• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.275-283
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• 2017

A large amount of RDF data has been generated along with the increase of semantic web services. Various distributed storage and query processing schemes have been studied to efficiently use the massive amounts of RDF data. In this paper, we propose a distributed SPARQL query processing scheme that considers the data locality and query execution path of large RDF data. The proposed scheme considers the data locality and query execution path in order to reduce join and communication costs. In a distributed environment, when processing a SPARQL query, it is divided into several sub-queries according to the conditions of the WHERE clause by considering the data locality. The proposed scheme reduces data communication costs by grouping and processing the sub-queries through the index based on associated nodes. In addition, in order to reduce unnecessary joins and latency when processing the query, it creates an efficient query execution path considering data parsing cost, the amount of each node's data communication, and latency. It is shown through various performance evaluations that the proposed scheme outperforms the existing scheme.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.19-33
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• 2013

In this research, a proposed Dynamic Virtual Ontology using Tags (DyVOT) supports dynamic search of resources depending on user's requirements using tags from social web driven resources. It is general that the tags are defined by annotations of a series of described words by social users who usually tags social information resources such as web-page, images, u-tube, videos, etc. Therefore, tags are characterized and mirrored by information resources. Therefore, it is possible for tags as meta-data to match into some resources. Consequently, we can extract semantic relationships between tags owing to the dependency of relationships between tags as representatives of resources. However, to do this, there is limitation because there are allophonic synonym and homonym among tags that are usually marked by a series of words. Thus, research related to folksonomies using tags have been applied to classification of words by semantic-based allophonic synonym. In addition, some research are focusing on clustering and/or classification of resources by semantic-based relationships among tags. In spite of, there also is limitation of these research because these are focusing on semantic-based hyper/hypo relationships or clustering among tags without consideration of conceptual associative relationships between classified or clustered groups. It makes difficulty to effective searching resources depending on user requirements. In this research, the proposed DyVOT uses tags and constructs ontologyfor effective search. We assumed that tags are extracted from user requirements, which are used to construct multi sub-ontology as combinations of tags that are composed of a part of the tags or all. In addition, the proposed DyVOT constructs ontology which is based on hierarchical and associative relationships among tags for effective search of a solution. The ontology is composed of static- and dynamic-ontology. The static-ontology defines semantic-based hierarchical hyper/hypo relationships among tags as in (http://semanticcloud.sandra-siegel.de/) with a tree structure. From the static-ontology, the DyVOT extracts multi sub-ontology using multi sub-tag which are constructed by parts of tags. Finally, sub-ontology are constructed by hierarchy paths which contain the sub-tag. To create dynamic-ontology by the proposed DyVOT, it is necessary to define associative relationships among multi sub-ontology that are extracted from hierarchical relationships of static-ontology. The associative relationship is defined by shared resources between tags which are linked by multi sub-ontology. The association is measured by the degree of shared resources that are allocated into the tags of sub-ontology. If the value of association is larger than threshold value, then associative relationship among tags is newly created. The associative relationships are used to merge and construct new hierarchy the multi sub-ontology. To construct dynamic-ontology, it is essential to defined new class which is linked by two more sub-ontology, which is generated by merged tags which are highly associative by proving using shared resources. Thereby, the class is applied to generate new hierarchy with extracted multi sub-ontology to create a dynamic-ontology. The new class is settle down on the ontology. So, the newly created class needs to be belong to the dynamic-ontology. So, the class used to new hyper/hypo hierarchy relationship between the class and tags which are linked to multi sub-ontology. At last, DyVOT is developed by newly defined associative relationships which are extracted from hierarchical relationships among tags. Resources are matched into the DyVOT which narrows down search boundary and shrinks the search paths. Finally, we can create the DyVOT using the newly defined associative relationships. While static data catalog (Dean and Ghemawat, 2004; 2008) statically searches resources depending on user requirements, the proposed DyVOT dynamically searches resources using multi sub-ontology by parallel processing. In this light, the DyVOT supports improvement of correctness and agility of search and decreasing of search effort by reduction of search path.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.111-123
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• 2013

Semantic similarity/relatedness measure between two concepts plays an important role in research on system integration and database integration. Moreover, current research on keyword recommendation or tag clustering strongly depends on this kind of semantic measure. For this reason, many researchers in various fields including computer science and computational linguistics have tried to improve methods to calculating semantic similarity/relatedness measure. This study of similarity between concepts is meant to discover how a computational process can model the action of a human to determine the relationship between two concepts. Most research on calculating semantic similarity usually uses ready-made reference knowledge such as semantic network and dictionary to measure concept similarity. The topological method is used to calculated relatedness or similarity between concepts based on various forms of a semantic network including a hierarchical taxonomy. This approach assumes that the semantic network reflects the human knowledge well. The nodes in a network represent concepts, and way to measure the conceptual similarity between two nodes are also regarded as ways to determine the conceptual similarity of two words(i.e,. two nodes in a network). Topological method can be categorized as node-based or edge-based, which are also called the information content approach and the conceptual distance approach, respectively. The node-based approach is used to calculate similarity between concepts based on how much information the two concepts share in terms of a semantic network or taxonomy while edge-based approach estimates the distance between the nodes that correspond to the concepts being compared. Both of two approaches have assumed that the semantic network is static. That means topological approach has not considered the change of semantic relation between concepts in semantic network. However, as information communication technologies make advantage in sharing knowledge among people, semantic relation between concepts in semantic network may change. To explain the change in semantic relation, we adopt the cognitive semantics. The basic assumption of cognitive semantics is that humans judge the semantic relation based on their cognition and understanding of concepts. This cognition and understanding is called 'World Knowledge.' World knowledge can be categorized as personal knowledge and cultural knowledge. Personal knowledge means the knowledge from personal experience. Everyone can have different Personal Knowledge of same concept. Cultural Knowledge is the knowledge shared by people who are living in the same culture or using the same language. People in the same culture have common understanding of specific concepts. Cultural knowledge can be the starting point of discussion about the change of semantic relation. If the culture shared by people changes for some reasons, the human's cultural knowledge may also change. Today's society and culture are changing at a past face, and the change of cultural knowledge is not negligible issues in the research on semantic relationship between concepts. In this paper, we propose the future directions of research on semantic similarity. In other words, we discuss that how the research on semantic similarity can reflect the change of semantic relation caused by the change of cultural knowledge. We suggest three direction of future research on semantic similarity. First, the research should include the versioning and update methodology for semantic network. Second, semantic network which is dynamically generated can be used for the calculation of semantic similarity between concepts. If the researcher can develop the methodology to extract the semantic network from given knowledge base in real time, this approach can solve many problems related to the change of semantic relation. Third, the statistical approach based on corpus analysis can be an alternative for the method using semantic network. We believe that these proposed research direction can be the milestone of the research on semantic relation.