RelEx—Relation extraction using dependency parse trees

Fundel, Katrin; Küffner, Robert; Zimmer, Ralf

doi:10.1093/bioinformatics/btl616

Cited by 450 publications

(330 citation statements)

References 18 publications

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“…Consistently, recent trends regarding the application of machine learning to biological IE head toward the development of public annotated corpora, targeting such binary relations to compare systems' performances (e.g. AIMed [29], Bioinfer [30], HPRD50 [10], LLL [9]). In this paper, the ontology does not limit us to the extraction of a single relation, but allows the definition of numerous relations.…”

Section: Resultsmentioning

confidence: 99%

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Extraction of Genic Interactions with the Recursive Logical Theory of an Ontology

Manine

Alphonse

Bessières

2010

Computational Linguistics and Intelligent Text Processing

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Abstract. We introduce an Information Extraction (IE) system which uses the logical theory of an ontology as a generalisation of the typical information extraction patterns to extract biological interactions from text. This provides inferences capabilities beyond current approaches: first, our system is able to handle multiple relations; second, it allows to handle dependencies between relations, by deriving new relations from the previously extracted ones, and using inference at a semantic level; third, it addresses recursive or mutually recursive rules. In this context, automatically acquiring the resources of an IE system becomes an ontology learning task: terms, synonyms, conceptual hierarchy, relational hierarchy, and the logical theory of the ontology have to be acquired. We focus on the last point, as learning the logical theory of an ontology, and a fortiori of a recursive one, remains a seldom studied problem. We validate our approach by using a relational learning algorithm, which handles recursion, to learn a recursive logical theory from a text corpus on the bacterium Bacillus subtilis. This theory achieves a good recall and precision for the ten defined semantic relations, reaching a global recall of 67.7% and a precision of 75.5%, but more importantly, it captures complex mutually recursive interactions which were implicitly encoded in the ontology.

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Section: Resultsmentioning

confidence: 99%

“…[4][5][6][7][8]), but neglect other biological phenomenons; whereas other stress on recall by extracting general relations (e.g. [9,10]), but face precision issues originating from the important lexical diversity.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Genic Interactions with the Recursive Logical Theory of an Ontology

Manine

Alphonse

Bessières

2010

Computational Linguistics and Intelligent Text Processing

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“…Numerous research efforts have been focused on automatically extracting and analysing information from the scientific literature in order to infer putative PPIs (Blaschke et al 2001;Fundel et al 2007;Airola et al 2008). These include, the search for the co-occurrence of terms (Blaschke et al 2001) or the presence of similar Gene Ontology terms (Pesquita et al 2009) or kernel-based methods including subsequence kernels, tree kernels, shortest path kernels and graph kernels (Tikk et al 2010).…”

Section: Literature-based Data Mining Methodsmentioning

confidence: 99%

Computational Tools and Databases for the Study and Characterization of Protein Interactions

Segura¹,

Pastor²,

Fernández-Fuentes³

2012

Protein-Protein Interactions - Computational and Experimental Tools

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“…We compare our relation ontology with the protein interaction relation words that are extracted from corpora BioInfer (Pyysalo et al, 2007), BioCreAtIvE-PPI , LLL05 , Hakenberg (Hakenberg et al, 2006), RelEx (Fundel et al, 2007), Temkin (Temkin and Gilder, 2003), and Kabiljo (Kabiljo et al, 2009), in which the singular and plural of verb and noun are ignored. As in Table 4, the columns Extracted relation words, Ignored, and Recall represent the total number of extracted relation words, the number of omitted words by our method, and the recall of our ontology that is computed in Formula 3, respectively.…”

Section: Coverage Evaluationmentioning

confidence: 99%

Interaction Relation Ontology Learning

Wang

Zhang

et al. 2014

Journal of Computational Biology

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Ontology is widely used in semantic computing and reasoning, and various biomedicine ontologies have become institutionalized to make the heterogeneous knowledge computationally amenable. Relation words, especially verbs, play an important role when describing the interaction between biological entities in molecular function, biological process, and cellular component; however, comprehensive research and analysis are still lacking. In this article, we propose an automatic method to build interaction relation ontology by investigating relation verbs, analyzing the syntactic relation of PubMed abstracts to perform relation vocabulary expansion, and integrating WordNet into our method to construct the hierarchy of relation vocabulary. Five attributes are populated automatically for each word in interaction relation ontology. As a result, the interaction relation ontology is constructed; it contains a total of 963 words and covers the most relation words used in existing methods of proteins interaction relation.

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RelEx—Relation extraction using dependency parse trees

Abstract: The used natural language preprocessing tools are free for use for academic research. Test sets and relation term lists are available from our website (http://www.bio.ifi.lmu.de/publications/RelEx/).

Cited by 450 publications

References 18 publications

Extraction of Genic Interactions with the Recursive Logical Theory of an Ontology

Extraction of Genic Interactions with the Recursive Logical Theory of an Ontology

Computational Tools and Databases for the Study and Characterization of Protein Interactions

Interaction Relation Ontology Learning

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