• KIPS Transactions on Software and Data Engineering
• /
• v.6 no.2
• /
• pp.103-116
• /
• 2017

In this paper, we present the design and implementation of a large-scale qualitative spatial reasoner, which can derive new qualitative spatial knowledge representing both topological and directional relationships between two arbitrary spatial objects in efficient way using Aparch Spark SQL. Apache Spark SQL is well known as a distributed parallel programming environment which provides both efficient join operations and query processing functions over a variety of data in Hadoop cluster computer systems. In our spatial reasoner, the overall reasoning process is divided into 6 jobs such as knowledge encoding, inverse reasoning, equal reasoning, transitive reasoning, relation refining, knowledge decoding, and then the execution order over the reasoning jobs is determined in consideration of both logical causal relationships and computational efficiency. The knowledge encoding job reduces the size of knowledge base to reason over by transforming the input knowledge of XML/RDF form into one of more precise form. Repeat of the transitive reasoning job and the relation refining job usually consumes most of computational time and storage for the overall reasoning process. In order to improve the jobs, our reasoner finds out the minimal disjunctive relations for qualitative spatial reasoning, and then, based upon them, it not only reduces the composition table to be used for the transitive reasoning job, but also optimizes the relation refining job. Through experiments using a large-scale benchmarking spatial knowledge base, the proposed reasoner showed high performance and scalability.

• Journal of KIISE
• /
• v.43 no.1
• /
• pp.61-70
• /
• 2016

In recent years, there has been a need for techniques for large-scale ontology inference in order to infer new knowledge from existing knowledge at a high speed, and for a diversity of semantic services. With the recent advances in distributed computing, developments of ontology inference engines have mostly been studied based on Hadoop or Spark frameworks on large clusters. Parallel programming techniques using GPGPU, which utilizes many cores when compared with CPU, is also used for ontology inference. In this paper, by combining the advantages of both techniques, we propose a new method for reasoning large RDFS ontology data using a Spark in-memory framework and inferencing distributed data at a high speed using GPGPU. Using GPGPU, ontology reasoning over high-capacity data can be performed as a low cost with higher efficiency over conventional inference methods. In addition, we show that GPGPU can reduce the data workload on each node through the Spark cluster. In order to evaluate our approach, we used LUBM ranging from 10 to 120. Our experimental results showed that our proposed reasoning engine performs 7 times faster than a conventional approach which uses a Spark in-memory inference engine.

• Journal of KIISE
• /
• v.42 no.7
• /
• pp.852-859
• /
• 2015

As the amount of knowledge information significantly increases, a lot of progress has been made in the studies focusing on how to reason large scale ontology effectively at the level of RDFS or OWL. These reasoning methods are divided into TBox classifications and ABox realizations. A TBox classification mainly deals with integrity and dependencies in schema, whereas an ABox realization mainly handles a variety of issues in instances. Therefore, the ABox realization is very important in practical applications. In this paper, we propose a realization method for analyzing the constraint of the specified class, so that the reasoning system automatically infers the classes to which instances belong. Unlike conventional methods that take advantage of the object oriented language based distributed file system, we propose a large scale ontology reasoning method using spark, which is a functional programming-based in-memory system. To verify the effectiveness of the proposed method, we used instances created from the Wine ontology by W3C(120 to 600 million triples). The proposed system processed the largest 600 million triples and generated 951 million triples in 51 minutes (696 K triple / sec) in our largest experiment.

• Journal of KIISE
• /
• v.45 no.3
• /
• pp.224-233
• /
• 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 Science Education
• /
• v.37 no.2
• /
• pp.323-337
• /
• 2013

The purpose of this study was to analyze the scientific reasoning ability during open-inquiry activities of science-gifted 2nd middle school students. Open-inquiry activity is similar to process of scientists' science knowledge generation. Identifying and analyzing the scientific reasoning process and the scientific reasoning ability during open-inquiry activities of science-gifted students, will be able to provide implications for future research. CSRI Matrix(Dolan & Grady, 2010) was used to analyze the complexity of the scientific reasoning ability. The higher degree of complexity of the scientific reasoning is similar to process of scientists' science knowledge generation. The results showed that each process of the open-inquiry activities were distributed by various steps of complexity of the scientific reasoning. Particularly, 'The generating questions' and 'Connecting data to the research question' were 'most complex' step in all teams. On the other side, 'Posing preliminary hypotheses', 'Selecting dependent and independent variables', 'Considering the limitations or flaws of their experiments' were low steps in most teams. And 'Communicating and defending findings' was distributed by most various steps of complexity of the scientific reasoning.

• Journal of KIISE
• /
• v.43 no.10
• /
• pp.1115-1123
• /
• 2016

Assumption-based truth maintenance system (ATMS) is a tool that maintains the reasoning process of inference engine. It also supports non-monotonic reasoning based on dependency-directed backtracking. Bookkeeping all the reasoning processes allows it to quickly check and retract beliefs and efficiently provide solutions for problems with large search space. However, the amount of data has been exponentially grown recently, making it impossible to use a single machine for solving large-scale problems. The maintaining process for solving such problems can lead to high computation cost due to large memory overhead. To overcome this drawback, this paper presents an approach towards incrementally maintaining the reasoning process of inference engine on cluster using Spark. It maintains data dependencies such as assumption, label, environment and justification on a cluster of machines in parallel and efficiently updates changes in a large amount of inferred datasets. We deployed the proposed ATMS on a cluster with 5 machines, conducted OWL/RDFS reasoning over University benchmark data (LUBM) and evaluated our system in terms of its performance and functionalities such as assertion, explanation and retraction. In our experiments, the proposed system performed the operations in a reasonably short period of time for over 80GB inferred LUBM2000 dataset.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.33 no.2
• /
• pp.27-44
• /
• 2008

Owl-based ontology is useful to realize the context-aware services which are composed of the distributed and self-configuring modules. Many ontology-based inference engines are developed to infer useful information from ontology. Since these engines show the uniqueness in terms of speed and information richness, it's difficult to ensure stable operation in providing dynamic context-aware services, especially when they should deal with the complex and big-size ontology. To provide a best inference service, the purpose of this paper is to propose a novel methodology of context-aware engine selection in a contextually prompt manner Case-based reasoning is applied to identify the causality between context and inference engined to be selected. Finally, a series of experiments is performed with a novel evaluation methodology to what extent the methodology works better than competitive methods on an actual context-aware service.

• IE interfaces
• /
• v.16 no.1
• /
• pp.1-7
• /
• 2003

Today's manufacturing enterprise relies heavily on the collaboration and coordination among the distributed manufacturing systems. The effective exchange of the various manufacturing information among the distributed organizations contributes a great deal to their productivity. An Internet-Based Manufacturing Desk is developed for the production of the press dies in the automobile industry in order to solve the manufacturing problems among the distributed organizations, to maintain their smooth and immediate information flow, and to increase their productivity finally. The CBR(Case-Based Reasoning) method is applied to achieve those effects. Using the method, the solutions and methods of the predetermined case-bases are retrieved and applied to solve a newly introduced problem. The developed system is implemented and applied successfully.

• KIPS Transactions on Software and Data Engineering
• /
• v.3 no.10
• /
• pp.397-406
• /
• 2014

In order to answer the questions successfully on behalf of the human in DeepQA environments such as Jeopardy! of the American quiz show, the computer is required to have the capability of fast temporal and spatial reasoning on a large-scale commonsense knowledge base. In this paper, we present a scalable spatial reasoning algorithm for deriving efficiently new directional and topological relations using the MapReduce framework, one of well-known parallel distributed computing environments. The proposed reasoning algorithm assumes as input a large-scale spatial knowledge base including CSD-9 directional relations and RCC-8 topological relations. To infer new directional and topological relations from the given spatial knowledge base, it performs the cross-consistency checks as well as the path-consistency checks on the knowledge base. To maximize the parallelism of reasoning computations according to the principle of the MapReduce framework, we design the algorithm to partition effectively the large knowledge base into smaller ones and distribute them over multiple computing nodes at the map phase. And then, at the reduce phase, the algorithm infers the new knowledge from distributed spatial knowledge bases. Through experiments performed on the sample knowledge base with the MapReduce-based implementation of our algorithm, we proved the high performance of our large-scale spatial reasoner.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2004.05a
• /
• pp.467-470
• /
• 2004

MANPro-based control is a novel control paradigm aimed at intelligent manufacturing systems on the basis of mobile agent-based negotiation process (MANPro). MANPro is a negotiation mechanism based on the agent-based control architecture and, especially, it adapts a mobile agent system called N-agent for the negotiation process. N-agent travels around the network of distributed manufacturing systems to acquire information, and it makes a decision for system control according to the obtained information. MANPro includes communication architecture and information architecture for intelligent shop floor control. MANPro also considers the following issues: (1) negotiation mechanism, (2) single-agent internal strategic policies, and (3) information model. Communication architecture concerns the first issue of the negotiation mechanism. It provides information exchanging mechanism with functional modules. In specific, N-agent is equipped with an intelligent reasoning engine with a built-in knowledge base. This reasoning engine is closely related to the single-agent internal strategic policies of the second issue. Finally, ontology-based information architecture addresses information models and provides a framework for information modeling on negotiation. In this paper, these three issues are addressed in detail and a framework of MANPro-based control is also proposed.