Saturday, December 23, 2006

Representing Context on the SemanticWeb 

Representing Context on the SemanticWeb (PDF file)
by Elena Paslaru Bontas

"Abstract: In this paper we analyze the dimensions of context for the Semantic Web. Starting from a definition which in our opinion relates to most of the ways context was understood in computer science before, we propose a context ontology and an architecture for a context modelling framework to control the acquisition of contextual information for typical information items on the Semantic Web (e.g. ontologies, Web Services) and to assist the development of context-sensitive applications."

"Finally we try to estimate the role of context in the novel scenario of the Semantic Web, by studying the particularities of this setting, compared to the Artificial Intelligence or Natural Language Processing ones, and the consequences of these particularities in resolving the key questions concerning context. The Context Modelling Framework intends to put these ideas into practice, by offering an environment for the development of context-sensitive applications on the Semantic Web and for the integration of context aspects in available applications, on the basis of a context ontology."

Friday, December 22, 2006

The Semantic Web is Closer Than You Think The Semantic Web is Closer Than You Think: "Two quick points are worth making here. First, we all spend some amount of our brain power -- almost entirely without consciously knowing that this is what we are doing -- dealing with informal, implicit ontologies. In order to act meaningfully at all within particular social contexts, we need to have understood something roughly like an ontology of that context. In any situation or context there will be features which we attend to, because they just are the salient features of that context, and an even larger number of things about the situation which we do not attend to, which we cannot even call features, because they are the background noise against which salience emerges. The homo sapiens form of the mammalian brain is very good at doing this. It's so good, in fact, that it has figured out how to get computers to do something (very) roughly like this, too."

Semantic Web in a Pervasive Context-Aware 

Semantic Web in a Pervasive Context-Aware

by Harry Chen, Tim Finin, and Anupam Joshi

This document describes a new approach that explores the use of Semantic Web languages in building an architecture for supporting context-aware systems. This new architecture called Context Broker Architecture (CoBrA) differs from other architectures in using theWeb Ontlogy Language OWL for modeling ontologies of context and for supporting context reasoning. Central to our architecture is a broker agent that maintains a shared model of context for all computing entities in the space and enforces the privacy policies defined by the users. We also describe the use of CoBrA and its associated ontologies in prototyping an intelligent meeting room."

Contexts for the Semantic Web 

Contexts for the Semantic Web
by R.Guha, R.McCool, and R.Fikes

"Abstract. A central theme of the semantic web is that programs should be able to easily aggregate data from different sources. Unfortunately, even if two sites provide their data using the same data model and vocabulary, subtle differences in their use of terms and in the assumptions they make pose challenges for aggregation. Experiences with the TAP project reveal some of the phenomena that pose obstacles to a simplistic model of aggregation. Similar experiences have been reported by AI projects such as Cyc, which has lead to the development and use of various context mechanisms. In this paper we report on some of the problems with aggregating independently published data and propose a context mechanism to handle some of these problems. We briefly survey the context mechanisms developed in in AI and contrast them with the requirements of a context mechanism for the semantic web. Finally, we present a context mechanism for the semantic web that is adequate to handle the aggregation tasks, yet simple from both computational and model theoretic perspectives."

"We believe that a context mechanism that is similar in spirit to the earlier context mechanisms will be not only useful, but required to achieve the semantic web vision."

Semantic Web Contexts - Google Search 

Semantic Web Contexts - Google Search. This result set is quite large.

Thursday, December 21, 2006

Dublin Core in Multiple Languages: Esperanto, Interlingua, or Pidgin? 

Dublin Core in Multiple Languages: Esperanto, Interlingua, or Pidgin?
by Thomas Baker
The experience of artificial languages like Esperanto suggests they need good governance to control divergence in usage, but flexibility to evolve and grow. Language engineers have neglected to consider pidgins --- simplified hybrids invented spontaneously by speakers of different languages. If Dublin Core is pidgin metadata, perhaps it needs an interlingua --- a language-neutral set of elements mediating between richer sets --- for the collective negotiation of meanings and for managing the inevitable tension between simplicity and complexity. Adaptations of Dublin Core in languages other than English would not be mere translations of a canon, but equal participants in an ongoing revision of that canon."

Tuesday, December 19, 2006

Towards an extensible context ontology for Ambient Intelligence 

Towards an extensible context ontology for
Ambient Intelligence
(PDF file)

by Davy Preuveneers, Jan Van den Bergh, Dennis Wagelaar, Andy Georges,
Peter Rigole, Tim Clerckx, Yolande Berbers, Karin Coninx, Viviane
Jonckers, and Koen De Bosschere

"Context-awareness is a hot research domain, with interesting topics such as
context modeling, formal context languages for specifying facts and interrelationships, and infrastructure support for querying and reasoning on contextual information using an inference engine.
The Context Ontology Language (CoOL) [3] is an ontology-based context modeling approach, which uses the Aspect-Scale-Context (ASC) model where each aspect (e.g. spatial distance) can have several scales (e.g. kilometer scale or mile scale) to express some context information (e.g. 20). Mapping functions exist to convert context information from one scale to another. CoOL is very useful for describing concepts with an inherent metric ordering such as in requirement R.2, though less practical for expressing scales for aspects as in requirement R.1. Chen et al. [4] propose a context broker architecture (CoBrA) using an ontology to describe persons, places and intentions. Less emphasis is put on the notion of services and related aspects, such as user interfaces and mobile devices on which these services are deployed, needed to fulfill the above requirements. Gu et al. [5] present a service-oriented context-aware middleware (SOCAM) based on a context model with person, location, activity and computational entity (such as a device, network, application, service, etc.) as basic context concepts. The notion of mobile services seems to be beyond the scope of this context model. Henricksen and Indulska [6] propose a context model that describes context based on several types of facts (e.g. sensed, static and profiled) subject to constraints and quality annotations."

"context ontology" - Google Search 

If the meaning of a URI is its location in the fabric of context, then it would help if we had an "context ontology" - Google Search. There seems to be more here than when I search on "ontology of context".

Sunday, December 17, 2006

Word Meanings: Deixis and Person 

HLW: Word Meanings: Deixis and Person:
"A particular instance of language is an utterance. An utterance needs to be distinguished from a particular word, phrase, or sentence because an utterance has an utterance context, a particular Speaker, Hearer, time, and place, in addition a linguistic form. I'll sometimes refer to the Speaker and Hearer as utterance participants. For example, we can put the English words I, like, and it together to make the English sentence I like it, but this sentence is a different utterance each time it is uttered.

Each utterance has its own context, and, as we will see below, for each context the sentence has a different meaning. That is, meaning always changes from one utterance context to another. The figure below is one way of representing the elements of an utterance context. Each of the elements is a role in the context, a kind of slot that gets filled by something in each different situation. For example, the Speaker role is filled by a particular person, and the Location role is filled by a particular place. We will meet the concept of role again later in this book; in fact it is one of the most fundamental notions in cognitive science."

Ontology Based Context Modeling and Reasoning using OWL 

There is a strong interest in an ontology of context in the field of mobile, ubiquitous computing. Researchers are using semantic web technologies to define the ontology.
Ontology Based Context Modeling and Reasoning using OWL (PDF file)
by Xiao Hang Wang, Tao Gu, Da Qing Zhang, Hung Keng Pung
In this paper we propose an OWL encoded context ontology (CONON) for modeling context in pervasive computing environments, and for supporting logic based context reasoning. CONON provides an upper context ontology that captures general concepts about basic context, and also provides extensibility for adding domain-specific ontology in a hierarchical manner. Based on this context ontology, we have studied the use of logic reasoning to check the consistency of context information, and to reason over low-level, explicit context to derive high-level, implicit context. By giving a performance study for our prototype, we quantitatively evaluate the feasibility of logic based context reasoning for non-time-critical applications in pervasive computing environments, where we always have to deal carefully with the limitation of computational resources."

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