• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1174-1182
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• 2017

The research and development on Web of Data to realize the opening and sharing of diverse, heterogonous data on the Web has been actively accomplished. As a standard data model for this effort, LOD (Linked Open Data) based on ontology has been proposed. A specialized instance generation system is vital to the development of LOD-based system effectively. This paper implements i-Manager as an appropriate environment for the development of LOD instances, considering the requirements of LOD systems and the practical development environment of the diverse application domains. i-Manager separates the instance layer from the ontology layer by way of LOD Interface Sheet (LIS) and implements the specialized functions requested in LOD instance development, such as instance edit/store, visualization and SPARQL query processing. This paper proposes a new approach for LOD instance development, and i-Manager can be applied for the practical LOD development environment in the diverse application areas.

• Journal of the Korean Society for information Management
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• v.37 no.2
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• pp.145-169
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• 2020

The purpose of this study is to find the cause and solution of the situation where library LOD does not spread after the introduction of library LOD. Research methods include literature research, case studies, and expert interviews. The improvement plan presented in this study is that first, the library needs to avoid the redundancy of the LOD construction target and build the only and specialized data. Second, libraries need to develop LOD services that reflect user needs and implement convenient LOD interfaces. Third, libraries need to establish identification system of data and build a authority file. Fourth, libraries need to recognize the necessity of data opening and linking to librarians and users, and provide opportunities for education and publicity. Fifth, it is necessary to use LOD for integrated search and to establish an integrated platform for search of library LOD. Sixth, libraries need to strengthen cooperation for LOD issuance and utilization, and form a working-level consultative body. Seventh, the government should pursue strong policies with a continuous commitment to LOD promotion and need to continue to provide budget support.

• Journal of Digital Contents Society
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• v.12 no.2
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• pp.185-194
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• 2011

This paper introduces a Statecharts-based progressive behavior LOD model for computer games and virtual reality systems. In order to use computing resources efficiently and generate an LOD model having arbitrary complexity, we defined a progressive behavior LOD model which including a Statecharts-based specification process, refinement operations, a switching policy, and an LOD selection policy. Additionally, in order to show the possibility of the proposed approach, we demonstrate an example of progressive LOD models by illustrating a step-by-step design of a virtual vehicle.

• The Journal of Bigdata
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• v.4 no.2
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• pp.195-205
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• 2019

Linked Open Data (LOD) is rated as the best of any kind of data disclosure, and allows you to search related data by linking them in a standard format across the Internet. There is an increasing number of cases in which relevant data are constructed in the LOD form in the global environment, but in the domestic healthcare sector, the disclosure of data in the form of LOD is still at the beginning stage. In this paper, we introduce a case of LOD platform construction that provides services by linking domestic and international related data by LOD method, based on the data of Korean medical research paper data and health care big data linkage platform. Linking all data from each DB into an LOD requires a lot of time and effort, and is basically an infrastructure task that government or public institutions should be in charge of rather than the private sector. In this study, ten domestic and foreign LOD sites were linked with only a portion of each DB, enabling users to link data from various domestic and foreign organizations in a convenient manner.

• Journal of Digital Contents Society
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• v.14 no.2
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• pp.143-149
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• 2013

Many researchers and developers take a greater interest on the LOD AI techniques as users demand more elaborate and sophisticated game AI in recent years. However, contrary to the traditional geometry LOD, existing LOD AI techniques can be used only to a limited extent. Therefore, in this paper, I propose an LOD AI technique, which uses HSM(Hierarchical State Machine) and the Lua script language as the method to control game objects. Using the proposed approach, we can easily produce multilevel AI models for LOD and design various objects without hard-coding state machines. Moreover, in order to show the effectiveness of the presented technique, this paper exemplifies the results of the efficiency test through the prototype engine.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.171-193
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• 2018

LOD(Linked Open Data) cloud is a practical implementation of semantic web. We suggested a new method that provides identity links conveniently in LOD cloud. It also allows changes in LOD to be reflected to searching results without any omissions. LOD provides detail descriptions of entities to public in RDF triple form. RDF triple is composed of subject, predicates, and objects and presents detail description for an entity. Links in LOD cloud, named identity links, are realized by asserting entities of different RDF triples to be identical. Currently, the identity link is provided with creating a link triple explicitly in which associates its subject and object with source and target entities. Link triples are appended to LOD. With identity links, a knowledge achieves from an LOD can be expanded with different knowledge from different LODs. The goal of LOD cloud is providing opportunity of knowledge expansion to users. Appending link triples to LOD, however, has serious difficulties in discovering identity links between entities one by one notwithstanding the enormous scale of LOD. Newly added entities cannot be reflected to searching results until identity links heading for them are serialized and published to LOD cloud. Instead of creating enormous identity links, we propose LOD to prepare its own link policy. The link policy specifies a set of target LODs to link and constraints necessary to discover identity links to entities on target LODs. On searching, it becomes possible to access newly added entities and reflect them to searching results without any omissions by referencing the link policies. Link policy specifies a set of predicate pairs for discovering identity between associated entities in source and target LODs. For the link policy specification, we have suggested a set of vocabularies that conform to RDFS and OWL. Identity between entities is evaluated in accordance with a similarity of the source and the target entities' objects which have been associated with the predicates' pair in the link policy. We implemented a system "Change Acceptable In-Depth Searching System(CAIDS)". With CAIDS, user's searching request starts from depth_0 LOD, i.e. surface searching. Referencing the link policies of LODs, CAIDS proceeds in-depth searching, next LODs of next depths. To supplement identity links derived from the link policies, CAIDS uses explicit link triples as well. Following the identity links, CAIDS's in-depth searching progresses. Content of an entity obtained from depth_0 LOD expands with the contents of entities of other LODs which have been discovered to be identical to depth_0 LOD entity. Expanding content of depth_0 LOD entity without user's cognition of such other LODs is the implementation of knowledge expansion. It is the goal of LOD cloud. The more identity links in LOD cloud, the wider content expansions in LOD cloud. We have suggested a new way to create identity links abundantly and supply them to LOD cloud. Experiments on CAIDS performed against DBpedia LODs of Korea, France, Italy, Spain, and Portugal. They present that CAIDS provides appropriate expansion ratio and inclusion ratio as long as degree of similarity between source and target objects is 0.8 ~ 0.9. Expansion ratio, for each depth, depicts the ratio of the entities discovered at the depth to the entities of depth_0 LOD. For each depth, inclusion ratio illustrates the ratio of the entities discovered only with explicit links to the entities discovered only with link policies. In cases of similarity degrees with under 0.8, expansion becomes excessive and thus contents become distorted. Similarity degree of 0.8 ~ 0.9 provides appropriate amount of RDF triples searched as well. Experiments have evaluated confidence degree of contents which have been expanded in accordance with in-depth searching. Confidence degree of content is directly coupled with identity ratio of an entity, which means the degree of identity to the entity of depth_0 LOD. Identity ratio of an entity is obtained by multiplying source LOD's confidence and source entity's identity ratio. By tracing the identity links in advance, LOD's confidence is evaluated in accordance with the amount of identity links incoming to the entities in the LOD. While evaluating the identity ratio, concept of identity agreement, which means that multiple identity links head to a common entity, has been considered. With the identity agreement concept, experimental results present that identity ratio decreases as depth deepens, but rebounds as the depth deepens more. For each entity, as the number of identity links increases, identity ratio rebounds early and reaches at 1 finally. We found out that more than 8 identity links for each entity would lead users to give their confidence to the contents expanded. Link policy based in-depth searching method, we proposed, is expected to contribute to abundant identity links provisions to LOD cloud.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.53-60
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• 2021

LOD-based recommender systems usually leverage the data available within LOD datasets, such as DBpedia, in order to recommend items(movies, books, music) to the end users. These systems use a semantic similarity algorithm that calculates the degree of matching between pairs of Linked Data resources. In this paper, we proposed a new approach to measuring semantic distance in an LOD-based recommender system by assigning weights converted from user ratings to links in the LOD graph. The semantic distance measurement model proposed in this paper is based on a processing step in which a graph is personalized to a user through weight calculation and a method of applying these weights to LDSD. The Experimental results showed that the proposed method showed higher accuracy compared to other similar methods, and it contributed to the improvement of similarity by expanding the range of semantic distance measurement of the recommender system. As future work, we aim to analyze the impact on the model using different methods of LOD-based similarity measurement.

• Journal of the Korean Society for information Management
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• v.34 no.1
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• pp.341-371
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• 2017

Recently, the web service environment has changed from document-centered to data-oriented focus, and the Linked Open Data (LOD) exists at the core of the new environment. Specific procedures and methods were examined to build the LOD of records information in accordance with this trend. With the service sustainability of small-scale archive in consideration, an exemplification on LOD building process by utilizing open source software was developed in this paper. To this end, a 5-step service framework for LOD construction was proposed and applied to a collection of diary records from 'Human and Memory Archive'. Proof of Concept (POC) utilizing open source softwares, Protege and Apache Jena Fuseki, was conducted according to the proposed 5 step framework. After establishing the LOD of record information by utilizing the open source software, the connection with external LOD through interlinking and SPARQL search has been successfully performed. In addition, archives' considerations for LOD construction, including improvement on the quality of content information, the role of the archivist, were suggested based on the understanding obtained through the LOD construction process of records information.

• Journal of KIISE
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• v.44 no.10
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• pp.1045-1061
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• 2017

This paper suggests a method to expand linkages in a Linked Open Data(LOD) cloud that is a practical consequence of a semantic web. LOD cloud, contrary to the first expectation, has not been used actively because of the lack of linkages. Current method for establishing links by applying to explicit links and attaching the links to LODs have restrictions on reflecting target LODs' changes in a timely manner and maintaining them periodically. Instead of attaching them, this paper suggests that each LOD should prepare a link policy and publish it together with the LOD. The link policy specifies target LODs, predicate pairs, and similarity degrees to decide on the establishment of links. We have implemented a system that performs in-depth searching through LODs using their link policies. We have published APIs of the system to Github. Results of the experiment on the in-depth searching system with similarity degrees of 1.0 ~ 0.8 and depth level of 4 provides searching results that include 91% ~ 98% of the trustworthy links and about 170% of triples expanded.

• Journal of KIISE:Databases
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• v.41 no.4
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• pp.226-241
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• 2014

LOD(Linked Open Data) is composed of RDF triples which are based on ontologies. They are identified, linked, and accessed under the principles of linked data. Publications of LOD data sets lead to the extension of LOD cloud and ultimately progress to the web of data. However, if ontologically the same things in different LOD data sets are identified by different URIs, it is difficult to figure out their sameness and to provide trustworthy links among them. To solve this problem, we suggest a Trustworthiness Improving Link Evaluation, TILE for short, technique. TILE evaluates links in 4 steps. Step 1 is to consider the inference property of syntactic elements in LOD data set and then generate RDF triples which have existed implicitly. In Step 2, TILE appoints predicates, compares their objects in triples, and then evaluates links between the subjects in the triples. In Step 3, TILE evaluates the predicates' syntactic property at the standpoints of subject description and vocabulary definition and compensates the evaluation results of Step 2. The syntactic elements considered by TILE contain RDFS, OWL, OWL2 which are recommended by W3C. Finally, TILE makes the publisher of LOD data set review the evaluation results and then decide whether to re-evaluate or finalize the links. This leads the publishers' responsibility to be reflected in the trustworthiness of links among the data published.