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The paper presents a recommendation model for developing new smart city and smart health projects. The objective is to provide recommendations to citizens about smart city and smart health startups to improve entrepreneurship and leadership. These recommendations may lead to the country’s advancement and the improvement of national income and reduce unemployment. This work focuses on designing and implementing an approach for processing and analyzing tweets inclosing data related to smart city and smart health startups and providing recommended projects as well as their required skills and competencies. This approach is based on tweets mining through a machine learning method, the Word2Vec algorithm, combined with a recommendation technique conducted via an ontology-based method. This approach allows discovering the relevant startup projects in the context of smart cities and makes links to the needed skills and competencies of users. A system was implemented to validate this approach. The attained performance metrics related to precision, recall, and F-measure are, respectively, 95%, 66%, and 79%, showing that the results are very encouraging.

now commonly accepted that building ontology is a key step in the development of knowledge based systems. A quite number of methodologies for ontology development have been proposed in the literature. However, this field still lacks mature methodologies which provide a clear vision for how to build ontologies. Most methodologies provide insufficient details about their adopted techniques and activities. This paper gives a preliminary guide which analysis and discusses some selected methodologies for ontology building. The important criteria and guidelines resulting from current literature have been adapted to perform this discussion and analysis. The performed analysis showed as outcomes that the existing methodology needs and additional hard work to be mature.

This paper reviews and compares some Ontology Development Tools, Formalisms and Languages from those reported in the Literature, with a special attention accorded to the interoperability between them. Additionally, this paper presents the Structure and Basic Features of Tools, Formalisms and languages. The main criterion for comparison of these tools and languages was the user interest and their application in different kind of real world tasks. The primary goal of this study is to introduce several tools and languages to ensure more understanding from their use. Consequently,we can solve the problems of current tools and languages and ensure the easy development of a new generation of tools and languages.

In data mining, an association rule is a pattern that states the occurrence of two items (premises and consequences) together with certain probability. A class association rule set (CARs) is a subset of association rules with classes specified as their consequences. This paper focuses on class association rules mining based on the approach of Rough Set Theory (RST). In addition, this paper presents an algorithm for finest class rule set mining inspired from Apriori algorithm, where the support and confidence are computed based on the elementary set of lower approximation inspired from RST. The proposed approach has been shown very effective, where the rough set approach for class association discovery is much simpler than the classic association method.

The main objective of the exploitation of semantic descriptions of services through Semantics is a better support for the life-cycle of Web services. The large number of developed ontologies, languages of representations, and integrated frameworks supporting the discovery, composition and invocation of services are a good indicator that research in the field of semantic web service (SWS) has been considerably active. The authors provide in this paper a detailed overview of the approaches and solutions, indicating their core characteristics and objectives required and provide indicators for the interested reader to follow up further insights and details about these solutions and related software.

Business intelligence (BI) is a promising way which let companies to gather large amounts of data, accessing, analyzing that data, and presenting a high-level set of reports that condense the huge amount of that data so that they can make fundamental business decisions regarding business actors (customer, supplier, logistics…). To learn from the past and present time and anticipate the new market trends, many companies are adopting Business Intelligence (BI) tools and systems. There are numerous business intelligence tools to help managers in making the best decisions. The paper give some explanation of the concepts and the components of BI, benefits of BI, tools for BI, the implementation of business intelligence, and a proposal to combine BI and semantic Web. Additionally, the paper describes the popularity and the increase of data and information use from open sources (OS) and its impacts in competitive and marketing intelligence.

The process of data mining produces various patterns from a given data source. The most recognized data mining tasks are the process of discovering frequent itemsets, frequent sequential patterns, frequent sequential rules and frequent association rules. Numerous efficient algorithms have been proposed to do the above processes. Frequent pattern mining has been a focused topic in data mining research with a good number of references in literature and for that reason an important progress has been made, varying from performant algorithms for frequent itemset mining in transaction databases to complex algorithms, such as sequential pattern mining, structured pattern mining, correlation mining. Association Rule mining (ARM) is one of the utmost current data mining techniques designed to group objects together from large databases aiming to extract the interesting correlation and relation among huge amount of data. In this article, we provide a brief review and analysis of the current status of frequent pattern mining and discuss some promising research directions. Additionally, this paper includes a comparative study between the performance of the described approaches., Comment: 14 pages, 3 figures. arXiv admin note: text overlap with arXiv:1312.4800; and with arXiv:1109.2427 by other authors

In the last few years, the Semantic Web gained scientific acceptance as a means of relationships identification in knowledge base, widely known by semantic association. Query about complex relationships between entities is a strong requirement for many applications in analytical domains. In bioinformatics for example, it is critical to extract exchanges between proteins. Currently, the widely known result of such queries is to provide paths between connected entities from data graph. However, they do not always give good results while facing the user need by the best association or a set of limited best association, because they only consider all existing paths but ignore the path evaluation. In this paper, we present an approach for supporting association discovery queries. Our proposal includes (i) a query language PmSPRQL which provides a multiparadigm query expressions for association extraction and (ii) some quantification measures making easy the process of association ranking. 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In recent years, semantic similarity measure has a great interest in Semantic Web and Natural Language Processing (NLP). Several similarity measures have been developed, being given the existence of a structured knowledge representation offered by ontologies and corpus which enable semantic interpretation of terms. Semantic similarity measures compute the similarity between concepts/terms included in knowledge sources in order to perform estimations. This paper discusses the existing semantic similarity methods based on structure, information content and feature approaches. Additionally, we present a critical evaluation of several categories of semantic similarity approaches based on two standard benchmarks. The aim of this paper is to give an efficient evaluation of all these measures which help researcher and practitioners to select the measure that best fit for their requirements., Comment: 10 pages

Given that semantic Web realization is based on the critical mass of metadata accessibility and the representation of data with formal knowledge, it needs to generate metadata that is specific, easy to understand and well-defined. However, semantic annotation of the web documents is the successful way to make the Semantic Web vision a reality. This paper introduces the Semantic Web and its vision (stack layers) with regard to some concept definitions that helps the understanding of semantic annotation. Additionally, this paper introduces the semantic annotation categories, tools, domains and models., Comment: 8 pages, 3 figures

Several researches in Semantic Web are based on information search centered meaning. The common purpose of these researches is to improve current information search and retrieval methods. The last few years have seen a various number of developed semantic search systems. The requirement of a complete survey in this field is one of the main purpose of this paper. The approaches, methodologies and objectives of some recognized projects and their corresponding practical systems are exploited to constitute this overall view of semantic search. In this paper, we present and compare various research directions in semantic search. Further, we give discussion with regards to future research in this area.

Semantic search engines promise to provide more accurate results than current keywords search engines. However, it is useful to exploit ontological relations in search steps. In this paper, we present an approach which automatically exploits the keywords in a user query to find related concepts in domain ontology. Given an ontology and some annotated pages, the weights assigned to relations in a given page indicate its strength. However, in order to rank results, we assign a weight for each annotated page. This paper proposes a prototype (SSERank) relation-based page weighting and ranking with semantic search capability. The obtained results show that SSERank will have great utility although there is potential for improvement.

International audience; Dans une ontologie de domaine, une association sémantique entre deux entités (concepts, attributs d'un concept) est une représentation d'un chemin ou d'un lien sémantique (LS) indirect entre elles. Un défi prometteur pour le Web sémantique est de développer des méthodes pour découvrir des données fortement liées dans un nombre important d'associations sémantiques rassemblées à partir des sources disparates. Dans ce contexte, cet article présente, en premier lieu, le degré d'un lien sémantique (DLS) pour mesurer une relation directe entre deux entités, et en deuxième lieu le degré d'une association sémantique (DAS) pour mesurer des associations sémantiques extraites à partir d'une ontologie de domaine. Les résultats expérimentaux montrent l'avantage des méthodes proposées et démontrent leur efficacité prometteuse.