Knowledge systematization for ontology learning methods

Konys, Agnieszka

doi:10.1016/j.procs.2018.07.229

Cited by 42 publications

(29 citation statements)

References 22 publications

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“…Building on their analysis, we have come to grips with linked and open data methods facilitating data access. As a case in point, the researcher in [18] has used such methods to facilitate access to cloud data. Linked and open data are based on a standard SPARQL [19], which offers a uniform approach for accessing massive data.…”

Section: Ontological Engineeringmentioning

confidence: 99%

“…The Table 1 shows in detail the correspondence between knowledge sources (Dataset and ontologies) and ontologies output: [21], whereas other researchers use existing algorithms proposed by the scientific community. In the Table 2, we show the efficiency of some proposed algorithms by researchers who dealt with ontological learning in their scientific proposals: In this stage, researchers [18,21] proposed some ontological learning solutions for integrating the input knowledge sources (i.e., a dataset and an ontology) and the output ontology. For instance, in his work, the author undertook an in-depth study on ontological matching for knowledge extraction.…”

Section: Ontological Engineeringmentioning

confidence: 99%

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Intelligent machine for ontological representation of massive pedagogical knowledge based on neural networks

Hadioui

Touimi

Faddouli

et al. 2021

IJECE

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Higher education is increasingly integrating free learning management systems (LMS). The main objective underlying such systems integration is the automatization of online educational processes for the benefit of all the involved actors who use these systems. The said processes are developed through the integration and implementation of learning scenarios similar to traditional learning systems. LMS produce big data traces emerging from actors’ interactions in online learning. However, we note the absence of instruments adequate for representing knowledge extracted from big traces. In this context, the research at hand is aimed at transforming the big data produced via interactions into big knowledge that can be used in MOOCs by actors falling within a given learning level within a given learning domain, be it formal or informal. In order to achieve such an objective, ontological approaches are taken, namely: mapping, learning and enrichment, in addition to artificial intelligence-based approaches which are relevant in our research context. In this paper, we propose three interconnected algorithms for a better ontological representation of learning actors’ knowledge, while premising heavily on artificial intelligence approaches throughout the stages of this work. For verifying the validity of our contribution, we will implement an experiment about knowledge sources example.

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Section: Ontological Engineeringmentioning

confidence: 99%

Section: Ontological Engineeringmentioning

confidence: 99%

Intelligent machine for ontological representation of massive pedagogical knowledge based on neural networks

Hadioui

Touimi

Faddouli

et al. 2021

IJECE

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show abstract

“…The creation of a new ontology can be necessary when no existing ontology is satisfying the needs of the current data access task. Thereby, the proposed method does not only provide mapping to a database but also falls in the category of ontology learning [7]. However, it is not the main purpose of this approach to create a new ontology.…”

Section: Database Conceptsmentioning

confidence: 99%

SQL Query Construction from Ontology Concept Descriptions

Gorskis

2018

ITMS

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Based on the usage of previously proposed database concepts as mapping point to a database in a domain ontology, the present paper describes the process of constructing SQL queries from them. The proposed database concepts allow for the mapping of domain concept to the source of data from a database. The paper describes the process of traversing the class hierarchy in an ontology for gathering these database concepts and constructing the SQL query. The purpose of the constructed SQL query is to obtain data from a database to populate the ontology with instances related to a selected ontology concept. The described process begins with the selection of one ontology concept, obtaining all directly related concepts, filtering and collecting database concepts, and finally constructing the SQL query.

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“…These methods can go under the name axiom learning and can be considered as one task of ontology learning, which can help alleviate the overall cost of extracting axioms in general. Ontology learning comprises the sets of methods and machine learning techniques referring to the automatic discovery and creation of ontological knowledge from scratch or the enrichment or adaptation of an existing ontology in a semi-automatic fashion using several sources [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

“…http://www.ietf.org/rfc/rfc3987.txt5 https://www.w3.org/RDF/ 6 https://docs.microsoft.com/en-us/windows/desktop/winrm/uri-prefixes 7 https://www.w3.org/TR/rdf-sparql-query/ 8 http://linkeddata.org/ 9 https://lod-cloud.net/ 10 https://wiki.dbpedia.org/…”

mentioning

confidence: 99%

Grammatical Evolution to Mine OWL Disjointness Axioms Involving Complex Concept Expressions

Nguyen

Tettamanzi

2020

2020 IEEE Congress on Evolutionary Computation (CEC)

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Discovering disjointness axioms is a very important task in ontology learning and knowledge base enrichment. To help overcome the knowledge-acquisition bottleneck, we propose a grammar-based genetic programming method for mining OWL class disjointness axioms from the Web of data. The effectiveness of the method is evaluated by sampling a large RDF dataset for training and testing the discovered axioms on the full dataset. First, we applied Grammatical Evolution to discover axioms based on a random sample of DBpedia, a large open knowledge graph consisting of billions of elementary assertions (RDF triples). Then, the discovered axioms are tested for accuracy on the whole DBpedia. We carried out experiments with different parameter settings and analyze output results as well as suggest extensions.

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Knowledge systematization for ontology learning methods

Cited by 42 publications

References 22 publications

Intelligent machine for ontological representation of massive pedagogical knowledge based on neural networks

Intelligent machine for ontological representation of massive pedagogical knowledge based on neural networks

SQL Query Construction from Ontology Concept Descriptions

Grammatical Evolution to Mine OWL Disjointness Axioms Involving Complex Concept Expressions

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