• Title/Summary/Keyword: Distributed SWRL

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SWAT: A Study on the Efficient Integration of SWRL and ATMS based on a Distributed In-Memory System (SWAT: 분산 인-메모리 시스템 기반 SWRL과 ATMS의 효율적 결합 연구)

  • Jeon, Myung-Joong;Lee, Wan-Gon;Jagvaral, Batselem;Park, Hyun-Kyu;Park, Young-Tack
    • 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.

A Study on Distributed Parallel SWRL Inference in an In-Memory-Based Cluster Environment (인메모리 기반의 클러스터 환경에서 분산 병렬 SWRL 추론에 대한 연구)

  • Lee, Wan-Gon;Bae, Seok-Hyun;Park, Young-Tack
    • 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.

Large Scale Incremental Reasoning using SWRL Rules in a Distributed Framework (분산 처리 환경에서 SWRL 규칙을 이용한 대용량 점증적 추론 방법)

  • Lee, Wan-Gon;Bang, Sung-Hyuk;Park, Young-Tack
    • Journal of KIISE
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    • v.44 no.4
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    • pp.383-391
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    • 2017
  • As we enter a new era of Big Data, the amount of semantic data has rapidly increased. In order to derive meaningful information from this large semantic data, studies that utilize the SWRL(Semantic Web Rule Language) are being actively conducted. SWRL rules are based on data extracted from a user's empirical knowledge. However, conventional reasoning systems developed on single machines cannot process large scale data. Similarly, multi-node based reasoning systems have performance degradation problems due to network shuffling. Therefore, this paper overcomes the limitations of existing systems and proposes more efficient distributed inference methods. It also introduces data partitioning strategies to minimize network shuffling. In addition, it describes a method for optimizing the incremental reasoning process through data selection and determining the rule order. In order to evaluate the proposed methods, the experiments were conducted using WiseKB consisting of 200 million triples with 83 user defined rules and the overall reasoning task was completed in 32.7 minutes. Also, the experiment results using LUBM bench datasets showed that our approach could perform reasoning twice as fast as MapReduce based reasoning systems.

An Analysis of Existing Studies on Parallel and Distributed Processing of the Rete Algorithm (Rete 알고리즘의 병렬 및 분산 처리에 관한 기존 연구 분석)

  • Kim, Jaehoon
    • The Journal of Korean Institute of Information Technology
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    • v.17 no.7
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    • pp.31-45
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    • 2019
  • The core technologies for intelligent services today are deep learning, that is neural networks, and parallel and distributed processing technologies such as GPU parallel computing and big data. However, for intelligent services and knowledge sharing services through globally shared ontologies in the future, there is a technology that is better than the neural networks for representing and reasoning knowledge. It is a knowledge representation of IF-THEN in RIF or SWRL, which is the standard rule language of the Semantic Web, and can be inferred efficiently using the rete algorithm. However, when the number of rules processed by the rete algorithm running on a single computer is 100,000, its performance becomes very poor with several tens of minutes, and there is an obvious limitation. Therefore, in this paper, we analyze the past and current studies on parallel and distributed processing of rete algorithm, and examine what aspects should be considered to implement an efficient rete algorithm.

An Unified Context Model for A Context-Aware System Supporting Distributed Processing and Multi-Reasoning (다중추론지원 분산형 상황인식 시스템을 위한 통합 상황모델)

  • Jeong, Jang-Seop;Hong, Seung-Taek;Jang, Dae-Jun;Bang, Dae-Wook
    • Proceedings of the Korea Information Processing Society Conference
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    • 2012.11a
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    • pp.168-171
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    • 2012
  • 본 논문에서는 모바일 컴퓨팅 환경과 불확실성을 지원하는 다중추론지원 분산형 상황인식 시스템의 지식 베이스(KB: Knowledge Base)를 위한 모델로써 상황정보(OWL), 온톨로지 추론정보(OWL DL), 규칙 추론정보(SWRL), 베이지안 추론정보(PR-OWL)를 통합적으로 표현하는 UniOWL 통합상황모델을 제안한다. 제안한 통합상황모델은 상황정보와 다중 추론정보를 단일 구문, 즉 OWL 구문으로 표현하여 지식베이스 설계를 수월하게 하고 표현을 단순화하는 장점이 있다.

Smart Contract's Hierarchical Rules Modularization and Security Mechanism (스마트 컨트랙트의 계층형 규칙 모듈화와 보안 메커니즘)

  • An, Jung Hyun;Na, Sung Hyun;Park, Young B.
    • Journal of the Semiconductor & Display Technology
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    • v.18 no.1
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    • pp.74-78
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    • 2019
  • As software becomes larger and network technology develops, the management of distributed data becomes more popular. Therefore, it is becoming increasingly important to use blockchain technology that can guarantee the integrity of data in various fields by utilizing existing infrastructure. Blockchain is a distributed computing technology that ensures that servers participating in a network maintain and manage data according to specific agreement algorithms and rules to ensure integrity. As smart contracts are applied, not only passwords but also various services to be applied to the code. In order to reinforce existing research on smart contract applied to the blockchain, we proposed a dynamic conditional rule of smart contract that can formalize rules of smart contract by introducing ontology and SWRL and manage rules dynamically in various situations. In the previous research, there is a module that receives the upper rule in the blockchain network, and the rule layer is formed according to this module. However, for every transaction request, it is a lot of resources to check the top rule in a blockchain network, or to provide it to every blockchain network by a reputable organization every time the rule is updated. To solve this problem, we propose to separate the module responsible for the upper rule into an independent server. Since the module responsible for the above rules is separated into servers, the rules underlying the service may be transformed or attacked in the middleware. Therefore, the security mechanism using TLS and PKI is added as an agent in consideration of the security factor. In this way, the benefits of computing resource management and security can be achieved at the same time.