• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.7-14
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• 2017

Theoretically, it is known that common beliefs and/or common knowledge cannot be attained in asynchronously distributed multiagent environments, however, it show that some propositions with deadlines can be attained as common beliefs among a set of fully trusted agents even when they communicate to each other asynchronously. Generally, in the multiagent environment, the attainment of common beliefs is approached as a problem of communication, and for the common beliefs paradox that the common beliefs is not attained on a system without communication time restriction is applied to loose coarser granularity and it prove that forming common beliefs is possible by relaxing necessary requirements through the KakaoTalk chatting model. I also experimented with the reasoning function that confirms the common beliefs by inquiring about the common belief generated by implementing the inference function in each agent of the KakaoTalk chatting model. Through utilizing metalogic programming, a formalization of the presentation and reasoning of common beliefs has been achieved, and the group chatting model of KakaoTalk was adopted in experiments to show that common beliefs can be formed among distributed agents using asynchronous communication.

• The Journal of Society for e-Business Studies
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• v.18 no.4
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• pp.195-214
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• 2013

Case-based reasoning is a methodology for solving problems more quickly and efficiently by bringing the most similar case of a given problem from past cases and transforming it to fit the current situation. The most important performance indicator of case-based reasoning is the number of cases, so it is difficult to apply the methodology for the area which has not enough cases. In this paper, we proposed a method to exchange cases based on the Semantic Web in order to overcome the problems. Inparticular, we separated cases into sub-cases to make it possible creating new cases by combining the appropriate sub-cases even if there was no proper full case. In order to achieve that, we designed an ontology that connects a case and its sub-cases, represents detailed similarity rules that compare sub-cases, and represents the rules for the combination of sub-cases. Moreover, we designed and implemented a semantic distributed case-based reasoning framework where a case requester can request sub-cases via the Web from case providers and integrates sub-cases into a new case by using the ontology.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.105-114
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• 2006

Context-Awareness is a important technology to support optimized services automatically by recognizing various context informations in ubiquitous environments. Previous middlewares which supports ubiquitous environments used a centralized storage and DBMS to store and manage context informations and service contents. Centralized management of context informations and service contents sometimes hinders the autonomy of moving node and interoperability between difference middlewares. In this paper, we design the systems which stores context informations in moving node by distributed form and shares service contents between middlewares in distributed ubiquitous environments. And it provides Context-Aware scripts to supports the definition of context informations reasoning and execution of services. It verifies the usefulness of the designed systems by applying the scenario of music playing service based on context awareness

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.208-209
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• 2011

In this paper, we introduce a Naive Bayes learning algorithm for learning and reasoning in Map-Reduce model based environment. For this purpose, we use Apache Mahout to execute Distributed Naive Bayes on University of California, Irvine (UCI) benchmark data sets. From the experimental results, we see that Apache Mahout' s Distributed Naive Bayes algorithm is comparable to WEKA' s Naive Bayes algorithm in terms of performance. These results indicates that in the future Big Data environment, Map-Reduce model based systems such as Apache Mahout can be promising for machine learning usage.

• Journal of information and communication convergence engineering
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• v.9 no.3
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• pp.287-294
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• 2011

This paper proposes a search system using the topic maps that it extends XMDR into Knowledge based XMDR for solving of the problems of the heterogeneity of distributed data on a network and integrate data by an efficient way. The proposed system combined Topic Maps and the extended metadata registry effectively. The Topic Maps represent related knowledge and reasoning relationship by associations of topic. And the extended metadata registry standards and manages the metadata of the local systems through registration and certification on the distributed environment. We also proposed a meta layer, include the meta topic and meta association to achieve semantic classification grouping of topics and to define relationship between Topic Maps and extended metadata registry.

• Journal of KIISE
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• v.43 no.4
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• pp.470-479
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• 2016

As the number of various types of media data such as UCC (User Created Contents) increases, research is actively being carried out in many different fields so as to provide meaningful media services. Amidst these studies, a semantic web-based media classification approach has been proposed; however, it encounters some limitations in video classification because of its underlying ontology derived from meta-information such as video tag and title. In this paper, we define recognized objects in a video and activity that is composed of video objects in a shot, and introduce a reasoning approach based on description logic. We define sequential rules for a sequence of shots in a video and describe how to classify it. For processing the large amount of increasing media data, we utilize Spark streaming, and a distributed in-memory big data processing framework, and describe how to classify media data in parallel. To evaluate the efficiency of the proposed approach, we conducted an experiment using a large amount of media ontology extracted from Youtube videos.

• 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 Institute of Electronics Engineers of Korea SD
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• v.44 no.3 s.357
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• pp.12-19
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• 2007

This paper proposes the enhanced auto-focus algorithm detecting several objects and selecting the target object. Proposed algorithm first detects some objects distributed in the image using focus measure operator and multi-window and then selects the target object through fuzzy reasoning with three fuzzy membership functions. Implementation can be simple because it only needs image sensor instead of infrared or ultrasonic equipment. Experimental result shows that the proposed algorithm can improve the quality of image by focusing to the target object.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.834-851
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• 2017

The amount of sources of information available on the web using ontologies as support continues to increase and is often heterogeneous and distributed. Ontology alignment is the solution to ensure semantic interoperability. In this paper, we describe a new ontology alignment approach, which consists of combining structure-based and reasoning-based approaches in order to discover new semantic correspondences between entities of different ontologies. We used the biblio test of the benchmark series and anatomy series of the Ontology Alignment Evaluation Initiative (OAEI) 2012 evaluation campaign to evaluate the performance of our approach. We compared our approach successively with LogMap and YAM++ systems. We also analyzed the contribution of our method compared to structural and semantic methods. The results obtained show that our performance provides good performance. Indeed, these results are better than those of the LogMap system in terms of precision, recall, and F-measure. Our approach has also been proven to be more relevant than YAM++ for certain types of ontologies and significantly improves the structure-based and reasoningbased methods.

• KIISE Transactions on Computing Practices
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• v.21 no.12
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• pp.774-779
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• 2015

In this paper, we present the design and implementation of a large-scale qualitative spatial reasoner using Apache Spark, an in-memory high speed cluster computing environment, which is effective for sequencing and iterating component reasoning jobs. The proposed reasoner can not only check the integrity of a large-scale spatial knowledge base representing topological and directional relationships between spatial objects, but also expand the given knowledge base by deriving new facts in highly efficient ways. In general, qualitative reasoning on topological and directional relationships between spatial objects includes a number of composition operations on every possible pair of disjunctive relations. The proposed reasoner enhances computational efficiency by determining the minimal set of disjunctive relations for spatial reasoning and then reducing the size of the composition table to include only that set. Additionally, in order to improve performance, the proposed reasoner is designed to minimize disk I/Os during distributed reasoning jobs, which are performed on a Hadoop cluster system. In experiments with both artificial and real spatial knowledge bases, the proposed Spark-based spatial reasoner showed higher performance than the existing MapReduce-based one.