• Title/Summary/Keyword: Knowledge graph

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Design of Cooperation Ontology by using PROLOG and Conceptual Graph (PROLOG와 개념 그래프를 이용한 협동 온톨로지의 설계)

  • Kim, Jin-Seong
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2006.11a
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    • pp.314-317
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    • 2006
  • This study proposes an ontology design framework to support the cooperation among devices by using PROLOG, Conceptual Graph (CG), and Resource Description Framework (RDF). Quite a large number of representation languages for representing ontology on the Web have been established over the last decade. Most of these researches are focused on design of independent resources description. In Semantic Web, however, cooperation ontology will be needed. In this study, the CG could make an entire conceptual view of knowledge and RDF can represent that knowledge. Then the PROLOG could support the natural inference based on that knowledge. Therefore, our proposed ontology will be used in the designing of Semantic Web-based cooperation systems.

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Development of Expert Systems using Automatic Knowledge Acquisition and Composite Knowledge Expression Mechanism

  • Kim, Jin-Sung
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2003.09a
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    • pp.447-450
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    • 2003
  • In this research, we propose an automatic knowledge acquisition and composite knowledge expression mechanism based on machine learning and relational database. Most of traditional approaches to develop a knowledge base and inference engine of expert systems were based on IF-THEN rules, AND-OR graph, Semantic networks, and Frame separately. However, there are some limitations such as automatic knowledge acquisition, complicate knowledge expression, expansibility of knowledge base, speed of inference, and hierarchies among rules. To overcome these limitations, many of researchers tried to develop an automatic knowledge acquisition, composite knowledge expression, and fast inference method. As a result, the adaptability of the expert systems was improved rapidly. Nonetheless, they didn't suggest a hybrid and generalized solution to support the entire process of development of expert systems. Our proposed mechanism has five advantages empirically. First, it could extract the specific domain knowledge from incomplete database based on machine learning algorithm. Second, this mechanism could reduce the number of rules efficiently according to the rule extraction mechanism used in machine learning. Third, our proposed mechanism could expand the knowledge base unlimitedly by using relational database. Fourth, the backward inference engine developed in this study, could manipulate the knowledge base stored in relational database rapidly. Therefore, the speed of inference is faster than traditional text -oriented inference mechanism. Fifth, our composite knowledge expression mechanism could reflect the traditional knowledge expression method such as IF-THEN rules, AND-OR graph, and Relationship matrix simultaneously. To validate the inference ability of our system, a real data set was adopted from a clinical diagnosis classifying the dermatology disease.

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Combining Local and Global Features to Reduce 2-Hop Label Size of Directed Acyclic Graphs

  • Ahn, Jinhyun;Im, Dong-Hyuk
    • Journal of Information Processing Systems
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    • v.16 no.1
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    • pp.201-209
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    • 2020
  • The graph data structure is popular because it can intuitively represent real-world knowledge. Graph databases have attracted attention in academia and industry because they can be used to maintain graph data and allow users to mine knowledge. Mining reachability relationships between two nodes in a graph, termed reachability query processing, is an important functionality of graph databases. Online traversals, such as the breadth-first and depth-first search, are inefficient in processing reachability queries when dealing with large-scale graphs. Labeling schemes have been proposed to overcome these disadvantages. The state-of-the-art is the 2-hop labeling scheme: each node has in and out labels containing reachable node IDs as integers. Unfortunately, existing 2-hop labeling schemes generate huge 2-hop label sizes because they only consider local features, such as degrees. In this paper, we propose a more efficient 2-hop label size reduction approach. We consider the topological sort index, which is a global feature. A linear combination is suggested for utilizing both local and global features. We conduct experiments over real-world and synthetic directed acyclic graph datasets and show that the proposed approach generates smaller labels than existing approaches.

Graph-based Segmentation for Scene Understanding of an Autonomous Vehicle in Urban Environments (무인 자동차의 주변 환경 인식을 위한 도시 환경에서의 그래프 기반 물체 분할 방법)

  • Seo, Bo Gil;Choe, Yungeun;Roh, Hyun Chul;Chung, Myung Jin
    • The Journal of Korea Robotics Society
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    • v.9 no.1
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    • pp.1-10
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    • 2014
  • In recent years, the research of 3D mapping technique in urban environments obtained by mobile robots equipped with multiple sensors for recognizing the robot's surroundings is being studied actively. However, the map generated by simple integration of multiple sensors data only gives spatial information to robots. To get a semantic knowledge to help an autonomous mobile robot from the map, the robot has to convert low-level map representations to higher-level ones containing semantic knowledge of a scene. Given a 3D point cloud of an urban scene, this research proposes a method to recognize the objects effectively using 3D graph model for autonomous mobile robots. The proposed method is decomposed into three steps: sequential range data acquisition, normal vector estimation and incremental graph-based segmentation. This method guarantees the both real-time performance and accuracy of recognizing the objects in real urban environments. Also, it can provide plentiful data for classifying the objects. To evaluate a performance of proposed method, computation time and recognition rate of objects are analyzed. Experimental results show that the proposed method has efficiently in understanding the semantic knowledge of an urban environment.

An Implementation of Spatio-Temporal Graph to Represent Situations in the Virtual World (가상현실 속의 상황 표현을 위한 시공간 그래프의 구현)

  • Park, Jong-Hee;Jung, Gung-Hun
    • The Journal of the Korea Contents Association
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    • v.13 no.6
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    • pp.9-19
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    • 2013
  • In this paper, we develop a Spatio-Temporal graph as of a key component of our knowledge representation Scheme. We design an integrated representation scheme to depict not only present and past but future in parallel with the spaces in an effective and intuitive manner. An event in general occupies not only a space but a time. Hence a crucial premise for the simulation of virtual situations is to position events in the multi-dimensional context, that is, 3-D space extended by the temporal dimension. Furthermore an event tends to have physical, social and mental aspects intertwined. As a result we need diverse information structures and functions to model entities and relations associated with events and to describe situations in different stances or perspectives of the virtual agents. These structures and functions are implemented in terms of integrated and intuitive representation schemes at different levels such as Ontology View, Instance View, ST View, Reality View. The resulting multi-dimensional comprehensive knowledge structure accommodates multi-layered virtual world developing in the time to maximize the diversity of situations in the historical context. The viability of this knowledge representation scheme is demonstrated with a typical scenario applied to a simulator implemented based on the ST Graph. The virtual stage based on the ST graph can be used to provide natural contexts for situated learning or next-generation simulation games.

Design of Knowledge-based Spatial Querying System Using Labeled Property Graph and GraphQL (속성 그래프 및 GraphQL을 활용한 지식기반 공간 쿼리 시스템 설계)

  • Jang, Hanme;Kim, Dong Hyeon;Yu, Kiyun
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.40 no.5
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    • pp.429-437
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    • 2022
  • Recently, the demand for a QA (Question Answering) system for human-machine communication has increased. Among the QA systems, a closed domain QA system that can handle spatial-related questions is called GeoQA. In this study, a new type of graph database, LPG (Labeled Property Graph) was used to overcome the limitations of the RDF (Resource Description Framework) based database, which was mainly used in the GeoQA field. In addition, GraphQL (Graph Query Language), an API-type query language, is introduced to address the fact that the LPG query language is not standardized and the GeoQA system may depend on specific products. In this study, database was built so that answers could be retrieved when spatial-related questions were entered. Each data was obtained from the national spatial information portal and local data open service. The spatial relationships between each spatial objects were calculated in advance and stored in edge form. The user's questions were first converted to GraphQL through FOL (First Order Logic) format and delivered to the database through the GraphQL server. The LPG used in the experiment is Neo4j, the graph database that currently has the highest market share, and some of the built-in functions and QGIS were used for spatial calculations. As a result of building the system, it was confirmed that the user's question could be transformed, processed through the Apollo GraphQL server, and an appropriate answer could be obtained from the database.

Synthesis of the Fault-Causality Graph Model for Fault Diagnosis in Chemical Processes Based On Role-Behavior Modeling (역할-거동 모델링에 기반한 화학공정 이상 진단을 위한 이상-인과 그래프 모델의 합성)

  • 이동언;어수영;윤인섭
    • Journal of Institute of Control, Robotics and Systems
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    • v.10 no.5
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    • pp.450-457
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    • 2004
  • In this research, the automatic synthesis of knowledge models is proposed. which are the basis of the methods using qualitative models adapted widely in fault diagnosis and hazard evaluation of chemical processes. To provide an easy and fast way to construct accurate causal model of the target process, the Role-Behavior modeling method is developed to represent the knowledge of modularized process units. In this modeling method, Fault-Behavior model and Structure-Role model present the relationship of the internal behaviors and faults in the process units and the relationship between process units respectively. Through the multiple modeling techniques, the knowledge is separated into what is independent of process and dependent on process to provide the extensibility and portability in model building, and possibility in the automatic synthesis. By taking advantage of the Role-Behavior Model, an algorithm is proposed to synthesize the plant-wide causal model, Fault-Causality Graph (FCG) from specific Fault-Behavior models of the each unit process, which are derived from generic Fault-Behavior models and Structure-Role model. To validate the proposed modeling method and algorithm, a system for building FCG model is developed on G2, an expert system development tool. Case study such as CSTR with recycle using the developed system showed that the proposed method and algorithm were remarkably effective in synthesizing the causal knowledge models for diagnosis of chemical processes.

An Inclusive Evaluation of Linkage Between Environmental Managerial Accounting and Knowledge Management: Empirical Evidence from Vietnam

  • HUYNH, Quang Linh
    • The Journal of Asian Finance, Economics and Business
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    • v.9 no.7
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    • pp.135-144
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    • 2022
  • The relationship between applying knowledge management and accepting environmentally managed accounting is more complicated than previous studies suggested. Knowledge management is both an antecedent and a consequence of implementing environmentally managed accounting in the workplace. Nonetheless, none of the prior studies have systematically investigated this relationship. The current article attempted to scrutinize the reciprocated multifaceted tie between environmental managerial accounting and knowledge management by utilizing the methods of directed graph searches as well as directed acyclic graphs. The research data was gathered from 342 publicly-listed corporations in Vietnam's key stock markets. The empirical findings disclose that implementing knowledge management can lead to adopting environmental managerial accounting in business, which is, in turn, an antecedent of accepting knowledge management. More importantly, the current research found that the adoption of knowledge management is the first factor to affect the research model. Nonetheless, the usage of knowledge management in business can, in turn, have a positive effect back to the implementing extent of environmental managerial accounting. The findings are beneficial to scientists and particularly to executives by shedding new insight into this reciprocated bond, which can lead executives to make sound decisions regarding knowledge management and environmental managerial accounting for businesses to acquire competitive advantages.

Deep Neural Network-Based Scene Graph Generation for 3D Simulated Indoor Environments (3차원 가상 실내 환경을 위한 심층 신경망 기반의 장면 그래프 생성)

  • Shin, Donghyeop;Kim, Incheol
    • KIPS Transactions on Software and Data Engineering
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    • v.8 no.5
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    • pp.205-212
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    • 2019
  • Scene graph is a kind of knowledge graph that represents both objects and their relationships found in a image. This paper proposes a 3D scene graph generation model for three-dimensional indoor environments. An 3D scene graph includes not only object types, their positions and attributes, but also three-dimensional spatial relationships between them, An 3D scene graph can be viewed as a prior knowledge base describing the given environment within that the agent will be deployed later. Therefore, 3D scene graphs can be used in many useful applications, such as visual question answering (VQA) and service robots. This proposed 3D scene graph generation model consists of four sub-networks: object detection network (ObjNet), attribute prediction network (AttNet), transfer network (TransNet), relationship prediction network (RelNet). Conducting several experiments with 3D simulated indoor environments provided by AI2-THOR, we confirmed that the proposed model shows high performance.

-An Implementation of a Graph-based Modeling System using Influence Diagram- (영향도를 이용한 그래프 기반 모델링 시스템의 응용)

  • 박동진;황인극
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.23 no.55
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    • pp.85-96
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    • 2000
  • This paper describes IDMS, a graph-based modeling system that supports problem structuring. We employs influence diagram as a problem representation tool, that is, a modeling tool. In particular, IDMS is designed as domain-independent shell. Therefore, a modeler can change the contents of the knowledge base to suit his/her own interested domain. Since the knowledge base of IDMS contains both modeling knowledge and domain knowledge, IDMS provides not only the syntactic support for modeling tool, but also the semantic support for problem domain. To apply the method in the real world context, we tested IDMS on the process selection problem in business reengineering, which is typical semi-structured problem.

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