Enriching the Gene Ontology via the Dissection of Labels Using the Ontology Pre-processor Language

Fernández‐Breis, Jesualdo Tomás; Iannone, Luigi; Palmisano, Ignazio; Rector, Alan L.; Stevens, Robert

doi:10.1007/978-3-642-16438-5_5

Cited by 16 publications

(19 citation statements)

References 7 publications

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“…This is to combine the techniques and methods of matching linguistic, syntactic, semantic or structural. Reference [38] enriched ontologies based on thin semantic analysis of concept of labels and in the fact that regularities exist in the way of naming them. These naming conventions are used to establish mappings between these labels and axioms of the ontology, which makes semantic information explicit and then use it to automatically reason above.…”

Section: Discussionmentioning

confidence: 99%

Enhancing eHealth Information Systems for chronic diseases remote monitoring systems

Hassani¹

2012

IJACSA

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Abstract-Statistics and demographics for the aging population in Europe are compelling. The stakes are then in terms of disability and chronic diseases whose proportions will increase because of increased life expectancy. Heart failure (HF), a serious chronic disease, induces frequent re-hospitalizations, some of which can be prevented by up-stream actions. Managing HF is quite a complex process: long, often difficult and expensive. In France, nearly one million people suffer from HF and 120,000 new cases are diagnosed every year. Managing such patients, a telemedicine system tools associated with motivation and education can significantly reduce the number of hospital days that believes therefore that the patient is hospitalized for acute HF. The current development projects are fully in prevention, human security, and remote monitoring of people in their living day-to-day spaces, from the perspective of health and wellness. These projects encompass gathering, organizing, structuring and sharing medical information. They also have to take into account the main aspects of interoperability. A different approach has been used to capitalize on such information: data warehouse approach, mediation approach (or integration by views) or integration approach by link (or so-called mashup).In this paper, we will focus on ontologies that take a central place in the Semantic Web: on one hand, they rely on modeling from conceptual representations of the areas concerned and, on the other hand, they allow programs to make inferences over them.

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Section: Discussionmentioning

confidence: 99%

Enhancing eHealth Information Systems for chronic diseases remote monitoring systems

Hassani¹

2012

IJACSA

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“…We illustrate the OntoEnrich platform with the GO-MF, and how the inspection of LRs might help to identify deviations or lexico-syntactic patterns like those manually detected in [2] (see Fig. 2).…”

Section: Example Of the Inspection Of Lexical Regularitiesmentioning

confidence: 99%

“…Then, the analysis of regularities in ontology labels might help to detect hidden semantics. For example, in the Gene Ontology Molecular Function ontology (GO-MF) [2], regularities like "binding" can be converted into patterns like "X binding" that enrich the ontology with axioms like "subClassOf enables some (binds some ?x)"; and these axioms can be re-used in other more specific patterns like "X receptor binding" and "X domain binding".…”

Section: Introductionmentioning

confidence: 99%

OntoEnrich: A Platform for the Lexical Analysis of Ontologies

Quesada-Martínez

Fernández-Breis

Stevens

et al. 2015

Lecture Notes in Computer Science

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao. Abstract. The content of the labels in ontologies is usually considered hidden semantics, because the domain knowledge of such labels is not available as logical axioms in the ontology. The use of systematic naming conventions as best practice for the design of the content of the labels generates labels with structural regularities, namely, lexical regularities. The structure and content of such regularities can help ontology engineers to increase the amount of machine-friendly content in ontologies, that is, to increase the number of logical axioms. In this paper we present a web platform based on the OntoEnrich framework, which detects and analyzes lexical regularities, providing a series of useful insights about the structure and content of the labels, which can be helpful for the study of the engineering of the ontologies and their axiomatic enrichment. Here, we describe its software architecture, and how it can be used for analyzing the labels of ontologies, which will be illustrated with some examples from our research studies.

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“…While in the biomedical field there have already been efforts in naming analysis, e.g., in [6,19], naming in the broad field of linked data vocabularies (where domain-specific heuristics cannot be applied) has rarely been addressed.…”

Section: Ontology Repair With Patomatmentioning

confidence: 99%

Knowledge Base Creation, Enrichment and Repair

Hellmann

Bryl

Bühmann

et al. 2014

Lecture Notes in Computer Science

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Abstract. This chapter focuses on data transformation to RDF and Linked Data and furthermore on the improvement of existing or extracted data especially with respect to schema enrichment and ontology repair. Tasks concerning the triplification of data are mainly grounded on existing and well-proven techniques and were refined during the lifetime of the LOD2 project and integrated into the LOD2 Stack. Triplification of legacy data, i.e. data not yet in RDF, represents the entry point for legacy systems to participate in the LOD cloud. While existing systems are often very useful and successful, there are notable differences between the ways knowledge bases and Wikis or databases are created and used. One of the key differences in content is in the importance and use of schematic information in knowledge bases. This information is usually absent in the source system and therefore also in many LOD knowledge bases. However, schema information is needed for consistency checking and finding modelling problems. We will present a combination of enrichment and repair steps to tackle this problem based on previous research in machine learning and knowledge representation. Overall, the Chapter describes how to enable tool-supported creation and publishing of RDF as Linked Data (Sect. 1) and how to increase the quality and value of such large knowledge bases when published on the Web (Sect. 2).

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Enriching the Gene Ontology via the Dissection of Labels Using the Ontology Pre-processor Language

Cited by 16 publications

References 7 publications

Enhancing eHealth Information Systems for chronic diseases remote monitoring systems

Enhancing eHealth Information Systems for chronic diseases remote monitoring systems

OntoEnrich: A Platform for the Lexical Analysis of Ontologies

Knowledge Base Creation, Enrichment and Repair

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