Representation of context-dependant knowledge in ontologies: A model and an application

Bobillo, Fernando; Delgado, Miguel; Gómez‐Romero, Juan

doi:10.1016/j.eswa.2007.08.090

Cited by 40 publications

(20 citation statements)

References 28 publications

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“…A Knowledge Management Systems (KMS) can be introduced to manage large amounts of information that may or may not be related to traditional decision-making processes. KMSs assist the user to systematically trawl though massive amount of data to determine the information, within the context, to make decisions without causing information overload [29]. A coherent system is required in order to integrate a KMS into the corporate DSS.…”

Section: Lispmentioning

confidence: 99%

Decision-Making in a Distributed and Dynamically Scalable Environments

Tweedale

2015

Intelligent Decision Technology Support in Practice

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Experiments conducted by the Knowledge-Based Intelligent Information and Engineering Systems (KES) Centre use Java to gain its many advantages, especially in a distributed and dynamically scalable environment. Interoperability within and across ubiquitous computing operations has evolved to a level where plug 'n' play protocols can be used to invoke common interfaces. Designers are able to create dynamic interfaces using reflectance to effectively and efficiently conduct distributed decision-making. Many applications now use mobile agents to support web-centric activities. One example includes: dynamic agent functionality within simulations that automatically adapt to incoming data and/or languages via scripts or messaging. This has been shown using demonstrations described herein. Many are web centric and involve data mining or the use of other types of Intelligent Decision Support System (IDSS).

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Section: Lispmentioning

confidence: 99%

Decision-Making in a Distributed and Dynamically Scalable Environments

Tweedale

2015

Intelligent Decision Technology Support in Practice

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“…In more expressive DLs such as SROIQ [46,41], RB consists of a finite set of role axioms stating restrictions as subsumption, transitivity, cardinality, etc [44].…”

Section: Ontologies and Description Logicsmentioning

confidence: 99%

“…Nowadays, properties and semantics of ontology constructs mainly are determined by Description Logics (DLs) [38][39][40], a family of logics for representing structured knowledge which have proved to be very useful as ontology languages [44]. Formally, an ontology is a triple O = ⟨RB, T B, AB⟩, where RB (the Role Box or RBox) and T B (the Terminological Box or TBox) comprise the intensional knowledge, i.e., general knowledge about the world to be described (statements about roles and concepts, respectively), and AB (the Assertional Box or ABox) the extensional knowledge, i.e., particular knowledge about a specific instantiation of this world (statements about individuals in terms of concepts and roles) [44].…”

Section: Ontologies and Description Logicsmentioning

confidence: 99%

Semantic operations of multiple soft sets under conflict

Jiang

Tang

Chen

et al. 2011

Computers & Mathematics with Applications

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a b s t r a c tMolodtsov initiated the concept of soft set theory, which can be used as a generic mathematical tool for dealing with uncertainty. Description Logics (DLs) are a family of knowledge representation languages which can be used to represent the terminological knowledge of an application domain in a structured and formally well-understood way. Nowadays, properties and semantics of ontology constructs mainly are determined by DLs. In this paper we investigate semantic operations of multiple standard soft sets by using domain ontologies (i.e., DL intensional knowledge bases). Concretely, we give some semantic operations such as complement, restricted difference, extended union, restricted intersection, restricted union, extended intersection, AND, and OR for (multiple) standard soft sets from a semantic point of view. Especially, we also present an approach to deal with conflict from a semantic point of view when we define these semantic operations. Moreover, the basic properties and implementation methods of these semantic operations under conflict are also presented and discussed.

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“…In this hierarchy, each higher-level concept is more generic than its lower-level concept. In description logic [104], when a more generic concept includes a more particular concept (like the generality relation between "female" and "mother" in the rule:…”

Section: Proposition 1 {Fuzzy Temporal Concept) Let Set Of M Fuzzy Smentioning

confidence: 99%

fuzzy temporal data-mining model for activity recognition in smart homes /

Amirjavid¹

2013

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At present time, aging of the population is one of the main challenges of the 21st century. The current situation is leading to an increased number of people afflicted with cognitive disorders such as Alzheimer's disease. This group of people suffers from a progressive decline in their abilities to perform what are called the activities of the daily living (ADLs).The consequence of this reality is the urgent need for more home assistance services, as these people desire to continue living independently at home. To address this important issue, Smart Home laboratories such as LIARA, DOMUS and MavHome perform research in order to propose technological solutions for assistance provision to residents of the Smart Home. Assisting people in carrying out their ADLs, increasing quality of life and optimizing spent energy are some of the goals in Smart Home design. Technically speaking, a Smart Home is an ambient environment which, through its embedded sensors, captures data resulting from the observation of activities carried out in this environment. This data is then analyzed by artificial intelligence techniques in order to provide information about home state normality and needed assistance. In the end, the system aims to intervene by providing guidance through its actuators. In this context, activity recognition becomes a key element in order to be able to provide adequate information services at the right moment.This thesis aims to contribute to this important challenge relating to activity recognition in the Smart Home designed for cognitive assistance. This contribution follows in the footsteps of temporal data mining and activity recognition approaches, and proposes a new way to automatically recognize and memorize ADLs from low-level sensors. From a formal point of view, the originality of the thesis relies on the proposition of a new unsupervised temporal datamining model for activity recognition addressing the problem of current temporal approaches based on Allen's framework. This new model incorporates some applications of fuzzy logic in order to take into account the uncertainty present in the realization of daily living activities by the resident. More specifically, we propose an extension of the fuzzy clustering technique in order to cluster the observations based on the degree of similarity between observations, so that activities are modeled and recognized. Moreover, anomaly recognition, decision making for assistance provision and judgment for simultaneous activities are some of the applicative contributions of this thesis. From a practical and experimental standpoint, the contribution of this research has been validated in order to evaluate how it would perform in a realistic context. To achieve this, we used MATLAB software as a simulation platform to test the proposed model. We then performed a series of tests which took the form of several case studies relating to common activities of daily living, in order to show the functionality and efficiency of the proposed temporal data-mining approa...

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Representation of context-dependant knowledge in ontologies: A model and an application

Cited by 40 publications

References 28 publications

Decision-Making in a Distributed and Dynamically Scalable Environments

Decision-Making in a Distributed and Dynamically Scalable Environments

Semantic operations of multiple soft sets under conflict

fuzzy temporal data-mining model for activity recognition in smart homes /

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