InforLorV4, Hal, Curation, bibRecord, 002E58

Learning Subgraph Patterns from text for Extracting Disease–Symptom Relationships

Identifieur interne : 002E58 ( Hal/Curation ); précédent : 002E57; suivant : 002E59

Learning Subgraph Patterns from text for Extracting Disease–Symptom Relationships

Auteurs : Mohsen Hassan [France] ; Adrien Coulet [France] ; Yannick Toussaint [France]

Source :

RBID : Hal:hal-01095595

English descriptors

mix :
- Biomedical informatics, Graph Mining, Relation Extraction, Text Mining.

Abstract

To some extent, texts can be represented in the form of graphs, such as dependency graphs in which nodes represent words and edges represent grammatical dependencies between words. Graph representation of texts is an interesting alternative to string representation because it provides an additional level of abstraction over the syntax that is sometime easier to compute. In this paper, we study the use of graph mining methods on texts represented as dependency graphs, for extracting relationships between pairs of annotated entities. We propose a three step approach that includes (1) the transformation of texts in a collection of dependency graphs; (2) the selection of frequent subgraphs, named hereafter patterns, on the basis of positive sentences; and (3) the extraction of relationships by searching for occurrences of patterns in novel sentences. Our method has been experimented by extracting disease–symptom relationships from a corpus of 51,292 PubMed abstracts (428,491 sentences)related to 50 rare diseases. The extraction of correct disease–symptom relationships has been evaluated on 565 sentences, showing a precision of 0.91 and a recall of 0.49 (F-Meaure is 0.63). These preliminary experiments show the feasibility of extracting good quality relationships using frequent subgraph mining.

Url:

https://hal.inria.fr/hal-01095595

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Le document en format XML

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<abstract xml:lang="en">To some extent, texts can be represented in the form of graphs, such as dependency graphs in which nodes represent words and edges represent grammatical dependencies between words. Graph representation of texts is an interesting alternative to string representation because it provides an additional level of abstraction over the syntax that is sometime easier to compute. In this paper, we study the use of graph mining methods on texts represented as dependency graphs, for extracting relationships between pairs of annotated entities. We propose a three step approach that includes (1) the transformation of texts in a collection of dependency graphs; (2) the selection of frequent subgraphs, named hereafter patterns, on the basis of positive sentences; and (3) the extraction of relationships by searching for occurrences of patterns in novel sentences. Our method has been experimented by extracting disease–symptom relationships from a corpus of 51,292 PubMed abstracts (428,491 sentences)related to 50 rare diseases. The extraction of correct disease–symptom relationships has been evaluated on 565 sentences, showing a precision of 0.91 and a recall of 0.49 (F-Meaure is 0.63). These preliminary experiments show the feasibility of extracting good quality relationships using frequent subgraph mining.</abstract>
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Learning Subgraph Patterns from text for Extracting Disease–Symptom Relationships

Learning Subgraph Patterns from text for Extracting Disease–Symptom Relationships

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