Design of ontology mapping framework and improvement of similarity computation

Ontology heterogeneity is the primary obstacle for interoperation of ontologies. Ontology mapping is the best way to solve this problem. The key of ontology mapping is the similarity computation. At present, the method of similarity computation is imperfect. And the computation quantity is high. To solve these problems, an ontology-mapping framework with a kind of hybrid architecture is put forward, with an improvement in the method of similarity computation. Different areas have different local ontologies. Two ontologies are taken as examples, to explain the specific mapping framework and improved method of similarity computation. These two ontologies are about classes and teachers in a university. The experimental results show that using this framework and improved method can increase the accuracy of computation to a certain extent. Otherwise, the quantity of computation can be decreased.

Ontology Mapping Based on Bayesian Network

Ontology mapping is a key interoperability enabler for the semantic web. In this paper,a new ontology mapping approach called ontology mapping based on Bayesian network( OM-BN) is proposed. OM-BN combines the models of ontology and Bayesian Network,and applies the method of Multi-strategy to computing similarity. In OM-BN,the characteristics of ontology,such as tree structure and semantic inclusion relations among concepts,are used during the process of translation from ontology to ontology Bayesian network( OBN). Then the method of Multi-strategy is used to create similarity table( ST) for each concept-node in OBN. Finally,the iterative process of mapping reasoning is used to deduce new mappings from STs,repeatedly.

Utilizing Statistical Semantic Similarity Techniques for Ontology Mapping——with Applications to AEC Standard Models

The objective of this paper is to introduce three semi-automated approaches for ontology mapping using relatedness analysis techniques. In the architecture, engineering, and construction (AEC) industry, there exist a number of ontological standards to describe the semantics of building models. Although the standards share similar scopes of interest, the task of comparing and mapping concepts among standards is challenging due to their differences in terminologies and perspectives. Ontology mapping is therefore necessary to achieve information interoperability, which allows two or more information sources to exchange data and to re-use the data for further purposes. The attribute-based approach, corpus-based approach, and name-based approach presented in this paper adopt the statistical relatedness analysis techniques to discover related concepts from heterogeneous ontologies. A pilot study is conducted on IFC and CIS/2 ontologies to evaluate the approaches. Preliminary results show that the attribute-based approach outperforms the other two approaches in terms of precision and F-measure.

Implementation of Enterprises Interoperation Based on Ontology

The interoperation among enterprises in e-business could block the ambient semantic collaboration and cause a big problem since varying information descriptions and different data models may be used in different enterprises＇ information systems. Ontology is an important tool to overcome the above mentioned syntax and semantic misunderstanding problem. Our goal is to provide a user-friendly environment supporting syntax and neutral format data model for business information. In this paper, two scenarios are discussed and a unified description of data model is developed to solve the gap in interoperation through mapping from logical data of enterprise＇s information system. It provides the methods to realize the mapping among different types of data or information. First, database and other types of information are transformed into neutral format that are described by web ontology language （OWL）. Second, the neutral format can be mapped into the semantic entities and semantic linking through the process of extraction and annotation and added into ontology and then described in a standard format that makes the collaboration be understood easily.

SVM-based Ontology Matching Approach

There are a lot of heterogeneous ontologies in semantic web, and the task of ontology mapping is to find their semantic relationship. There are integrated methods that only simply combine the similarity values which are used in current multi-strategy ontology mapping. The semantic information is not included in them and a lot of manual intervention is also needed, so it leads to that some factual mapping relations are missed. Addressing this issue, the work presented in this paper puts forward an ontology matching approach, which uses multi-strategy mapping technique to carry on similarity iterative computation and explores both linguistic and structural similarity. Our approach takes different similarities into one whole, as a similarity cube. By cutting operation, similarity vectors are obtained, which form the similarity space, and by this way, mapping discovery can be converted into binary classification. Support vector machine (SVM) has good generalization ability and can obtain best compromise between complexity of model and learning capability when solving small samples and the nonlinear problem. Because of the said reason, we employ SVM in our approach. For making full use of the information of ontology, our implementation and experimental results used a common dataset to demonstrate the effectiveness of the mapping approach. It ensures the recall ration while improving the quality of mapping results.

Recognizing Hierarchically Related Biomedical Entities Using MeSH-Based Mapping

Identifying hierarchically related entities is a critical step towards constructing bio-networks in the field of biomedical text mining. To this end, we adopt a mapping-based approach by first mapping bio-entities to terms in an established ontology Medical Subject Headings （MESH）. We then utilize the hierarchical relationships available in MeSH to recognize hierarchically related entities. Specifically, we present two approaches to map biomedical entities identified using the Unified Medical Language System （UMLS） Metathesaurus to MeSH terms. The first approach utilizes a special feature provided by the MetaMap algorithm, whereas the other employs approximate phrase-based match to directly map entities to MeSH terms. These two approaches deliver comparable results with an accuracy of 72% and 75%, respectively, based on two evaluation datasets. A thorough error analysis demonstrates that these two approaches result in only around 10% mutual errors, indicating the complementary nature of these two approaches.