural networks (GCN [62] and GAT [63]) that we also evaluate on our datasets 3 .…”

Section: Graph-based Representations For Taxonomiesmentioning

confidence: 99%

Taxonomy Enrichment with Text and Graph Vector Representations

Nikishina¹,

Tikhomirov²,

Logacheva³

et al. 2022

Preprint

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Knowledge graphs such as DBpedia, Freebase or Wikidata always contain a taxonomic backbone that allows the arrangement and structuring of various concepts in accordance with hypo-hypernym ("class-subclass") relationship. With the rapid growth of lexical resources for specific domains, the problem of automatic extension of the existing knowledge bases with new words is becoming more and more widespread. In this paper, we address the problem of taxonomy enrichment which aims at adding new words to the existing taxonomy.We present a new method which allows achieving high results on this task with little effort. It uses the resources which exist for the majority of languages, making the method universal. We extend our method by incorporating deep representations of graph structures like node2vec, Poincaré embeddings, GCN etc. that have recently demonstrated promising results on various NLP tasks. Furthermore, combining these representations with word embeddings allows us to beat the state of the art.We conduct a comprehensive study of the existing approaches to taxonomy enrichment based on word and graph vector representations and their fusion approaches. We also explore the ways of using deep learning architectures to extend taxonomic backbones of knowledge graphs. We create a number of datasets for taxonomy extension for English and Russian. We achieve state-of-the-art results across different datasets and provide an in-depth error analysis of mistakes.

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“…GCN also provides a powerful toolkit for embedding the taxonomies into low dimension represen-tations that could be utilized for specific tasks. For instance, Pujary et al (2020) used GCN to learn an undirected graph derived from disease names in the MeSH taxonomy in order to detect and normalize disease mentions in biomedical texts.…”

Section: Graph Convolutional Network In Natural Language Processingmentioning

confidence: 99%

KenMeSH: Knowledge-enhanced End-to-end Biomedical Text Labelling

Wang¹,

Mercer²,

Rudzicz³

2022

Preprint

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Currently, Medical Subject Headings (MeSH) are manually assigned to every biomedical article published and subsequently recorded in the PubMed database to facilitate retrieving relevant information. With the rapid growth of the PubMed database, large-scale biomedical document indexing becomes increasingly important. MeSH indexing is a challenging task for machine learning, as it needs to assign multiple labels to each article from an extremely large hierachically organized collection. To address this challenge, we propose KenMeSH, an end-to-end model that combines new text features and a dynamic Knowledge-enhanced mask attention that integrates document features with MeSH label hierarchy and journal correlation features to index MeSH terms. Experimental results show the proposed method achieves state-of-the-art performance on a number of measures.

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Disease Normalization with Graph Embeddings

Cited by 9 publications

References 16 publications

Taxonomy enrichment with text and graph vector representations

Taxonomy enrichment with text and graph vector representations

Taxonomy Enrichment with Text and Graph Vector Representations

KenMeSH: Knowledge-enhanced End-to-end Biomedical Text Labelling

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