An NLP-based ontology population for a risk management generic structure

Makki, Jawad; Alquier, Anne-Marie; Prince, Violaine

doi:10.1145/1456223.1456296

Cited by 6 publications

(3 citation statements)

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“…OntoPrima [15] is a NLP-based Ontology Population system that extracts instances of concepts and instances of relations from text, to populate a given ontology based on NLP techniques for language processing, semantic web techniques (RDFS, RDF, Jena APIs) for knowledge modeling and representation, and on domain expert's intervention to validate extracted instances. This topic is explored in other works such as [6,16,18].…”

Section: Melo Et Al / Archives Metadata Representation On Cidoc-crm and Knowledge Discoverymentioning

confidence: 99%

“…A pipeline of natural language processing tools was integrated in the Gate framework. 16 A set of Jape rules was also used to extract the entities and relations. This work is ongoing but some experiences were already performed, enabling the automatic extraction of some information, and can be confirmed in the dataset example [24].…”

Section: Melo Et Al / Archives Metadata Representation On Cidoc-crm and Knowledge Discoverymentioning

confidence: 99%

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A strategy for archives metadata representation on CIDOC-CRM and knowledge discovery

Melo

Rodrigues

Varagnolo

2023

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This paper presents a strategy for the semantic migration of Portuguese National Archives records into CIDOC-CRM standard, an ontology developed for museums, within the context of the EPISA project. The approach to automatically populate the CIDOC-CRM is based on Mapping Description Rules to semantically translate the archives descriptive information into CIDOC-CRM representation. The compliance of the CIDOC-CRM model recommendations guarantees that the populated CIDOC-CRM ontology of archives descriptive information verifies interoperability, and could be linked and integrated with other populated CIDOC-CRM ontologies. In the information modelling, requirements on the mapping representation, due to the intent of interpreting natural language text to automatically extract information of metadata text fields and to interpret natural language queries, are taken into account. To automatically interpret the Mapping Description Rules, OWL API was used to obtain the set of assertions that represents the information in the target ontology and two datasets are available with some migration examples. The exploration of the knowledge representation is done through some Description Logic queries to highlight the advantages of having this new representation of the National Archives. The evaluation of the resulting representation can be done automatically proving its correctness for the metadata that has a direct representation in CIDOC-CRM.

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Section: Melo Et Al / Archives Metadata Representation On Cidoc-crm and Knowledge Discoverymentioning

confidence: 99%

Section: Melo Et Al / Archives Metadata Representation On Cidoc-crm and Knowledge Discoverymentioning

confidence: 99%

A strategy for archives metadata representation on CIDOC-CRM and knowledge discovery

Melo

Rodrigues

Varagnolo

2023

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“…However, they offer limited customization and flexibility to the user for creating task-specific rules for evaluation, even more so for non-programmers. In regards to semantic evaluation, researchers have traditionally employed domain experts [21], while in this decade, crowdsourced validators [17], [24] are being used for semantic ontology validation. This paper proposes a customizable and scalable framework that evaluates syntactic and semantic aspects of ontology quality using SynEvaluator and SemValidator respectively.…”

Section: Introductionmentioning

confidence: 99%

A framework for syntactic and semantic quality evaluation of ontologies

Iyer¹,

Sanagavarapu²,

Reddy³

2021

Preprint

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The increasing focus on Web 3.0 is leading to automated creation and enrichment of ontologies and other linked datasets. Alongside automation, quality evaluation of enriched ontologies can impact software reliability and reuse. Current quality evaluation approaches oftentimes seek to evaluate ontologies in either syntactic (degree of following ontology development guidelines) or semantic (degree of semantic validity of enriched concepts/relations) aspects. This paper proposes an ontology quality evaluation framework consisting of: (a) SynEvaluator and (b) SemValidator for evaluating syntactic and semantic aspects of ontologies respectively. SynEvaluator allows dynamic task-specific creation and updation of syntactic rules at run-time without any need for programming. SemValidator uses Twitter-based expertise of validators for semantic evaluation. The efficacy and validity of the framework is shown empirically on multiple ontologies.

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