Using Bayesian decision for ontology mapping

Tang, Jie; Li, Juanzi; Liang, Bin; Huang, Xiaotong; Li, Yang; Wang, Kehong

doi:10.1016/j.websem.2006.06.001

Cited by 157 publications

(89 citation statements)

References 27 publications

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“…In the relevant studies about property similarity, in [10], [11] some properties were taken as common properties between two concepts, the ratio of the common properties to all properties is property similarity, the method didn't consider that non-identical properties may be very similar in the semantics. In the relevant studies about instance similarity, in [12], [13] the ratio of the identical instances to all instances was used as instance similarity, but it did not be considered that the non-identical instances may have the very similar senses. In the relevant studies about structural similarities, the methods in [3], [14]- [16] took the weighted sum of similarities of super-concept, subconcept and sibling-concept as structure similarity, but the weights were given by experts.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, these methods would be unable to effectively deal with the large-scale ontology mapping task. There are also some famous ontology mapping systems, such as GLUE [12], QOM [28], Similarity Flooding [29], PROMPT [30], Falcon-AO [31], RiMOM [13], LILY [32], Cupid [33], and ASMOV [34]. From the aggregation view, though Falcon-AO measures both linguistic comparability and structural comparability of ontologies to estimate the reliability of matched entity pairs, it only uses them to form three heuristic rules to integrate results generated by GMO [35].…”

Section: Related Workmentioning

confidence: 99%

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A Similarity-Based Concepts Mapping Method between Ontologies

Liu

Qin

Gao

et al. 2015

IEICE Trans. Inf. & Syst.

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SUMMARYOntology mapping is important in many areas, such as information integration, semantic web and knowledge management. Thus the effectiveness of ontology mapping needs to be further studied. This paper puts forward a mapping method between different ontology concepts in the same field. Firstly, the algorithms of calculating four individual similarities (the similarities of concept name, property, instance and structure) between two concepts are proposed. The algorithm features of four individual similarities are as follows: a new WordNet-based method is used to compute semantic similarity between concept names; property similarity algorithm is used to form property similarity matrix between concepts, then the matrix will be processed into a numerical similarity; a new vector space model algorithm is proposed to compute the individual similarity of instance; structure parameters are added to structure similarity calculation, structure parameters include the number of properties, instances, sub-concepts, and the hierarchy depth of two concepts. Then similarity of each of ontology concept pairs is represented by a vector. Finally, Support Vector Machine (SVM) is used to accomplish mapping discovery by training and learning the similarity vectors. In this algorithm, Harmony and reliability are used as the weights of the four individual similarities, which increases the accuracy and reliability of the algorithm. Experiments achieve good results and the results show that the proposed method outperforms many other methods of similarity-based algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Similarity-Based Concepts Mapping Method between Ontologies

Liu

Qin

Gao

et al. 2015

IEICE Trans. Inf. & Syst.

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show abstract

“…APFEL [16] uses an average weight function, similar to the first version of MapPso. RIMOM [15] uses risk minimization to search for optimal mappings from the results of multiple strategies. It utilizes a sigmoid function with a set of experimental parameters.…”

Section: Related Workmentioning

confidence: 99%

Structural Weights in Ontology Matching

Keikha¹,

Nematbakhsh²,

Ladani³

2013

IJWEST

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“…RiMOM [20] is a general ontology mapping system based on Bayesian decision theory and the approach divides the process of discovering complex mapping (m:1 mapping) into two steps: mapping entities discovery and mapping expression discovery. We mainly focus on mapping entities discovery.…”

Section: Related Work and Conclusionmentioning

confidence: 99%

A Concept Hierarchy Based Ontology Mapping Approach

Wang

Liu

Bell

2010

Knowledge Science, Engineering and Management

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Abstract. Ontology mapping is one of the most important tasks for ontology interoperability and its main aim is to find semantic relationships between entities (i.e. concept, attribute, and relation) of two ontologies. However, most of the current methods only consider one to one (1:1) mappings. In this paper we propose a new approach (CHM: Concept Hierarchy based Mapping approach) which can find simple (1:1) mappings and complex (m:1 or 1:m) mappings simultaneously. First, we propose a new method to represent the concept names of entities. This method is based on the hierarchical structure of an ontology such that each concept name of entity in the ontology is included in a set. The parent-child relationship in the hierarchical structure of an ontology is then extended as a set-inclusion relationship between the sets for the parent and the child. Second, we compute the similarities between entities based on the new representation of entities in ontologies. Third, after generating the mapping candidates, we select the best mapping result for each source entity. We design a new algorithm based on the Apriori algorithm for selecting the mapping results. Finally, we obtain simple (1:1) and complex (m:1 or 1:m) mappings. Our experimental results and comparisons with related work indicate that utilizing this method in dealing with ontology mapping is a promising way to improve the overall mapping results.

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Using Bayesian decision for ontology mapping

Cited by 157 publications

References 27 publications

A Similarity-Based Concepts Mapping Method between Ontologies

A Similarity-Based Concepts Mapping Method between Ontologies

Structural Weights in Ontology Matching

A Concept Hierarchy Based Ontology Mapping Approach

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