• Journal of the Korean Geographical Society
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• v.44 no.3
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• pp.372-394
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• 2009

This study investigates the ontology-based public participation GIS(PPGIS). The major reason that ontology-based GIS has attracted attention in semantic communication in recent year is due to the wide availability of geographical variable and the imminent need for turning such recommendation into useful geographical knowledge. Therefore, this study has been focused on designing and implementing the pilot tested system for public participation GIS. The applicability of the pilot tested was validated through a simulation experiment for history tourism in Guri city Gyeongi-do, Focused on the methodology, the life cycle model which involves regional statues and user recognition, can be viewed as an important preprocessing step(specification, conceptualization, formalization, integration and implementation) for recommended geographical knowledge discovery by axiom. Focusing on practicality, ontology in this study would be recommended for geographical knowledge through reasoning. In addition, ontology-based public participation GIS would show integration epistemological and ontological approach, and be utilized as an index which is connected with semantic communication. The results of the pilot system was applied to the study area, which was a part of scenario. The model was carried out using axiom of logical constraint in the meaning of human-activity.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.485-495
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• 2009

We in this paper propose a social network based on ontology in Korea Institute of Oriental Medicine (KIOM). By using the social network, researchers can find collaborators and share research results with others. For this purpose, first, personal profiles, scholarships, careers, licenses, academic activities, research results, and personal connections for all of researchers in KIOM are collected. After relationship and hierarchy among ontology classes and attributes of classes are defined through analyzing the collected information, a social network ontology are constructed using FOAF and OWL. This ontology can be easily interconnected with other social network by FOAF and provide the reasoning based on OWL ontology.

• Journal of KIISE
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• v.43 no.12
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• pp.1365-1375
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• 2016

One of the most important capabilities for autonomous service robots working in living environments is to recognize and understand the correct context in dynamically changing environment. To generate high-level context knowledge for decision-making from multiple sensory data streams, many technical problems such as multi-modal sensory data fusion, uncertainty handling, symbolic knowledge grounding, time dependency, dynamics, and time-constrained spatio-temporal reasoning should be solved. Considering these problems, this paper proposes an effective dynamic context management and spatio-temporal reasoning method for intelligent service robots. In order to guarantee efficient context management and reasoning, our algorithm was designed to generate low-level context knowledge reactively for every input sensory or perception data, while postponing high-level context knowledge generation until it was demanded by the decision-making module. When high-level context knowledge is demanded, it is derived through backward spatio-temporal reasoning. In experiments with Turtlebot using Kinect visual sensor, the dynamic context management and spatio-temporal reasoning system based on the proposed method showed high performance.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.181-186
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• 2005

In collaborative environment, it is necessary that the participants in collaboration should share the same understanding about the semantics of terms. For example, they should know that 'Part' and 'Item' are different word-expressions for the same meaning. In this paper, we consider sharing between CAD and PDM data. In order to handle such problems in information sharing, an information system needs to automatically recognize that the terms have the same semantics. Serving this purpose, the semantic mapping logic and the ontology based mapper system is described in this paper. In the semantic mapping logic topic, we introduce our logic that consists of four modules: Character Matching, Instance Reasoning, definition comparing and Similarity Checking. In the ontology based mapper, we introduce the system architecture and the mapping procedure.

• 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.

• Journal of KIISE
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• v.45 no.2
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• pp.113-125
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• 2018

Recently, with the advent of the Big Data era, we have gained the capability of acquiring vast amounts of knowledge from various fields. The collected knowledge is expressed by well-formed formula and in particular, OWL, a standard language of ontology, is a typical form of well-formed formula. The symbolic reasoning is actively being studied using large amounts of ontology data for extracting intrinsic information. However, most studies of this reasoning support the restricted rule expression based on Description Logic and they have limited applicability to the real world. Moreover, knowledge management for inaccurate information is required, since knowledge inferred from the wrong information will also generate more incorrect information based on the dependencies between the inference rules. Therefore, this paper suggests that the SWAT, knowledge management system should be combined with the SWRL (Semantic Web Rule Language) reasoning based on ATMS (Assumption-based Truth Maintenance System). Moreover, this system was constructed by combining with SWRL reasoning and ATMS for managing large ontology data based on the distributed In-memory framework. Based on this, the ATMS monitoring system allows users to easily detect and correct wrong knowledge. We used the LUBM (Lehigh University Benchmark) dataset for evaluating the suggested method which is managing the knowledge through the retraction of the wrong SWRL inference data on large data.

• The KIPS Transactions:PartD
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• v.18D no.6
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• pp.423-432
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• 2011

In this paper, we introduce XOnto-Apriori algorithm which is an extension of the Onto-Apriori algorithm. The extended algorithm is designed to improve the conventional algorithm's problem of comparing only identifiers of transaction items by reasoning transaction properties of the items which belong in the same category. We show how the mining algorithm works with a smartphone application recommender system based on our extended algorithm to clearly describe the procedures providing personalized recommendations. Further, our simulation results validate our analysis on the algorithm overhead, precision, and recall.

• Journal of KIISE
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• v.42 no.3
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• pp.295-306
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• 2015

MOnCa2 is a framework for building intelligent smartphone applications based on smartphone sensors and ontology reasoning. In previous studies, MOnCa determined and inferred user situations based on sensor values represented by ontology instances. When this approach is applied, recognizing user space information or objects in user surroundings is possible, whereas determining the user's physical context (travel behavior, travel destination) is impossible. In this paper, MOnCa2 is used to build recognition models for travel behavior and routes using smartphone sensors to analyze the user's physical context, infer basic context regarding the user's travel behavior and routes by adapting these models, and generate high-level context by applying ontology reasoning to the basic context for creating intelligent applications. This paper is focused on approaches that are able to recognize the user's travel behavior using smartphone accelerometers, predict personal routes and destinations using GPS signals, and infer high-level context by applying realization.

• Journal of KIISE
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• v.45 no.3
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• pp.224-233
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• 2018

Recently, there are many of studies on SWRL reasoning engine based on user-defined rules in a distributed environment using a large-scale ontology. Unlike the schema based axiom rules, efficient inference orders cannot be defined in SWRL rules. There is also a large volumet of network shuffled data produced by unnecessary iterative processes. To solve these problems, in this study, we propose a method that uses Map-Reduce algorithm and distributed in-memory framework to deduce multiple rules simultaneously and minimizes the volume data shuffling occurring between distributed machines in the cluster. For the experiment, we use WiseKB ontology composed of 200 million triples and 36 user-defined rules. We found that the proposed reasoner makes inferences in 16 minutes and is 2.7 times faster than previous reasoning systems that used LUBM benchmark dataset.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.3
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• pp.110-117
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• 2012

A product family design has received much attention over the last several decades, since a product family-based development shortens lead-times and reduces cost, as well as increases efficiency and effectiveness of the product realization process. It is challenging work, however, to define the product family design in the heterogeneous product development environments, due to myriads of products related information described in different ways across products in any companies. In this paper, we provided a way of defining product family design framework using formal concept analysis and ontology language. Based on this, the specific product family can be derived by ontological reasoning, and the new product concept can be also expanded in the framework. The proposed framework is formalized using OWL (Web Ontology Language) and implemented in $Prot{\acute{e}}g{\acute{e}}$. Actual product family design algorithm is carried out using FaCT++ engine, a plug-in to $Prot{\acute{e}}g{\acute{e}}$, and the benefits of the proposed method are also demonstrated through a case study.