• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.125-135
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• 2014

In the medical field, for the purpose of diagnosis and treatment of diseases, building knowledge base has received a lot of attention. The most important thing to build a knowledge base is representing the knowledge accurately. In this paper we suggest a knowledge representation method using Ontology technique with the datasets obtained from the domestic papers on Alzheimer disease that has received a lot of attention recently in the medical field. The suggested Ontology for Alzheimer disease defines all the possible classes: lexical information from journals such as 'author' and 'publisher' research subjects extracted from 'title', 'abstract', 'keywords', and 'results'. It also included various semantic relationships between classes through the Ontology properties. Inference can be supported since our Ontology adopts hierarchical tree structure for the classes and transitional characteristics of the properties. Therefore, semantic representation based query is allowed as well as simple keyword query, which enables inference based knowledge query using an Ontology query language 'SPARQL'.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4968-4986
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• 2017

In this paper, we primarily address the difficulty of automatic generation of a plausible depth map from a single image in an unstructured environment. The aim is to extrapolate a depth map with a more correct, rich, and distinct depth order, which is both quantitatively accurate as well as visually pleasing. Our technique, which is fundamentally based on a preexisting DepthTransfer algorithm, transfers depth information at the level of superpixels. This occurs within a framework that replaces a pixel basis with one of instance-based learning. A vital superpixels feature enhancing matching precision is posterior incorporation of predictive semantic labels into the depth extraction procedure. Finally, a modified Cross Bilateral Filter is leveraged to augment the final depth field. For training and evaluation, experiments were conducted using the Make3D Range Image Dataset and vividly demonstrate that this depth estimation method outperforms state-of-the-art methods for the correlation coefficient metric, mean log10 error and root mean squared error, and achieves comparable performance for the average relative error metric in both efficacy and computational efficiency. This approach can be utilized to automatically convert 2D images into stereo for 3D visualization, producing anaglyph images that are visually superior in realism and simultaneously more immersive.

• Journal of KIISE:Databases
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• v.35 no.2
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• pp.132-142
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• 2008

In biological domain, there exist a variety of protein analysis databases which have their own meaning toward the same target of protein. If we integrate these scattered heterogeneous data efficiently, we can obtain useful information which otherwise cannot be found from each original source. Reflecting the characteristics of biological data, each data source has its own syntax and semantics. If we describe these data through RDF(S) models, one of the Semantic Web standards, we can achieve not only syntactic but also semantic integration. In this paper, we propose a new concept of integration layer based on the RDF unified schema. As a conceptual model, we construct a unified schema focusing on the protein information; as a representational model, we propose a technique for the wrappers to aggregate necessary information from the relevant sources and dynamically generate RDF instances. Two example queries show that our integration layer succeeds in processing the integrated requests from users and displaying the appropriate results.

• 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 Digital Convergence
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• v.14 no.11
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• pp.267-273
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• 2016

Creation and Sharing of information which is structured data as well as various unstructured data. makes progress actively through the spread of mobile. Recently, Big Data extracts the semantic information from SNS and data mining is one of the big data technique. Especially, the general emotion analysis that expresses the collective intelligence of the masses is utilized using large and a variety of materials. In this paper, we propose the emotion prediction system architecture which extracts the significant keywords from social network paragraphs using n-gram and Korean morphological analyzer, and predicts the emotion using SVM and these extracted emotion features. The proposed system showed 82.25% more improved recall rate in average than previous systems and it will help extract the semantic keyword using morphological analysis.

• Journal of IKEEE
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• v.7 no.2 s.13
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• pp.144-155
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• 2003

In this paper, a conceptual data model, called the UDM(Unified Data Model), to efficiently represent database structures related with object technology and complex structured data, is proposed. This model integrates major features of modern data models, such as E-R model, Semantic Object Model, and UML, especially from the viewpoint of database design. This model is basically a simplified, but extended version of the Object-Relationship Model, which was proposed to model complex structures of temporal-spatial multimedia data. This model incorporates some of the important semantic and structural information of modern database applications and it is designed to support all of the major logical database models, including relational, object-relational, object-oriented, and (semi-)structured databases. A special diagrammatic technique, called the UDD(Unified Data Diagram), is introduced as a tool for database design. Also, possible ways to derive logical views of data from this unified data model are presented. The proposed model can be utilized as a convenient and practical tool for conceptual database designs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.292-298
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• 2011

In order to acquire new information and biological meaning of the signal data by defining the relationships between them, new modeling technique, ontology, has been proposed. The data of bio-signal can be represented as a systematic and logical to manage continuously bio-signal data using ontology. Furthermore, knowledge of which resources are utilized to provide improved service quality in medical information, health services in various fields. However, relevant studies have not been performed actively to compare importance of relationships between bio-signals. Therefore semantic representation of biometric information should be by defining the relationship between bio-signals. In this paper, we have developed bio-signal ontology to use as a model for using domain knowledge. We verified the usefulness of the ontology by using scenarios.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.151-162
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• 2006

RDF Semantics, one of W3C Recommendations, provides the RDFS entailment rules, which are used for the RDFS inference. Sesame, which is well known RDF repository, supports the RDBMS-based RDFS inference using the forward-chaining strategy. Since inferencing in the forward-chaining strategy is performed in the data loading time, the data loading time in Sesame is slow down be inferencing. In this paper, we propose the order scheme for applying the RDFS entailment rules to improve inference performance. The proposed application order makes the inference process terminate without repetition of the process for most cases and guarantees the completeness of inference result. Also the application order helps to reduce redundant results during the inference by predicting the results which were made already by previously applied rules. In this paper, we show that our approaches can improve the inference performance with comparisons to the original Sesame using several real-life RDF datasets.

• Journal of Internet Computing and Services
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• v.5 no.2
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• pp.97-105
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• 2004

The reason that an intelligent agent cannot understand the representation of its own perception or activity is caused by the traditional syntactic approach that translates a semantic feature into a simulated string, To implement an autonomously learning intelligent agent, Cohen introduces a experimentally semantic approach that the system learns a contentful representation of physical schema from physically interacting with environment using its own sensors and effectors. We propose that negation is a meta-level schema that enables an agent to recognize its own physical schema, To improve the planner's efficiency, Graphplan introduces the control rule that manipulates the inconsistency between planning operators, but it cannot cognitively understand negation and suffers from redundancy problem. By introducing a negative function not, IPP solves the problem, but its approach is still syntactic and is inefficient in terms of time and space. In this paper, we propose that, to represent a negative fact, a positive atom, which is called opposite concept, is a very efficient technique for implementing an cognitive agent, and demonstrate some empirical results supporting the hypothesis.

• 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.