Algebraic Graph Transformations for Merging Ontologies

Mahfoudh, Mariem; Thiry, Laurent; Forestier, Germain; Hassenforder, Michel

doi:10.1007/978-3-319-11587-0_16

Cited by 10 publications

(8 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ontology aligning and merging were studied and implemented in different settings extensively [6,7,9,10,11,18,19,23,24]. However, the aligning and merging operations in the different settings share some algebraic properties.…”

Section: Ontology Merging Systems and Propertiesmentioning

confidence: 99%

“…Ontology operations, e.g., aligning and merging, are studied and implemented extensively in different settings, such as, categorical operations [2,5,6,7,12,13,17,24], relation algebras [9], typed graph grammars [19]. In [24], the concept of ontology alignment was formalized by an ontology V -alignment pair (r 1 : B → O 1 , r 2 : B → O 2 ) with ontology homomorphisms r 1 : B → O 1 and r 2 : B → O 2 while merging two ontologies O 1 and O 2 , for which there is an ontology Valignment pair (r 1 : B → O 1 , r 2 : B → O 2 ), was obtained by the categorical colimit, the pushout of r 1 and r 2 , in the category of the ontologies concerned there.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Merging Ontologies Algebraically

Guo¹,

Berrill²,

Kulkarni³

et al. 2022

Preprint

View full text Add to dashboard Cite

Ontology operations, e.g., aligning and merging, were studied and implemented extensively in different settings, such as, categorical operations, relation algebras, typed graph grammars, with different concerns. However, aligning and merging operations in the settings share some generic properties, e.g., idempotence, commutativity, associativity, and representativity, labeled by (I), (C), (A), (R), respectively, which are defined on an ontology merging system (O, ∼, ), where O is a set of the ontologies concerned, ∼ is a binary relation on O modeling ontology aligning and is a partial binary operation on O modeling ontology merging. Given an ontology repository, a finite set O ⊆ O, its merging closure O is the smallest set of ontologies, which contains the repository and is closed with respect to merging. If (I), (C), (A), and (R) are satisfied, then both O and O are partially ordered naturally by merging, O is finite and can be computed efficiently, including sorting, selecting, and querying some specific elements, e.g., maximal ontologies and minimal ontologies. We also show that the ontology merging system, given by ontology V -alignment pairs and pushouts, satisfies the properties: (I), (C), (A), and (R) so that the merging system is partially ordered and the merging closure of a given repository with respect to pushouts can be computed efficiently.

show abstract

Section: Ontology Merging Systems and Propertiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Merging Ontologies Algebraically

Guo¹,

Berrill²,

Kulkarni³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Ontology merging can be performed with the use of typed graph grammars done using the techniques SPO (simple Push Out) and GROM (Graph Rewriting for Ontologies Merge) [23]. The AGG (Algebraic Graph Grammar) is an application of algebraic outlook to graph transformation.…”

Section: Ontology Mergingmentioning

confidence: 99%

“…For same we have developed two algorithm script and modified an existing tool. We have used Agreement Maker Light (AML) and GROM [23] tools for ontology matching and ontology merging respectively. Problem with both tools is they could do matching and merging of one ontology pair at a time.…”

Section: Introductionmentioning

confidence: 99%

A Known in Advance, What Ontologies to Integrate? For Effective Ontology Merging Using K-means Clustering

Makwana¹,

Ganatra²

2018

IJIES

View full text Add to dashboard Cite

A significant aspect of any system is how we present the knowledge. Ontology is one of the methods to present shared knowledge of the particular domain. Ontology can be designed and developed on specific domain or subdomain by many groups and researchers, which will create heterogeneity. To solve this problem ontology integration is necessary. To integrate shared knowledge, we require calculating the similarity between two ontologies, selected from a corpus using ontology matching techniques. Here we define a procedure to create a group of ontologies. Ontology comparison can be made using tools to find similar classes. As a similarity measure, Jaccard Similarity Index (JSI) is used create a group of ontology by an algorithm named k means. For each cluster, we generate buckets of ontologies, and from the bucket, all ontologies are grouped in one ontology reducing the attempt of exploring in enquiring understanding in multiple ontologies. Here we have check performance of ontology matching; clustering and merging algorithms script using standard benchmarking techniques of agriculture domain and conference track. Also, compare response time performance of SPARQL (SPARQL Protocol and RDF Query Language) query on merged ontology using this method. For experimentation, we have selected ontology corpus from agriculture domain and conference tack domain of OAEI (Ontology Alignment Evaluation Initiative). At the end of experimentation through our proposed approach we could achieve improvement in average loading and match time in ontology matching process compare to an existing tool. Also, we could achieve significance result in ontology merging process though benchmark parameter of coverage, compactness, and average merge time of two ontologies. Finally, in SPARQL query experimentation, we got success in improvement in reducing search space and response time of the query.

show abstract

“…A new approach show for merging ontologies by research group (Mahfoudh et al, 2014) utilizing typed graph grammars. Simple Push Out (SPO) and another ontology merging algorithm Graph Rewriting for Ontologies Merge (GROM) are the techniques utilized as a part of this approach.…”

Section: Ontology Mergingmentioning

confidence: 99%

A Better Approach to Ontology Integration using Clustering Through Global Similarity Measure

Makwana

Ganatra

2018

Journal of Computer Science

View full text Add to dashboard Cite

Knowledge representation is a crucial area of work in the intelligent system, especially in query answering system development. Ontology is used to represent shared knowledge of a particular domain for query answering system. Domain-specific ontology can be designed and developed by many groups and researchers, because of which there is heterogeneity in the knowledgebase. Ontology integration or merging is necessary in order to solve this problem of mixed knowledge. Finding similarity between two ontologies is crucial to achieve integration or merging of ontology. In this study, we present a method to generate a cluster of ontologies using global similarity measure of two ontologies. Ontology matching tools are used to find matched classes between two ontologies. Output of ontology matching tool is mapping between two ontologies and is used for generating clusters of ontology. We use Jaccard Similarity Index as a global similarity measure for clustering. Based on this measure, the popular k-means clustering algorithm is used to perform clustering of ontologies. Bins of ontologies are generated from each cluster. From each bin, all ontologies are finally merged into a single ontology, which helps us in reducing search effort in querying knowledge in query processing. The outcome of this research paper to provide better solution for merging ontology. Here, we use agriculture domain ontology corpus from the standard dataset for experimentation.

show abstract

Algebraic Graph Transformations for Merging Ontologies

Cited by 10 publications

References 18 publications

Merging Ontologies Algebraically

Merging Ontologies Algebraically

A Known in Advance, What Ontologies to Integrate? For Effective Ontology Merging Using K-means Clustering

A Better Approach to Ontology Integration using Clustering Through Global Similarity Measure

Contact Info

Product

Resources

About