A Tree-Based Similarity for Evaluating Concept Proximities in an Ontology

Blanchard, Emmanuel; Kuntz, Pascale; Harzallah, Mounira; Briand, H.

doi:10.1007/3-540-34416-0_1

Cited by 11 publications

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“…The type of data taken as input by the function is varying depending on the precision degree required. Type of data constrains the specific technique that can be used for effectively evaluating function results as well (using, e.g., structure-based, graph-based or attribute-based techniques) (Lin 1998, Rodrýguez & Egenhofer 2003, Blanchard, Kuntz, Harzallah & Briand 2006. Finally, the measure should be meaningful.…”

Section: Cm: Ueml C Ueml C →mentioning

confidence: 99%

The Unified Enterprise Modelling Language—Overview and further work

Anaya

Berio

Harzallah

et al. 2010

Computers in Industry

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Abstract:The Unified Enterprise Modelling Language (UEML) aims at supporting integrated use of enterprise and IS models expressed using different languages. To achieve this aim, UEML offers a hub through which modelling languages can be connected, thereby paving the way for also connecting the models expressed in those languages. This paper motivates and presents the most central parts of the UEML approach: a structured path to describing enterprise and IS modelling constructs; a common ontology to interrelate construct descriptions at the semantic level; a correspondence analysis approach to estimate semantic construct similarity; a quality framework to aid selection of languages; a meta-meta model to integrate the different parts of the approach; and a set of tools to aid its use and evolution. The paper also discusses the benefits of UEML and points to paths for further work.

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Section: Cm: Ueml C Ueml C →mentioning

confidence: 99%

The Unified Enterprise Modelling Language—Overview and further work

Anaya

Berio

Harzallah

et al. 2010

Computers in Industry

Self Cite

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“…Using different degrees of precision may be useful in order to to master complex correspondences and when dealing incomplete representation mappings and/or ontology.The work is in progress on deriving measures of correspondence between pairs of modelling constructs, providing evidence of kinds of correspondence with various degrees of precision. The measures are inspired by measures used to compare objects in the areas of classification theory and knowledge engineering (Lin 1998, Rodrýguez & Egenhofer 2003, Blanchard, Kuntz, Harzallah & Briand 2006.…”

Section: Language and Construct Correspondencesmentioning

confidence: 99%

The Unified Enterprise Modelling Language – Overview and Further Work

Anaya

Berio

Harzallah

et al. 2008

IFAC Proceedings Volumes

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The Unified Enterprise Modelling Language (UEML) aims to support integrated use of enterprise and IS models expressed in a variety of languages. The achieve this aim, UEML provides a hub through which different languages can be connected, thereby paving the way for connecting the models expressed in those languages. UEML offers a structured approach to describing enterprise and IS modelling constructs, a common ontology to interrelate construct descriptions at the semantic level, a correspondence analysis approach to estimate semantic construct similarity, a quality framework to aid selection of languages, a meta-meta model to organise the UEML and a set of tools to aid its use. This paper presents an overview of UEML and points to paths for further work.

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“…Such similarity measures can be based on comparing features that describe the objects, and a semantic similarity measure uses the relationships which exist between the features of the items being compared [ 11 ]. Blanchard et al have established a general model for comparing semantic similarity measures based on a subsumption hierarchy [ 12 ]. They divide tree-based similarities into two categories: those based only on the hierarchical relationships between the terms [ 13 ], and those combining additional statistics such as term frequency in a corpus [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

TopoICSim: a new semantic similarity measure based on gene ontology

Ehsani

Drabløs

2016

BMC Bioinformatics

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BackgroundThe Gene Ontology (GO) is a dynamic, controlled vocabulary that describes the cellular function of genes and proteins according to tree major categories: biological process, molecular function and cellular component. It has become widely used in many bioinformatics applications for annotating genes and measuring their semantic similarity, rather than their sequence similarity. Generally speaking, semantic similarity measures involve the GO tree topology, information content of GO terms, or a combination of both.ResultsHere we present a new semantic similarity measure called TopoICSim (Topological Information Content Similarity) which uses information on the specific paths between GO terms based on the topology of the GO tree, and the distribution of information content along these paths. The TopoICSim algorithm was evaluated on two human benchmark datasets based on KEGG pathways and Pfam domains grouped as clans, using GO terms from either the biological process or molecular function. The performance of the TopoICSim measure compared favorably to five existing methods. Furthermore, the TopoICSim similarity was also tested on gene/protein sets defined by correlated gene expression, using three human datasets, and showed improved performance compared to two previously published similarity measures. Finally we used an online benchmarking resource which evaluates any similarity measure against a set of 11 similarity measures in three tests, using gene/protein sets based on sequence similarity, Pfam domains, and enzyme classifications. The results for TopoICSim showed improved performance relative to most of the measures included in the benchmarking, and in particular a very robust performance throughout the different tests.ConclusionsThe TopoICSim similarity measure provides a competitive method with robust performance for quantification of semantic similarity between genes and proteins based on GO annotations. An R script for TopoICSim is available at http://bigr.medisin.ntnu.no/tools/TopoICSim.R.

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A Tree-Based Similarity for Evaluating Concept Proximities in an Ontology

Cited by 11 publications

References 10 publications

The Unified Enterprise Modelling Language—Overview and further work

The Unified Enterprise Modelling Language—Overview and further work

The Unified Enterprise Modelling Language – Overview and Further Work

TopoICSim: a new semantic similarity measure based on gene ontology

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