연관지식의 효율적인 표현 및 추론이 가능한 지식그래프 기반 지식지도|RISS 상세보기

다국어 초록 (Multilingual Abstract)

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 researchs 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 studys 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 studys artifacts can be applied to implement the user-friendly function of knowledge exploration reflecting users cognitive process toward associated knowledge, and can further underpin the development of an intelligent knowledge-base expanding autonomously through the discovery of

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참고문헌 (Reference)

1 유기동, "지식 간 상호참조적 네비게이션이 가능한 온톨로지 기반 프로세스 중심 지식지도" 한국지능정보시스템학회 18 (18): 61-83, 2012

2 장기태, "계층형 및 네트워크형 지식지도의 사용자 관점 성능 비교" 한국지식경영학회 22 (22): 75-89, 2021

3 Lin, F., "Visualized cognitive knowledge map integration for P2P networks" 46 (46): 774-785, 2009

4 Aasman, J., "Transmuting Information to Knowledge with an Enterprise Knowledge Graph" 19 (19): 44-51, 2017

5 Ehrlinger, L., "Towards a Definition of Knowledge Graphs" 48 (48): 2016

6 Suh, J., "Technology trends and application cases of graph DB" Institute of Information and Communications Technology Planning and Evaluation 2020

7 Fathy, N., "ProGOMap: Automatic Generation of Mappings From Property Graphs to Ontologies" 9 : 113100-113116, 2021

8 Martinez-Rodriguez, J.L., "OpenIE-based approach for Knowledge Graph construction from text" 113 : 339-355, 2018

9 Jiang, Z., "Let Knowledge Make Recommendations for You" 9 : 118194-118204, 2021

10 Yoo, K., "Knowledge flow-based business process redesign: applying a knowledge map to redesign a business process" 11 : 104-125, 2007