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Nowadays there is a real need to operate and link existing knowledge expressed by experts, in domains in which highly reliable recommendation systems are needed. This is especially true in the medical domain where knowledge sources are heterogeneous, since they are separately formed in different contexts. A major difficulty is to relate these sources together in a way that respects the specic medical...
Can we mine latent patterns from discrete, non-numeric heterogeneous data? Many modern data sets contain heterogeneous non-numerical information measured over Boolean, ordinal and ternary scales. Values for features like these are "mixable" in the sense that they have intuitive non-linear analogs to classical "addition" (e.g. logical OR for Boolean data). We present a novel, general...
Discrimination discovery from data consists of designing data mining methods for the actual discovery of discriminatory situations and practices hidden in a large amount of historical decision records. Approaches based on classification rule mining consider items at a flat concept level, with no exploitation of background knowledge on the hierarchical and inter-relational structure of domains. On...
We present an end-to-end visual analytics framework that aims to facilitate prediction and decision making about honey bee health based on micro sensing data. The framework is particularly tailored to cope with heterogeneous data from micro sensors and environmental sensors that are deployed to collect information about bees and their environment. The framework design allows for a wide range of end...
This paper describes a decision support system (DSS) design for small business owners/managers to support their strategic decision-making in particular for achieving competitive advantages in the e-commerce environment. To design the DSS framework we collected data through web-based questionnaires that cater for the concerns of owners'/managers' experiences and strategic decisions need — in such environment...
Complex adaptive systems are in the heart of smart city operations. Developing such systems is very challenging. Current user-driven techniques for modeling the system domain are not sufficient for supporting the development of such systems. Additionally, in the majority of cases, existing work focused on user interface adaptation without considering content personalization. In this paper, we propose...
The use of Information and Communication Technology (ICT) has touched various aspects in the domain of transport engineering and logistics (TEL). As the development of TEL tends to be more complex in operation and large in scale, recent practices start to pay more attentions on improving system robustness and reliability. In addition, current ICT innovations (such as WSN and IOT) could record and...
Context-aware Recommender Systems aim to provide users with the most adequate recommendations for their current situation. However, an exact context obtained from a user could be too specific and may not have enough data for accurate rating prediction. This is known as the data sparsity problem. Moreover, often user preference representation depends on the domain or the specific recommendation approach...
In order to use technology to influence human behaviour and promote safer and more fuel efficient behaviour through incentive mechanisms, an instrumented vehicle is developed. The first step is to make it “perceive” the outside world, so extracting knowledge from some data sources such as sensors is crucial. More critically, there is a fundamental need for a standard that would enable knowledge sharing/exchanging...
Medical tourism is a domain rich of data that are stored in many hundreds of data sources. Many of these sources should be used during the development of tourism information systems. That is why, it is very important to create a referential model that represents the medical tourism in Tunisia. The e-tourism ontology provides a way to achieve integration and interoperability through the use of shared...
As wireless communication and mobile devicesadvances, recommendation system is one of the keytechnologies to realize personalized service. This paperproposes a service recommendation mechanism using aprobabilistic model in mobile devices. With the contextualinformation and the use's demand state inferred by the model, we can recommend a service to meet the user's preferencesand needs at real time...
Accelerated growth of the Web has made difficult retrieving information although the current search engines algorithms implemented are very sophisticated. This paper proposes a methodology that makes use of Semantic Web technologies to build search engine for a given context. The Semantic Web is made of a set of technologies, tools and standards that form the basis of an infrastructure to support...
In recent years, the fast growth of Web pages and the constant evolution of internet technologies have lead to a significant increase in the number of pedagogical resources. Thus, the indexing and search problems have become crucial. To overcome this problem, it was proposed to use information coming from the norms and standards of educational metadata. However, this solution does not solve completely...
The scale of the social web has integrated users in order to organize shared resources. Users freely associate keywords (tags) to resources. This collection of tags creates a folksonomy. Folksonomy is a collaborative tagging system, which has grown popular with its simplicity of free tagging. However, it rises up a number of issues such as ambiguous and redundant tags. So as to achieve an optimal...
Dynamic adaptation of educational content has been an important research topic. Therefore, in order for it to run effectively, student models that properly describe and monitor the cognitive state of students are needed. In this sense, this paper presents a hybrid student model approach that combines ontologies and Bayesian Networks to identify the knowledge of each student based on their characteristics...
This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between...
In a related work, the author developed an approach to support model-based system engineering (MBSE) which ensures the conformance to a standard metamodel such as UML, SysML, or NAF, with dedicated project ontologies in order to ease the understanding and creation of models. The proposed mix approach brings flexibility and opens perspectives to support MBSE via predefined templates. However, the usage...
The annotation of protein provides a considerable knowledge for the biologists in order to understand life at the molecular level. The computational annotation of protein function has therefore emerged as an important alternative given that the biological experiments are extremely laborious. A number of methods have been developed to computationally annotate proteins using standardized nomenclatures...
The context life cycle demonstrates how context information moves around each phase into a system, from context modeling until context dissemination. The modeling phase turns context data gathered from sensors into a higher level information which in most cases is modeled through ontologies. This paper presents a systematic mapping of the state of art (2009-2015) about ontological context modeling-related...
Generally speaking, information filtering systems recommend users items that are related to their usage and/or static profiles or items visited or liked by users having same interests or habits. However, a new user who starts with an empty profile and who does not belong yet to any community could not benefit from this recommendation service. This problem is known as the cold start of a new user....
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