PROPheT – Ontology Population and Semantic Enrichment from Linked Data Sources

Riga, Marina; Mitzias, Panagiotis; Kontopoulos, Efstratios; Kompatsiaris, Ioannis

doi:10.1007/978-3-319-96553-6_12

Cited by 6 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Automatic ontological construction is often based on learning (Maedche and Staab, 2001; Buitelaar et al, 2005). Such approach can be based on unstructured texts (Asim, 2018; Cimiano et al, 2006; Emani et al, 2015; Costa et al, 2016; Dasgupta et al, 2018), informal ontologies (Astrakhantsev and Turdakov, 2013), or linked data (Gavankar et al, 2012; Tiddi et al, 2012; Riga et al, 2017). A particular case of unstructured data corresponds to the labels of ontology identifiers that may be very dense with information.…”

Section: Introductionmentioning

confidence: 99%

Ontological and Non-Ontological Resources for Associating Medical Dictionary for Regulatory Activities Terms to SNOMED Clinical Terms With Semantic Properties

Bousquet¹,

Souvignet²,

Sadou³

et al. 2019

Front. Pharmacol.

View full text Add to dashboard Cite

Background: Formal definitions allow selecting terms (e.g., identifying all terms related to “Infectious disease” using the query “has causative agent organism”) and terminological reasoning (e.g., “hepatitis B” is a “hepatitis” and is an “infectious disease”). However, the standard international terminology Medical Dictionary for Regulatory Activities (MedDRA) used for coding adverse drug reactions in pharmacovigilance databases does not beneficiate from such formal definitions. Our objective was to evaluate the potential of reuse of ontological and non-ontological resources for generating such definitions for MedDRA. Methods: We developed several methods that collectively allow a semiautomatic semantic enrichment of MedDRA: 1) using MedDRA-to-SNOMED Clinical Terms (SNOMED CT) mappings (available in the Unified Medical Language System metathesaurus or other mapping resources, e.g., the MedDRA preferred term “hepatitis B” is associated to the SNOMED CT concept “type B viral hepatitis”) to extract term definitions (e.g., “hepatitis B” is associated with the following properties: has finding site liver structure, has associated morphology inflammation morphology, and has causative agent hepatitis B virus); 2) using MedDRA labels and lexical/syntactic methods for automatic decomposition of complex MedDRA terms (e.g., the MedDRA systems organ class “blood and lymphatic system disorders” is decomposed in blood system disorders and lymphatic system disorders) or automatic suggestions of properties (e.g., the string “cyclic” in preferred term “cyclic neutropenia” leads to the property has clinical course cyclic). Results: The Unified Medical Language System metathesaurus was the main ontological resource reusable for generating formal definitions for MedDRA terms. The non-ontological resources (another mapping resource provided by Nadkarni and Darer in 2010 and MedDRA labels) allowed defining few additional preferred terms. While the Ci4SeR tool helped the curator to define 1,935 terms by suggesting potential supplemental relations based on the parents’ and siblings’ semantic definition, defining manually all MedDRA terms remains expensive in time. Discussion: Several ontological and non-ontological resources are available for associating MedDRA terms to SNOMED CT concepts with semantic properties, but providing manual definitions is still necessary. The ontology of adverse events is a possible alternative but does not cover all MedDRA terms either. Perspectives are to implement more efficient techniques to find more logical relations between SNOMED CT and MedDRA in an automated way.

show abstract

Section: Introductionmentioning

confidence: 99%

Ontological and Non-Ontological Resources for Associating Medical Dictionary for Regulatory Activities Terms to SNOMED Clinical Terms With Semantic Properties

Bousquet¹,

Souvignet²,

Sadou³

et al. 2019

Front. Pharmacol.

View full text Add to dashboard Cite

show abstract

A Semantic Technologies Toolkit for Bridging Early Diagnosis and Treatment in Brain Diseases: Report from the Ongoing EU-Funded Research Project ALAMEDA

Maga-Nteve

Kontopoulos²,

Tsolakis

et al. 2022

Metadata and Semantic Research

View full text Add to dashboard Cite

Multimedia ontology population through semantic analysis and hierarchical deep features extraction techniques

et al. 2022

View full text Add to dashboard Cite

The rapid increase of available data in different complex contexts needs automatic tasks to manage and process contents. Semantic Web technologies represent the silver bullet in the digital Internet ecosystem to allow human and machine cooperation in achieving these goals. Specific technologies as ontologies are standard conceptual representations of this view. It aims to transform data into an interoperability format providing a common vocabulary for a given domain and defining, with different levels of formality, the meaning of informative objects and their possible relationships. In this work, we focus our attention on Ontology Population in the multimedia realm. An automatic and multi-modality framework for images ontology population is proposed and implemented. It allows the enrichment of a multimedia ontology with new informative content. Our multi-modality approach combines textual and visual information through natural language processing techniques, and convolutional neural network used the features extraction task. It is based on a hierarchical methodology using images descriptors and semantic ontology levels. The results evaluation shows the effectiveness of our proposed approach.

show abstract

PROPheT – Ontology Population and Semantic Enrichment from Linked Data Sources

Cited by 6 publications

References 20 publications

Ontological and Non-Ontological Resources for Associating Medical Dictionary for Regulatory Activities Terms to SNOMED Clinical Terms With Semantic Properties

Ontological and Non-Ontological Resources for Associating Medical Dictionary for Regulatory Activities Terms to SNOMED Clinical Terms With Semantic Properties

A Semantic Technologies Toolkit for Bridging Early Diagnosis and Treatment in Brain Diseases: Report from the Ongoing EU-Funded Research Project ALAMEDA

Multimedia ontology population through semantic analysis and hierarchical deep features extraction techniques

Contact Info

Product

Resources

About