Parallel mining of OWL 2 EL ontology from large linked datasets

Li, Huiying; Sima, Qiang

doi:10.1016/j.knosys.2015.03.023

Cited by 10 publications

(6 citation statements)

References 22 publications

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“…Research papers were proposed for the integration of different knowledge graphs into a unified ontology. In [12], statistical data analysis measures were used to generate ontology axioms from large RDF datasets by running SPARQL queries on them. RDF data were divided into blocks, depending on disjoint properties, to execute the querying process in parallel.…”

Section: B Knowledge Graph-to-ontology Mapping Generationmentioning

confidence: 99%

ProGOMap: Automatic Generation of Mappings From Property Graphs to Ontologies

et al. 2021

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Property Graph databases (PGs) are emerging as efficient graph stores with flexible schemata. This raises the need to have a unified view over heterogenous data produced from these stores. Ontology based Data Access (OBDA) has become the most dominant approach to integrate heterogeneous data sources by providing a unified conceptual view (ontology) over them. The corner stone of any OBDA system is to define mappings between the data source and the target (domain) ontology. However, manual mapping generation is time consuming and requires great efforts. This paper proposes ProGOMap (Property Graph to Ontology Mapper) system that automatically generates mappings from property graphs to a domain ontology. ProGOMap starts by generating a putative ontology with direct axioms from PG. A novel ontology learning algorithm is proposed to enrich the putative ontology with subclass axioms inferred from PG. The putative ontology is then aligned to an existing domain ontology using string similarity metrics. Another algorithm is proposed to align object properties between the two ontologies considering different modelling criteria. Finally, mappings are generated from alignment results. Experiments were done on eight data sets with different scenarios to evaluate the effectiveness of the generated mappings. The experimental results achieved mapping accuracy up to 97% and 81% when addressing PG-to-ontology terminological and structural heterogeneities, respectively. Ontology learning by inferring subclass axioms from a property graph helps to address the heterogeneity between the PG and ontology models.

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Section: B Knowledge Graph-to-ontology Mapping Generationmentioning

confidence: 99%

ProGOMap: Automatic Generation of Mappings From Property Graphs to Ontologies

et al. 2021

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“…Li and Sima [18] proposed an approach to learn an ontology from a Linked Data dataset using SPARQL queries. Tettamanzi et al [34] presented an approach to ontology learning based on measuring possiblity and necessity of an axiom.…”

Section: Related Workmentioning

confidence: 99%

Mining Cardinality Restrictions in OWL

Potoniec

2020

Foundations of Computing and Decision Sciences

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We present an approach to mine cardinality restriction axioms from an existing knowledge graph, in order to extend an ontology describing the graph. We compare frequency estimation with kernel density estimation as approaches to obtain the cardinalities in restrictions. We also propose numerous strategies for filtering obtained axioms in order to make them more available for the ontology engineer. We report the results of experimental evaluation on DBpedia 2016-10 and show that using kernel density estimation to compute the cardinalities in cardinality restrictions yields more robust results that using frequency estimation. We also show that while filtering is of limited usability for minimum cardinality restrictions, it is much more important for maximum cardinality restrictions. The presented findings can be used to extend existing ontology engineering tools in order to support ontology construction and enable more efficient creation of knowledge-intensive artificial intelligence systems.

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“…Otherwise, lines 13 to 19 check if r new can be added to the optional set of properties in the base. If we do not find candidate bases that contain either r 1 or r 2 or the pair cannot be added to a candidate base, we create a new base (lines [23][24][25][26][27]. Each base contains the representative pair, the core properties set, the optional properties set and a score.…”

Section: First Phase: Generation Of Bases Of Mdasmentioning

confidence: 99%

“…In [28] they predict properties for resources based on a statistical dataset analysis, in particular, in co-occurrence of properties. The proposals [35] and [24] infer schema axioms from RDF datasets using mining algorithms. The previous approaches are research examples that make use of statistical machinery to enrich the schema of an RDF dataset rather than to infer potential MD schemas for analysis purposes.…”

Section: Related Workmentioning

confidence: 99%

Statistically-driven generation of multidimensional analytical schemas from linked data

Nebot

Berlanga

2016

Knowledge-Based Systems

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The ever-increasing Linked Data (LD) initiative has given place to open, large amounts of semi-structured and rich data published on the Web. However, effective analytical tools that aid the user in his/her analysis and go beyond browsing and querying are still lacking. To address this issue, we propose the automatic generation of multidimensional analytical stars (MDAS). The success of the multidimensional (MD) model for data analysis has been in great part due to its simplicity. Therefore, in this paper we aim at automatically discovering MD conceptual patterns that summarize LD. These patterns resemble the MD star schema typical of relational data warehousing. The underlying foundations of our method is a statistical framework that takes into account both concept and instance data. We present an implementation that makes use of the statistical framework to generate the MDAS. We have performed several experiments that assess and validate the statistical approach with two well-known and large LD sets.

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Parallel mining of OWL 2 EL ontology from large linked datasets

Cited by 10 publications

References 22 publications

ProGOMap: Automatic Generation of Mappings From Property Graphs to Ontologies

ProGOMap: Automatic Generation of Mappings From Property Graphs to Ontologies

Mining Cardinality Restrictions in OWL

Statistically-driven generation of multidimensional analytical schemas from linked data

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