Generic ontology matching method The similarity computation is based on the semantic subgraphs, which means all the information used in the simi- larity computation comes from

Generic ontology matching method The similarity computation is based on the semantic subgraphs, which means all the information used in the simi- larity computation comes from

In The 4th International Workshop on Ontology Matching, Washington Dc., USA (2009) [2] Peng Wang, Baowen Xu: Lily: Ontology Alignment Results for OAEI 2008.

In The 4th International Workshop on Ontology Matching, Washington Dc., USA (2009) [2] Peng Wang, Baowen Xu: Lily: Ontology Alignment Results for OAEI 2008.

Generic ontology matching method The similarity computation is based on the semantic subgraphs, which means all the information used in the simi- larity computation comes from

To solve the matching problem without rich literal information, a similarity propagation matcher with strong propagation condition (SSP matcher) is presented, and the

To solve the matching problem without rich literal information, a similarity propagation matcher with strong propagation condition (SSP matcher) is presented, and the

Lily has three main matching functions: (1) Generic Ontology Matching (GOM) is used for common matching tasks with normal size ontologies.. (2) Large scale

This consequence of quasiregularity is usually proved via the funda- mental fact that quasiregular mappings are free extremals for certain variational integrals;

(2) The similarity propagation strategy could compensate for the linguistic matching methods, and it can produce more alignments when ontologies lack of linguistic

Creemos que el hecho de presentar de esta manera a los grandes nombres literarios podría hacer que los alumnos se interesaran por ellos y que por sí mismos se acercaran a sus obras,

During our first participation in the OAEI cam- paign, POMap succeeded to be one of the top three performing systems in the Anatomy track.. In the remaining of this paper, we

Extensional matcher In many situation, to-be-matched ontologies provide data (in- stances), therefore, the aim of Extensional module is to discover new mappings which are complement

Especially in the Anatomy and Large Biomedical Ontologies tracks GOMMA’s techniques such as composition- based matching, parallel matching and blocking showed to be valuable for

In test 202, although the identifiers of the entities were replaced by random strings and their labels and comments suppressed, ASMOV was still able to leverage other

Zhishi.links we proposed here is an efficient and flexible instance matching system, which utilizes distributed framework to index and process semantic resources, and uses

The similar neighbors filter uses the instance alignment (generated by the previous string processing step) to count for each instance correspondence how many resources or literals

The interactions among different textual information are taken into considera- tion, which allows instances to be fine-grained divided. It also has the ability to infer indexes