• Journal of the Economic Geographical Society of Korea
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• v.19 no.4
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• pp.799-811
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• 2016

After the participatory government began, various cluster policies in explicit and tacit forms had been promoted. However, an opinion of coming up with new policy alternative different from the existing one is recently brought up for strengthening the competitiveness of industrial agglomerations. This research attempts to discuss the ways in which both a smart specialization strategy and a triple-helix innovation system approach, as an alternative approach to regional industrial policy, are theoretically associated with the existing cluster policy. Through this discussion, it highlights that post-cluster policy should be not just based on regional specificity, but also facilitated by establishing the consensus space of innovation on the bassis of voluntary cooperation among industry, academy and government. It also stresses that it is necessary to focus on nurturing a new industry by systematic and intensive investment and the diversification of industrial cluster for reinforcing competitiveness of local universities and revitalizing practical cooperation between industry and university.

• Journal of KIISE:Databases
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• v.33 no.5
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• pp.476-485
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• 2006

The Resource Description Framework(RDF), which emerged with the semantic web, is settling down as a standard for representing information about the resources in the World Wide Web Hence, a lot of research on storing and query processing RDF documents has been done and several RDF storage systems, such as Sesame and Jena, have been developed. But the research on updating RDF documents is still insufficient. When a RDF document is changed, data in the RDF triple store also needs to be updated. However, current RDF triple stores don't support incremental update. So updating can be peformed only by deleting the old version and then storing the new document. This updating method is very inefficient because RDF documents are steadily updated. Furthermore, it makes worse when several RDF documents are stored in the same database. In this paper, we propose an incremental update algorithm on RDF, documents in triple stores. We use a text matching technique for two versions of a RDF document and compensate for the text matching result to find the right target triples to be updated. We show that our approach efficiently update RDF documents through experiments with real-life RDF datasets.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.7
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• pp.1261-1266
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• 2009

In this paper, triple band mobile chip antenna for DCS(1.71${\sim}$1.88GHz) / PCS(1.75${\sim}$1.87GHz) / UPCS(1.8S${\sim}$1.99GHz) on PCB Layout is designed. To analyze the characteristics of the designed antenna, we used commerical simulation tool(HFSS). Triple and wide band characteristic could be realized the measured bandwidth(V.S.W.R<2.0) of the designed antenna operated in 1.71GHz${\sim}$1.99GHz. The size of the designed antenna is about 19mm${\times}$4mm${\times}$1.6mm, narrow bandwidth which is a defect of chip antenna is improved. And its experimental results were a good agreement with simulation performance.

• Journal of KIISE:Databases
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• v.36 no.6
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• pp.422-433
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• 2009

As an effort to secure Semantic Web, in this paper, we introduce an RDF access authorization model based on an ontology hierarchy and an RDF triple pattern. In addition, we apply the authorization model to RDF query validation for approved access authorizations. A subscribed SPARQL or RQL query, which has RDF triple patterns, can be denied or granted according to the corresponding access authorizations which have an RDF triple pattern. In order to efficiently perform the query validation process, we first analyze some primary authorization conflict conditions under RDF subsumption inference, and then we introduce an efficient query validation algorithm using the conflict conditions and Dewey graph labeling technique. Through experiments, we also show that the proposed validation algorithm provides a reasonable validation time and when data and authorizations increase it has scalability.

• Database Research
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• v.34 no.3
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• pp.69-85
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• 2018

Many researches proposed federated query engines to perform query on several homogeneous or heterogeneous datasets simultaneously that significantly improve the quality of query results. The existing techniques allow querying only over a few heterogeneous datasets considering the static binding using the non-standard query. However, we observe that a simultaneous system considering the integration of heterogeneous metadata standards can offer better opportunity to generalize the query over any homogeneous and heterogeneous datasets. In this paper, we propose a transparent federated engine (DRAZ) to query over multiple data sources using SPARQL. In our system, we first develop the ontology for a non-RDF metadata standard based on the metadata kernel dictionary elements, which are standardized by the metadata provider. For a given SPARQL query, we translate any triple pattern into an API call to access the dataset of corresponding non-RDF metadata standard. We convert the results of every API call to N-triples and summarize the final results considering all triple patterns. We evaluated our proposed DRAZ using modified Fedbench benchmark queries over heterogeneous metadata standards, such as DCAT and DOI. We observed that DRAZ can achieve 70 to 100 percent correctness of the results despite the unavailability of the JOIN operations.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.21-30
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• 2009

Semantic web uses ontology languages such as RDF, RDFS, and OWL to define the metadata on the web. There have been many researching efforts in the semantic web technologies based on an agent for extracting triple and relation about concept of ontology. But the extraction of relation and triple about the concept of ontology based on an agent ends up writing a limited query statement as characteristics of an agent. As for this, there is the less of flexibility when extracting triple and relation about the other concept of ontology. We are need a query tool for flexible information retrieval of ontology that is can access the standard ontology and can be used standard query language. In this paper, we propose a SPARQL query tool that is can access the OWL ontology via HTTP protocol and it can be used to make a query. Query result can be output to the soap message. These operations can be support the web service.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.10
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• pp.474-492
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• 2007

In the protein interaction network, there are many meaningful functional modules, each involving several protein interactions to perform discrete functions. Pathways and protein complexes are the examples of the functional modules. In this paper, we propose a new method for detecting the functional modules based on concept. A conceptual functional module, briefly concept module is introduced to match the modules taking them as its instances. It is defined by the corresponding rule composed of triples and operators between the triples. The triples represent conceptual relations reifying the protein interactions of a module, and the operators specify the structure of the module with the relations. Furthermore, users can define a composite concept module by the counterpart rule which, in turn, is defined in terms of the predefined rules. The concept module makes it possible to detect functional modules that are conceptually similar as well as structurally identical to users' queries. The rules are managed in the XML format so that they can be easily applied to other networks of different species. In this paper, we also provide a visualized environment for intuitionally describing complexly structured rules.

• Journal of KIISE
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• v.43 no.1
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• pp.87-95
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• 2016

Recently, due to the development of the Internet and electronic devices, there has been an enormous increase in the amount of available knowledge and information. As this growth has proceeded, studies on large-scale ontological reasoning have been actively carried out. In general, a machine learning program or knowledge engineer measures and provides a degree of confidence for each triple in a large ontology. Yet, the collected ontology data contains specific uncertainty and reasoning such data can cause vagueness in reasoning results. In order to solve the uncertainty issue, we propose an RDFS reasoning approach that utilizes confidence values indicating degrees of uncertainty in the collected data. Unlike conventional reasoning approaches that have not taken into account data uncertainty, by using the in-memory based cluster computing framework Spark, our approach computes confidence values in the data inferred through RDFS-based reasoning by applying methods for uncertainty estimating. As a result, the computed confidence values represent the uncertainty in the inferred data. To evaluate our approach, ontology reasoning was carried out over the LUBM standard benchmark data set with addition arbitrary confidence values to ontology triples. Experimental results indicated that the proposed system is capable of running over the largest data set LUBM3000 in 1179 seconds inferring 350K triples.

• Journal of Contemporary Eastern Asia
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• v.7 no.2
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• pp.17-27
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• 2008

• Journal of Contemporary Eastern Asia
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• v.11 no.2
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• pp.1-12
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• 2012