• Journal of KIISE:Software and Applications
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• v.33 no.3
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• pp.356-364
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• 2006

'Ubiquitous Computing' is the most important paradigm of the next generation Information-Communication technology. The one of important problems to develop ubiquitous computing service system get hold of relations between times of transfer objects and events of transfer objects. Another problem is what reason transfer-pattern through location data of transfer objects. In this paper, we propose an approach to offer temporal-relation service in ubiquitous computing environment. The first is temporal reasoning in service viewpoint. The second is temporal reasoning to record user's preference. Users have preferences that are closely connected with time. These preferences are recorded at user profile. Therefore, the user profile-based ubiquitous service system can offer suitable service to users.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.5
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• pp.231-242
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• 2016

In order to meet rapid changes of Web information, it is necessary to extend the current Web technologies to represent both the valid time and location of each fact and knowledge, and reason their relationships. Until recently, many researches on qualitative temporal reasoning have been conducted in laboratory-scale, dealing with small knowledge bases. However, in this paper, we propose the design and implementation of a parallel qualitative temporal reasoner, MRQUTER, which can make reasoning over Web-scale large knowledge bases. This parallel temporal reasoner was built on a Hadoop cluster system using the MapReduce parallel programming framework. It decomposes the entire qualitative temporal reasoning process into several MapReduce jobs such as the encoding and decoding job, the inverse and equal reasoning job, the transitive reasoning job, the refining job, and applies some optimization techniques into each component reasoning job implemented with a pair of Map and Reduce functions. Through experiments using large benchmarking temporal knowledge bases, MRQUTER shows high reasoning performance and scalability.

• Journal of Korean Institute of Industrial Engineers
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• v.16 no.2
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• pp.63-70
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• 1990

This paper presents a new temporal reasoning scheme based on explicit expression of time intervals. The proposed scheme deals with the general problem of temporal knowledge representation and temporal reasoning and may be used in rule-based systems and qualitative models. Time intervals, not time points, are defined in terms of orders and/or numbers in a quantity space. As a result, the system behavior is represented in the form of partially ordered networks. Such explicit and qualitative description of temporal quantities enables both reduction of ambiguity and parsimonious used of temporal information. Based on the proposed temporal reasoning scheme, a new rule-based qualitative simulation system is being built and evaluated.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.5
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• pp.93-102
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• 1997

Conventional expert systems have difficulties in the inference on time-varing situations because they don't have the structure for processing time related informations and rule representation method to describe time explicitely. Some expert systems capable of temporal reasoning are not applicable to the domain in which state changes happen by unpredictble events that cannot be represented by periodic changes of data. In this paper, an event based temporal reasoning method is proposed. It is capable of processing te unpredictable events, representing the knowledge related to event and time, and infering by that knowledge as well as infering by periodically time-varing data. The NEO/temporal, an temporal inference engine, is implemented by applying the proposed temporal reasoning situation assessment and decision supporting system is implemented to show the benefits of the proposed temporal information processing model.

• Journal of KIISE:Software and Applications
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• v.36 no.7
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• pp.509-522
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• 2009

The W3C Ontology Working Group has recently developed OWL as an ontology language for the Semantic Web. OWL, however, fails to perform the process of reasoning about temporal knowledge because it lacks full-pleadged semantics for temporal language. Entities in the real world are changing as time passes, while new facts are being introduced as new events occur. KBs without temporal information are incomplete and incorrect. In this paper, we propose an extended temporal ontology language called TL-OWL which provides an abstract syntax and semantics for representing and reasoning about temporal information in the Semantic Web.

• Journal of KIISE
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• v.43 no.12
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• pp.1365-1375
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• 2016

One of the most important capabilities for autonomous service robots working in living environments is to recognize and understand the correct context in dynamically changing environment. To generate high-level context knowledge for decision-making from multiple sensory data streams, many technical problems such as multi-modal sensory data fusion, uncertainty handling, symbolic knowledge grounding, time dependency, dynamics, and time-constrained spatio-temporal reasoning should be solved. Considering these problems, this paper proposes an effective dynamic context management and spatio-temporal reasoning method for intelligent service robots. In order to guarantee efficient context management and reasoning, our algorithm was designed to generate low-level context knowledge reactively for every input sensory or perception data, while postponing high-level context knowledge generation until it was demanded by the decision-making module. When high-level context knowledge is demanded, it is derived through backward spatio-temporal reasoning. In experiments with Turtlebot using Kinect visual sensor, the dynamic context management and spatio-temporal reasoning system based on the proposed method showed high performance.

• Journal of KIISE
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• v.43 no.9
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• pp.963-973
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• 2016

Providing a semantic knowledge system using media ontologies requires not only conventional axiom reasoning but also knowledge extension based on various types of reasoning. In particular, spatio-temporal information can be used in a variety of artificial intelligence applications and the importance of spatio-temporal reasoning and expression is continuously increasing. In this paper, we append the LOD data related to the public address system to large-scale media ontologies in order to utilize spatial inference in reasoning. We propose an RDFS/Spatial inference system by utilizing distributed memory-based framework for reasoning about large-scale ontologies annotated with spatial information. In addition, we describe a distributed spatio-temporal SPARQL parallel query processing method designed for large scale ontology data annotated with spatio-temporal information. In order to evaluate the performance of our system, we conducted experiments using LUBM and BSBM data sets for ontology reasoning and query processing benchmark.

• The Journal of Society for e-Business Studies
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• v.12 no.3
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• pp.133-150
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• 2007

In this paper we investigate a trend analysis service using Semantic Web technology in a news domain. The trend analysis service can provide more intelligent answers rather than the answer given In current news search engines since it can analyze the passage of time and the relation among news. In order to provide the trend analysis service, the capability of temporal reasoning is required, but the Semantic Web language such as OWL does not support the reasoning capability. Therefore, we propose a language TL-OWL(Temporal Web Ontology Language) extending OWL with the temporal reasoning.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.4
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• pp.37-48
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• 2009

Household service robots should be able to provide their users with a variety of temporal reasoning services. In this paper, we propose an effective way of developing such temporal reasoning services using a domain-independent AI planner. Developing temporal reasoning services with a domain-independent AI planner, we have to address both the knowledge engineering problem of how to represent various real-world temporal constraints in a planning domain definition language, and the system design problem of how to realize the interface between the AI planner and the service consumer. In this paper, we introduce an example scenario and a set of typical temporal constraints for a household service robot, and then present how to represent them in the standard planning domain definition language. We also explain how to implement a service agent based on an AI planner in order to develop and provide new services efficiently.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.91-93
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• 2004

차세대 정보통신 기술의 가장 중요한 패러다임으로 '유비쿼터스 컴퓨팅' 이 새롭게 주목받고 있다 유비쿼터스 환경에서의 서비스 지원 시스템을 개발하기 위한 중요한 문제 중의 하나는 이동 객체(사용자)의 시간과 이벤트의 관계를 파악하고 위치 이동 데이터로부터 시공간 이동 패턴을 탐사하는 것이다. 본 논문에서는 유비쿼터스 환경 내에서 사용자에게 시간과 관련된 서비스를 적절히 제공하기 위해서 다음과 같은 연구를 한다. 첫째, 서비스 관점에서의 시간적 추론(Temporal Reasoning)이다. 각 사용자들은 각자의 취향을 가지고 있으며 이는 시간과 밀접한 관계를 가지고 있다. 시간과 관련된 사용자의 취향이 기록된 각 사용자 프로파일을 기반으로 서비스 지원 시스템은 적절한 서비스를 제공할 수 있다. 둘째, 사용자의 취향을 기록하기 위한 시간적 추론(Temporal Reasoning)이 다. 기록된 내 용들은 사용자 프로파일 (User Profile)을 생성하는데 도움을 준다.