• The Journal of Korean Association of Computer Education
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• v.14 no.3
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• pp.37-49
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• 2011

A user can use resources on the Semantic Web using a Semantic Web browser. In order to utilize the functions of Semantic Web browsers in education, we compared the functions of well-known Semantic Web browsers such as Tabulator, Contextual Search Browser (CSB), Magpie, and Piggy Bank. In order to utilize Semantic Web browsers in education, a user needs to understand the features of each Semantic Web browser and our work can help both teachers and students. Tabulator is an RDF browser that can help to check whether resources can be used for learning and relevance of resources. CSB can be used to search educational resources using a conrtext file that contains the subjects of learning. It can also help learning by showing semantic web resources in the form of triple set as well as by supporting highlighting function. Magpie can help learners without basic knowledge on learning materials by providing interpretation based on a glossary file and related background knowledge. Piggy Bank supports conversion of web resources into semantic web resources and allows to browse semantic web resources in various views as well as to share semantic web resources.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1698-1711
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• 2011

Knowledge is often represented as a set of rules or a semantic network in intelligent systems. Recently, ontology has been widely used to represent semantic knowledge, because it organizes thesaurus and hierarchal information between concepts in a particular domain. However, it is not easy to collect semantic relationships among concepts. Much time and expense are incurred in ontology construction. Collective intelligence can be a good alternative approach to solve these problems. In this paper, we propose a collective intelligence approach of Games With A Purpose (GWAP) to collect various semantic resources, such as words and word-senses. We detail how to construct the semantic information model or ontology from the collected semantic resources, constructing a system named FunWords. FunWords is a Korean lexical-based semantic resource collection tool. Experiments demonstrated the resources were grouped as common nouns, abstract nouns, adjective and neologism. Finally, we analyzed their characteristics, acquiring the semantic relationships noted above. Common nouns, with structural semantic relationships, such as hypernym and hyponym, are highlighted. Abstract nouns, with descriptive and characteristic semantic relationships, such as synonym and antonym are underlined. Adjectives, with such semantic relationships, as description and status, illustration - for example, color and sound - are expressed more. Last, neologism, with the semantic relationships, such as description and characteristics, are emphasized. Weighting the semantic relationships with these characteristics can help reduce time and cost, because it need not consider unnecessary or slightly related factors. This can improve the expressive power, such as readability, concentrating on the weighted characteristics. Our proposal to collect semantic resources from the collective intelligence approach of GWAP (our FunWords) and to weight their semantic relationship can help construct the semantic information model or ontology would be a more effective and expressive alternative.

• Journal of information and communication convergence engineering
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• v.14 no.2
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• pp.97-105
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• 2016

With the increasing demand for interoperability among existing learning resource systems in order to enable the sharing of learning resources, such resources need to be annotated with ontologies that use different metadata standards. These different ontologies must be reconciled through ontology mediation, so as to cope with information heterogeneity problems, such as semantic and structural conflicts. In this paper, we propose an ontology-mapping technique using Semantic Web Rule Language (SWRL) to generate semantic mapping rules that integrate learning resources from different systems and that cope with semantic and structural conflicts. Reasoning rules are defined to support a semantic search for heterogeneous learning resources, which are deduced by rule-based inference. Experimental results demonstrate that the proposed approach enables the integration of learning resources originating from multiple sources and helps users to search across heterogeneous learning resource systems.

• Korean Journal of Computational Design and Engineering
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• v.4 no.3
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• pp.238-246
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• 1999

The modeling method to support product development processes (PDP) must have certain characteristics including the ability to represent multiple viewpoints of the product development and integrate with currently available analysis and design methods based on CE concept. This paper describes the reference model to support multiple viewpoints (PPO: Products, Processes, and Organizations/Resources viewpoints) of the product development processes, from which each model (Products model, Processes model, and Organizations/Resources model) can be extracted, as well as produces PPO data schema. This reference model has associative relationships among the products, processes, and organizations/resources. To allow the extensibility to support design evolution, we propose structured dat representation methods using semantic network, which can be constructed through first-order logic. The product development processes is so represented by specifying entities and semantic relationships among them hat he appropriate information can be accessed and all of the relevant attributes about the entities can be retrieved simultaneously.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.1
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• pp.59-72
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• 2013

In this paper, we propose a keyword query processing technique based on semantic relationships for OWL data. The proposed keyword query processing technique can improve user's search satisfaction by performing two types of associative search. The first associative search uses information inferred by the relationships between classes or properties during keyword query processing. And it supports to search all information resources that are either directly or indirectly related with query keywords by semantic relationships between information resources. The second associative search returns not only information resources related with query keywords but also values of properties of them. We design a storage schema and index structures to support the proposed technique. And we propose evaluation functions to rank retrieved information resources according to three criteria. Finally, we evaluate the validity and accuracy of the proposed technique through experiments. The proposed technique can be utilized in a variety of fields, such as paper retrieval and multimedia retrieval.

• Language and Information
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• v.15 no.2
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• pp.23-45
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• 2011

This paper proposes a statistical-based linguistic methodology for automatic mapping between large-scale heterogeneous languages resources for NLP applications in general. As a particular case, it treats automatic mapping between two large-scale heterogeneous Korean language resources: Sejong Semantic Classes (SJSC) in the Sejong Electronic Dictionary (SJD) and nouns in KorLex. KorLex is a large-scale Korean WordNet, but it lacks syntactic information. SJD contains refined semantic-syntactic information, with semantic labels depending on SJSC, but the list of its entry words is much smaller than that of KorLex. The goal of our study is to build a rich language resource by integrating useful information within SJD into KorLex. In this paper, we use both linguistic and statistical methods for constructing an automatic mapping methodology. The linguistic aspect of the methodology focuses on the following three linguistic clues: monosemy/polysemy of word forms, instances (example words), and semantically related words. The statistical aspect of the methodology uses the three statistical formulae ${\chi}^2$, Mutual Information and Information Gain to obtain candidate synsets. Compared with the performance of manual mapping, the automatic mapping based on our proposed statistical linguistic methods shows good performance rates in terms of correctness, specifically giving recall 0.838, precision 0.718, and F1 0.774.

• Journal of Information Management
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• v.40 no.1
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• pp.133-156
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• 2009

In this study, we link informal Web resources to KISTI NDSL's collections using automatic semantic indexing and tagging to examine the possibility of the service which recommends related documents using the similarity between KISTI's formal information resources and informal web resources. We collect and index Web resources and make automatic semantic linking through STEAK with KISTI's collections for NDSL retrieval. The macro precision which shows retrieval precision per a subject category is 62.6% and the micro precision which shows retrieval precision per a query is 66.9%. The experts' evaluation score is 76.7. This study shows the possibility of semantic linking NDSL retrieval results with Web information resources and expanding information services' coverage to informal information resources.

• Electronics and Telecommunications Trends
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• v.37 no.6
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• pp.74-83
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• 2022

Shannon and Weaver's semantic communication has been actively studied in recent years as a new communication method to provide intelligent mobile services without requiring more capacity, infrastructure, and energy, even with limited radio resources. Considered a breakthrough beyond the Shannon paradigm, semantic communication aims at successfully transmitting semantic information conveyed by a source rather than accurately receiving each symbol or bit, regardless of meaning. Thus, semantic communication can lead to knowledgeable systems that significantly reduce data traffic because the transmitter only transmits the necessary information related to a specific task. This study describes essential differences between existing and semantic communication, research trends related to semantic communication principles and theory, performance metrics of semantic communication, semantic communication system framework, and future research and development issues.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.7
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• pp.718-722
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• 2008

Ontology plays an important role of the Semantic Web to describe meaning and reasoning of resources. Ontology has more rich expressive power through OWL that is a next standard representation language recommended by W3C. As the Semantic Web is widely known, an amount of information resources on the Web is growing rapidly and the related information resources are placed in distributed systems on the Web. So, for providing seamless services without the awareness of far distance, efficient management of the distributed information resources is required. Especially, sear ching fast for local repositories that include data related to user's queries is important to the performance of systems in the distributed environment. In this paper, first, we propose an index structure to search local repositories related to queries in the distributed Semantic Web. Second, we propose a query rewriting strategy to extend given user's query using various expression of OWL. Through the proposed index and query strategy, we can utilize various expressions of OWL and find local repositories related to all query patterns on the Semantic Web.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.93-102
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• 2007

Metadata and ontology can be used to retrieve related information through the inference mure accurately and simply on the Semantic Web. RDF and RDF Schema are general languages for representing metadata and ontology. An enormous number of keywords on the Semantic Web are very important to make practical applications of the Semantic Web because most users prefer to search with keywords. In this paper, we consider a resource as a unit of query results. And we classily queries with keyword conditions into three patterns and propose indexing techniques for keyword-search considering both metadata and ontology. Our index maintains resources that contain keywords indirectly using conceptual relationships between resources as well as resources that contain keywords directly. So, if user wants to search resources that contain a certain keyword, all resources are retrieved using our keyword index. We propose a structure of table for storing RDF Schema information that is labeled using some simple methods.