• Journal of the Korean Society for information Management
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• v.29 no.4
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• pp.123-142
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• 2012

To develop an intelligent search engine to help users retrieve information effectively, various methods, such as Semantic Web, have been used, An effective retrieval method of such methods uses ontology technology. In this paper, we built National R&D ontology after analyzing National R&D Information in NTIS and then implemented National R&D Knowledge Map to represent and retrieve information of the relationship between object and subject (project, human information, organization, research result) in R&D Ontology. In the National R&D Knowledge Map, center-node is the object selected by users, node is subject, subject's sub-node is user's favorite query in National R&D ontology after analyzing the relationship between object and subject. When a user selects sub-node, the system displays the results from inference engine after making query by SPARQL in National R&D ontology.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.834-839
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• 2005

When a great variety of services become available to user through the broadband convergence network in the ubiquitous home environment, an intelligent agent is required to deal with the complexity of services and perceive intension of a user. Different from the old-fashioned command-based user interface for selecting services, conversation enables flexible and rich interactions between human and agents, but diverse expressions of the user's background and context make conversation hard to implement by using either user-initiative or system-initiative methods. To deal with the ambiguity of diverse expressions between user and agents, we have to apply hierarchial bayesian networks for the mixed initiative conversation. Missing information from user's query is analyzed by hierarchial bayesian networks to inference the user's intension so that can be collected through the agent's query. We have implemented this approach in ubiquitous home environment by implementing simulation program.

• Journal of the Korean Society for information Management
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• v.27 no.3
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• pp.227-239
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• 2010

Opinion retrieval is to retrieve items which are relevant to the user information need topically and include opinion about the topic. This paper aims to find a method to represent user information need for effective opinion retrieval and to analyze the combination methods for opinion features through various experiments. The experiments are carried out in the inference network framework using the Blogs06 collection and 100 TREC test topics. The results show that our suggested representation method based on hidden 'opinion' concept is effective, and the compact model with very small opinion lexicon shows the comparable performance to the previous model on the same test data set.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.49-71
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• 2021

Users who intend to utilize knowledge to actively solve given problems proceed their jobs with cross- and sequential exploration of associated knowledge related each other in terms of certain criteria, such as content relevance. A knowledge map is the diagram or taxonomy overviewing status of currently managed knowledge in a knowledge-base, and supports users' knowledge exploration based on certain relationships between knowledge. A knowledge map, therefore, must be expressed in a networked form by linking related knowledge based on certain types of relationships, and should be implemented by deploying proper technologies or tools specialized in defining and inferring them. To meet this end, this study suggests a methodology for developing the knowledge graph-based knowledge map using the Graph DB known to exhibit proper functionality in expressing and inferring relationships between entities and their relationships stored in a knowledge-base. Procedures of the proposed methodology are modeling graph data, creating nodes, properties, relationships, and composing knowledge networks by combining identified links between knowledge. Among various Graph DBs, the Neo4j is used in this study for its high credibility and applicability through wide and various application cases. To examine the validity of the proposed methodology, a knowledge graph-based knowledge map is implemented deploying the Graph DB, and a performance comparison test is performed, by applying previous research's data to check whether this study's knowledge map can yield the same level of performance as the previous one did. Previous research's case is concerned with building a process-based knowledge map using the ontology technology, which identifies links between related knowledge based on the sequences of tasks producing or being activated by knowledge. In other words, since a task not only is activated by knowledge as an input but also produces knowledge as an output, input and output knowledge are linked as a flow by the task. Also since a business process is composed of affiliated tasks to fulfill the purpose of the process, the knowledge networks within a business process can be concluded by the sequences of the tasks composing the process. Therefore, using the Neo4j, considered process, task, and knowledge as well as the relationships among them are defined as nodes and relationships so that knowledge links can be identified based on the sequences of tasks. The resultant knowledge network by aggregating identified knowledge links is the knowledge map equipping functionality as a knowledge graph, and therefore its performance needs to be tested whether it meets the level of previous research's validation results. The performance test examines two aspects, the correctness of knowledge links and the possibility of inferring new types of knowledge: the former is examined using 7 questions, and the latter is checked by extracting two new-typed knowledge. As a result, the knowledge map constructed through the proposed methodology has showed the same level of performance as the previous one, and processed knowledge definition as well as knowledge relationship inference in a more efficient manner. Furthermore, comparing to the previous research's ontology-based approach, this study's Graph DB-based approach has also showed more beneficial functionality in intensively managing only the knowledge of interest, dynamically defining knowledge and relationships by reflecting various meanings from situations to purposes, agilely inferring knowledge and relationships through Cypher-based query, and easily creating a new relationship by aggregating existing ones, etc. This study's artifacts can be applied to implement the user-friendly function of knowledge exploration reflecting user's cognitive process toward associated knowledge, and can further underpin the development of an intelligent knowledge-base expanding autonomously through the discovery of new knowledge and their relationships by inference. This study, moreover than these, has an instant effect on implementing the networked knowledge map essential to satisfying contemporary users eagerly excavating the way to find proper knowledge to use.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.59-68
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• 2022

This paper presents a method for 1:1 verification by comparing the similarity between the given real product image and the drawing image. The proposed method combines two existing CNN-based deep learning models to construct a Siamese Network. After extracting the feature vector of the image through the FC (Fully Connected) Layer of each network and comparing the similarity, if the real product image and the drawing image (front view, left and right side view, top view, etc) are the same product, the similarity is set to 1 for learning and, if it is a different product, the similarity is set to 0. The test (inference) model is a deep learning model that queries the real product image and the drawing image in pairs to determine whether the pair is the same product or not. In the proposed model, through a comparison of the similarity between the real product image and the drawing image, if the similarity is greater than or equal to a threshold value (Threshold: 0.5), it is determined that the product is the same, and if it is less than or equal to, it is determined that the product is a different product. The proposed model showed an accuracy of about 71.8% for a query to a product (positive: positive) with the same drawing as the real product, and an accuracy of about 83.1% for a query to a different product (positive: negative). In the future, we plan to conduct a study to improve the matching accuracy between the real product image and the drawing image by combining the parameter optimization study with the proposed model and adding processes such as data purification.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.90-94
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• 2006

The research about social network for analyzing human relationship has been steadily worked due to the importance in the social science field. It is also important that analyzing the relationship between researchers in the academic and research fields. Especially, the network by joint research or citation between researchers is useful to evaluating projects or making policy on academic and research fields. This paper describes a method that generates two kinds of researcher networks showing co-authorship and citation relationship between researchers based on national R&D reference information ontology. We infer pair of researchers in co-authorship or citation relationship by SPARQL query from the ontology which is composed of research outcomes and their participating researchers in RDF triples. By postprocessing, we construct researcher network which links researchers in co-authorship and citation relationship.

• The KIPS Transactions:PartD
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• v.11D no.6
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• pp.1231-1238
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• 2004

By virtue of the advanced Internet technology, there are lots of research works for e-Logistics which means virtual business activities or service architecture based on the Internet among the logistics companies. Because e-Logistics environment requires more dynamic and global service area, conventional vehicle monitoring and control technologies innate many problems in terms of Integrating, storing and sharing the location data. It needs the development of the moving object technology in order to resolve efficiently the limitations. In this paper, we propose the whole components of the moving object management system which supports the advanced sharing the location information as well as the integration of location data. We are sure the suggested system can be adopted to construct the next generation-logistics vehicle monitoring and control system by reducing the overall cost and time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.5
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• pp.971-978
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• 2008

This paper designs a service-oriented wireless sensor network ontology model which can be used as a knowledge base in future ubiquitous computing. In contrast to legacy approaches, this paper defines the new service classes (ServiceProperty, LocationProperty, and PhysicalProperty), as well as their properties and constraints that enable the service-oriented service based on service items. The service item merging between the proposed model and the legacy ontology was processed using the "equivalentClass" object property of OWL. The Protege 3.3.1 and RACER 1.9.0 inference tools were used for the validation and consistency check of the proposed ontology model, respectively, and the results of service query was applied to the newly defined property in SPARQL language without reference to the properties of legacy ontology.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.61-83
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• 2012

A knowledge map describes the network of related knowledge into the form of a diagram, and therefore underpins the structure of knowledge categorizing and archiving by defining the relationship of the referential navigation between knowledge. The referential navigation between knowledge means the relationship of cross-referencing exhibited when a piece of knowledge is utilized by a user. To understand the contents of the knowledge, a user usually requires additionally information or knowledge related with each other in the relation of cause and effect. This relation can be expanded as the effective connection between knowledge increases, and finally forms the network of knowledge. A network display of knowledge using nodes and links to arrange and to represent the relationship between concepts can provide a more complex knowledge structure than a hierarchical display. Moreover, it can facilitate a user to infer through the links shown on the network. For this reason, building a knowledge map based on the ontology technology has been emphasized to formally as well as objectively describe the knowledge and its relationships. As the necessity to build a knowledge map based on the structure of the ontology has been emphasized, not a few researches have been proposed to fulfill the needs. However, most of those researches to apply the ontology to build the knowledge map just focused on formally expressing knowledge and its relationships with other knowledge to promote the possibility of knowledge reuse. Although many types of knowledge maps based on the structure of the ontology were proposed, no researches have tried to design and implement the referential navigation-enabled knowledge map. This paper addresses a methodology to build the ontology-based knowledge map enabling the referential navigation between knowledge. The ontology-based knowledge map resulted from the proposed methodology can not only express the referential navigation between knowledge but also infer additional relationships among knowledge based on the referential relationships. The most highlighted benefits that can be delivered by applying the ontology technology to the knowledge map include; formal expression about knowledge and its relationships with others, automatic identification of the knowledge network based on the function of self-inference on the referential relationships, and automatic expansion of the knowledge-base designed to categorize and store knowledge according to the network between knowledge. To enable the referential navigation between knowledge included in the knowledge map, and therefore to form the knowledge map in the format of a network, the ontology must describe knowledge according to the relation with the process and task. A process is composed of component tasks, while a task is activated after any required knowledge is inputted. Since the relation of cause and effect between knowledge can be inherently determined by the sequence of tasks, the referential relationship between knowledge can be circuitously implemented if the knowledge is modeled to be one of input or output of each task. To describe the knowledge with respect to related process and task, the Protege-OWL, an editor that enables users to build ontologies for the Semantic Web, is used. An OWL ontology-based knowledge map includes descriptions of classes (process, task, and knowledge), properties (relationships between process and task, task and knowledge), and their instances. Given such an ontology, the OWL formal semantics specifies how to derive its logical consequences, i.e. facts not literally present in the ontology, but entailed by the semantics. Therefore a knowledge network can be automatically formulated based on the defined relationships, and the referential navigation between knowledge is enabled. To verify the validity of the proposed concepts, two real business process-oriented knowledge maps are exemplified: the knowledge map of the process of 'Business Trip Application' and 'Purchase Management'. By applying the 'DL-Query' provided by the Protege-OWL as a plug-in module, the performance of the implemented ontology-based knowledge map has been examined. Two kinds of queries to check whether the knowledge is networked with respect to the referential relations as well as the ontology-based knowledge network can infer further facts that are not literally described were tested. The test results show that not only the referential navigation between knowledge has been correctly realized, but also the additional inference has been accurately performed.

• The Transactions of the Korea Information Processing Society
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• v.4 no.9
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• pp.2280-2288
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• 1997

An agent is a software element that, by making use of knowledge and inference, performs tasks on behalf of the user. In general, an agent has the properties of autonomy, social ability, reactivity, and durability. Many researches on agents are more and more aiming at the multiagent systems since it is not sufficient to let a single agent do the whole things, especially in a real world where tasks require many diverse activities. However, the multiagent frameworks still have some limitations in the processing of user queries that are often ambiguous and goal-oriented. Also, a series of procedures or plans could not be generated from a single query directly. In order to give more intelligence to the multiagent framework, we propose a method of extending the framework with a plan generation module. The open agent architecture (OAA), which is a multiagent framework that we developed, is integrated with UCPOP, which is a AI planner. A travel schedule management agent (TSMA) system is implemented to explore the effects of the method. The extended system enables the user to only specify goal-oriented queries, and the plans and procedures to satisfy these goals are generated automatically. Also, this system provides a cooperative and knowledge-sharing environment that integrates several knowledge-based systems and planning systems that are distributed and used independently.