Mapping adaptation actions for the automatic reconciliation of dynamic ontologies

Reis, Júlio Cesar dos; Dinh, Duy; Pruski, Cédric; Silveira, Marcos Da; Reynaud-Delaître, Chantal

doi:10.1145/2505515.2505564

Cited by 13 publications

(18 citation statements)

References 15 publications

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“…Ontology evolution approaches (Hartung et al 2011, Dos Reis et al 2013 allow for the study of changes in ontologies and using the change management mechanisms to detect candidate missing relations. An approach that allows for detection and user-guided completion of the is-a structure is given in (Ivanova & Lambrix 2013, Lambrix et al 2015 where completion is formalized as an abduction problem and the RepOSE tool is presented.…”

Section: Extending Ontologies From Unstructured Textmentioning

confidence: 99%

A Method for Extending Ontologies with Application to the Materials Science Domain

Armiento

Lambrix

2019

Data Science Journal

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In the materials science domain the data-driven science paradigm has become the focus since the beginning of the 2000s. A large number of research groups and communities are building and developing data-driven workflows. However, much of the data and knowledge is stored in different heterogeneous data sources maintained by different groups. This leads to a reduced availability of the data and poor interoperability between systems in this domain. Ontologybased techniques are an important way to reduce these problems and a number of efforts have started. In this paper we investigate efforts in the materials science, and in particular in the nanotechnology domain, and show how such ontologies developed by domain experts, can be improved. We use a phrase-based topic model approach and formal topical concept analysis on unstructured text in this domain to suggest additional concepts and axioms for the ontology that should be validated by a domain expert. We describe the techniques and show the usefulness of the approach through an experiment where we extend two nanotechnology ontologies using approximately 600 titles and abstracts.

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Section: Extending Ontologies From Unstructured Textmentioning

confidence: 99%

A Method for Extending Ontologies with Application to the Materials Science Domain

Armiento

Lambrix

2019

Data Science Journal

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“…A recent review of current methods for biomedical ontology mapping highlights the importance in developing semi-automatic methods to aid in ontology evolution efforts and reiterates the aforementioned concept of semantic correspondence in terms of scoping between terms [20]. Methods incorporating such correspondences have been published elsewhere, but these deal with issues of ontology evolution and merging, and not with categorizing terms into user-defined subsets [21,22]. Ontology merging also continues to be an active area of development for integrating functional, locational, and phenotypic information.…”

Section: Maintenance Of Ontologiesmentioning

confidence: 99%

GOcats: A tool for categorizing Gene Ontology into subgraphs of user-defined concepts

Hinderer

Flight

Moseley

2018

Preprint

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results)relations are included in the analysis. Therefore, we developed a custom graph parsing scheme that allows these relations to be incorporated without resulting in erroneous term categorizations. This enables a more robust semantic scoping utilization of Gene Ontology, facilitating summarization of annotations in large data sets. We also demonstrate that GOcats is well suited for comparing results from separate annotation sources due to its ability to allow adjustment of categories to the appropriate annotation term granularity. GOcats thus facilitates more robust interpretation and comparison of experimental and knowledgebase annotation sources and provides new tools for semantic scoping utilization and development of Gene Ontology. Furthermore, when used alongside annotation enrichment tools such as categoryCompare2, GOcats' unique method for inferring category membership results in both improved enrichment statistics and the identification of enriched terms otherwise impossible-to-identify with statistical significance when compared to using conventional ontology inference rules. 8 has_part describe part-whole (mereological) correspondence. Therefore, we consider scoping relations to be comprised of is_a, part_of, and has_part, and mereological relations to be comprised of part_of and has_part.There are three versions of the GO database, each containing aspects of the CV with varying complexity: go-basic is filtered to exclude relations that span across multiple sub-ontologies and to include only relations that point toward the root of the ontology; go or go-core contains additional relations, such as has_part that may span sub-ontologies and which point both toward and away from the root of the ontology; and go-plus contains yet more relations in addition to cross-references to entries in external databases like the Chemical Entities of Biological Interest (ChEBI) ontology (Munoz-Torres & Carbon, 2017). The first and second versions are available in the Open Biomedical Ontology (OBO) flat text file formatting, while the third is available only in the Web Ontology Language (OWL) RDF/XML format. Categorization-relevant issues in GOOntological graphs are typically designed as directed graphs, meaning that every edge has directionality or directed acyclic graphs (DAGs), meaning that no path exists that leads back to a node already visited if one were to traverse the graph stepwise.This allows the graph to form a complex semantic model of biology containing both general concepts and more-specific (fine-grained) concepts. The "parent-child" relation hierarchy allows biological entities to be annotated at any level of specificity (granularity) with a single term code, as fine-grained terms intrinsically capture the meaning of every one of its parent and ancestor terms through the linking of relation-defining is_a edges in the graph. However, it is deceptively non-trivial to reverse the logic and organize 1 0 Author Contributions E.W.H. worked on the design of GOcats, implemented GOcats, performed all analyses,...

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“…Dos Reis et al [55] 5 propose a similar approach than the diff-based scheme of [28] using so-called mapping adaptation actions (MAAs) to keep mappings up-to-date for different ontology changes. Ontology changes are computed using the COnto-Diff algorithm [27] and further categorized into revision, deletion and addition of ontology elements ( C , A , R ).…”

Section: Adaptation Of Ontology-based Mappingsmentioning

confidence: 99%

Evolution of biomedical ontologies and mappings: Overview of recent approaches

Groß

Pruski

Rahm

2016

Computational and Structural Biotechnology Journal

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Biomedical ontologies are heavily used to annotate data, and different ontologies are often interlinked by ontology mappings. These ontology-based mappings and annotations are used in many applications and analysis tasks. Since biomedical ontologies are continuously updated dependent artifacts can become outdated and need to undergo evolution as well. Hence there is a need for largely automated approaches to keep ontology-based mappings up-to-date in the presence of evolving ontologies. In this article, we survey current approaches and novel directions in the context of ontology and mapping evolution. We will discuss requirements for mapping adaptation and provide a comprehensive overview on existing approaches. We will further identify open challenges and outline ideas for future developments.

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Mapping adaptation actions for the automatic reconciliation of dynamic ontologies

Cited by 13 publications

References 15 publications

A Method for Extending Ontologies with Application to the Materials Science Domain

A Method for Extending Ontologies with Application to the Materials Science Domain

GOcats: A tool for categorizing Gene Ontology into subgraphs of user-defined concepts

Evolution of biomedical ontologies and mappings: Overview of recent approaches

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