Reasoning About Temporal Context Using Ontology and Abductive Constraint Logic Programming

Zhu, Hongwei; Madnick, Stuart E.; Siegel, Michael

doi:10.1007/978-3-540-30122-6_7

Cited by 11 publications

(11 citation statements)

References 12 publications

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“…Recent research [13,14] extended COIN to represent and reason about semantic changes over time. For example, when comparing historic stock prices in different exchanges, some of them changed reporting currency.…”

Section: B Scalability Analysismentioning

confidence: 99%

Context Interchange as a Scalable Solution to Interoperating Amongst Heterogeneous Dynamic Services

Zhu

Madnick

2004

SSRN Journal

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Abstract-Many online services access a large number of autonomous data sources and at the same time need to meet different user requirements. It is essential for these services to achieve semantic interoperability among these information exchange entities. In the presence of an increasing number of proprietary business processes, heterogeneous data standards, and diverse user requirements, it is critical that the services are implemented using adaptable, extensible, and scalable technology. The Context Interchange (COIN) approach, inspired by similar goals of the Semantic Web, provides a robust solution. In this paper, we describe how COIN can be used to implement dynamic online services where semantic differences are reconciled on the fly. We show that COIN is flexible and scalable by comparing it with several conventional approaches. With a given ontology, the number of conversions in COIN is quadratic to the semantic aspect that has the largest number of distinctions. These semantic aspects are modeled as modifiers in a conceptual ontology; in most cases the number of conversions is linear with the number of modifiers, which is significantly smaller than traditional hard-wiring middleware approach where the number of conversion programs is quadratic to the number of sources and data receivers. In the example scenario in the paper, the COIN approach needs only 5 conversions to be defined while traditional approaches require 20,000 to 100 million. COIN achieves this scalability by automatically composing all the comprehensive conversions from a small number of declaratively defined sub-conversions.

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Section: B Scalability Analysismentioning

confidence: 99%

Context Interchange as a Scalable Solution to Interoperating Amongst Heterogeneous Dynamic Services

Zhu

Madnick

2004

SSRN Journal

Self Cite

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“…Although seemingly not directly related to automated trading, the Semantic Web may come to meet the increased technological demands emerging in the world of trading [10,15]. In achieving this purpose, we deem it necessary to provide extensions to current Semantic Web languages, thus making the latter more suitable for the knowledge we seek to represent.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Engineering in a Temporal Semantic Web Context

Milea

Frăsincar

Kaymak

2008

2008 Eighth International Conference on Web Engineering

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The emergence of Web 2.0 and the Semantic Web as established technologies is fostering a whole new breed of Web applications and systems. These are often centered around knowledge engineering and context awareness. However, adequate temporal formalisms underlying context awareness are currently scarce. Our focus in this paper is two-fold. We first introduce a new OWL-based temporal formalism -TOWL -for the representation of time, change, and state transitions. Based hereon we present a financial Web-based application centered around the aggregation of stock recommendations and financial data.

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“…For example, Nogueira et al (2004) built a framework of contextual logic programming language (CxLP) for a temporal database. Zhu et al (2004) extended a context interchange framework (COIN) for overcoming the context heterogeneity problem that is complicated when the context is changed over time. Moldovan et al (2005) proposed a model for converting the temporal event in a query of natural language into a logic representation in order to increase the performance of a question answering system.…”

Section: Introductionmentioning

confidence: 99%

A RDF and OWL-Based Temporal Context Reasoning Model for Smart Home

Liao¹,

Tu²

2007

Information Technology J.

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Abstract:In the ubiquitous computing environment, context reasoning is an important issue of context-awareness. It is used to deduce desired or higher-level context and then to provide suitable services automatically. The previous context-reasoning approaches are mainly non-temporal. The reasoning is according to the real-time contexts without time information. However, temporal contexts are very important information for context-awareness. Therefore, a temporal context reasoning model (TempCRM) based on resource description framework (RDF) and Web ontology language (OWL) is proposed in this paper. TempCRM is used for inferring the dangerous level of a smart home. In a home environment, a potential dangerous situation is caused by a series of temporal events. A temporal event is represented as a RDF-based temporal context. A smart home ontology is defined for the terms and relationships used in the temporal context. Then, a set of reasoning rules can be defined for inferring and computing the dangerous level. In the simulation study, a script with dangerous situations is designed to evaluate the dangerous level generated by TempCRM. The result illustrates that TempCRM is useful to alarm the inhabitant and thus prevent the occurrence of an incident from the temporal contexts.

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Reasoning About Temporal Context Using Ontology and Abductive Constraint Logic Programming

Cited by 11 publications

References 12 publications

Context Interchange as a Scalable Solution to Interoperating Amongst Heterogeneous Dynamic Services

Context Interchange as a Scalable Solution to Interoperating Amongst Heterogeneous Dynamic Services

Knowledge Engineering in a Temporal Semantic Web Context

A RDF and OWL-Based Temporal Context Reasoning Model for Smart Home

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