Discovering semantic biomedical relations utilizing the Web

Abstract

To realize the vision of a Semantic Web for Life Sciences, discovering relations between resources is essential. It is very difficult to automatically extract relations from Web pages expressed in natural language formats. On the other hand, because of the explosive growth of information, it is difficult to manually extract the relations. In this paper we present techniques to automatically discover relations between biomedical resources from the Web. For this purpose we retrieve relevant information from Web Search engines and Pubmed database using various lexico-syntactic patterns as queries over SOAP web services. The patterns are initially handcrafted but can be progressively learnt. The extracted relations can be used to construct and augment ontologies and knowledge bases. Experiments are presented for general biomedical relation discovery and domain specific search to show the usefulness of our technique.

Connections between the lines

“…The pattern-based approaches [1,9,21,29] and syntax based approaches [3,14] require patterns or parsers which are meticulously handcrafted, often fragile, and typically need several examples of desired relationships limiting the type of relationships that can be discovered. In contrast, Nubbi makes minimal assumptions about the input text, and is thus practical for languages and non-linguistic data where parsing is not available or applicable.…”

BioOntoVerb: A top level ontology based framework to populate biomedical ontologies from texts

“…In [43] the authors manually construct some lexical-semantic patterns, such as ''d is caused by e'', which are able to extract basic information from biomedical web snippets. With the obtained information, they enrich some existing resources such as UMLS.…”

Topological analysis of knowledge maps

“…The nodes of a semantic network represent concepts or terms, the edges are semantic relations [13,14]. Holanda et al [15] studied the Moby Thesaurus [16], in which the terms are seen as vertices and their relationships as edges.…”