Stefan Janaqi

Generalization of the strategies in differential evolution

Summary form only given. Differential evolution, is a recently invented global optimization algorithm. Originally proposed as a method for the global continuous optimization differential evolution has been easily modified for handling mixed (continuous and discrete) variables. In order to have a better choice of the differentiations formula, we introduce a generalization of the differential evolutions strategies. This is done by dividing them into four groups according to their differentiation principle. Such approach leads us to the new universal formula of differentiation. Some examples of strategies are demonstrated and compared on the De Jong test functions.

A framework for unifying ontology-based semantic similarity measures

Ontologies are widely adopted in the biomedical domain to characterize various resources (e.g. diseases, drugs, scientific publications) with non-ambiguous meanings. By exploiting the structured knowledge that ontologies provide, a plethora of ad hoc and domain-specific semantic similarity measures have been defined over the last years. Nevertheless, some critical questions remain: which measure should be defined/chosen for a concrete application? Are some of the, a priori different, measures indeed equivalent? In order to bring some light to these questions, we perform an in-depth analysis of existing ontology-based measures to identify the core elements of semantic similarity assessment. As a result, this paper presents a unifying framework that aims to improve the understanding of semantic measures, to highlight their equivalences and to propose bridges between their theoretical bases. By demonstrating that groups of measures are just particular instantiations of parameterized functions, we unify a large number of state-of-the-art semantic similarity measures through common expressions. The application of the proposed framework and its practical usefulness is underlined by an empirical analysis of hundreds of semantic measures in a biomedical context.

The semantic measures library and toolkit: fast computation of semantic similarity and relatedness using biomedical ontologies

UNLABELLED The semantic measures library and toolkit are robust open-source and easy to use software solutions dedicated to semantic measures. They can be used for large-scale computations and analyses of semantic similarities between terms/concepts defined in terminologies and ontologies. The comparison of entities (e.g. genes) annotated by concepts is also supported. A large collection of measures is available. Not limited to a specific application context, the library and the toolkit can be used with various controlled vocabularies and ontology specifications (e.g. Open Biomedical Ontology, Resource Description Framework). The project targets both designers and practitioners of semantic measures providing a JAVA library, as well as a command-line tool that can be used on personal computers or computer clusters. AVAILABILITY AND IMPLEMENTATION Downloads, documentation, tutorials, evaluation and support are available at http://www.semantic-measures-library.org.

Semantic Similarity from Natural Language and Ontology Analysis

Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments; most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli. In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics, i.e. their meaning; intuitively, the words tea and coffee, which both refer to stimulating beverage, will be estimated to be more semantically similar than the words toffee (confection) and coffee, despite that the last pair has a higher syntactic similarity. The two state-of-the-art approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal, computer-readable and workable forms of knowledge such as semantic networks, thesauri or ontologies. Semantic measures are widely used today to compare units of language, concepts, instances or even resources indexed by them (e.g., documents, genes). They are central elements of a large variety of Natural Language Processing applications and knowledge-based treatments, and have therefore naturally been subject to intensive and interdisciplinary research efforts during last decades. Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains toward a better understanding of semantic similarity estimation and more generally semantic measures. To this end, we propose an in-depth characterization of existing proposals by discussing their features, the assumptions on which they are based and empirical results regarding their performance in particular applications. By answering these questions and by providing a detailed discussion on the foundations of semantic measures, our aim is to give the reader key knowledge required to: (i) select the more relevant methods according to a particular usage context, (ii) understand the challenges offered to this field of study, (iii) distinguish room of improvements for state-of-the-art approaches and (iv) stimulate creativity toward the development of new approaches. In this aim, several definitions, theoretical and practical details, as well as concrete applications are presented.

Semantic Measures Based on RDF Projections: Application to Content-Based Recommendation Systems

Many applications take advantage of both ontologies and the Linked Data paradigm to characterize various kinds of resources. To fully exploit this knowledge, measures are used to estimate the relatedness of resources regarding their semantic characterization. Such semantic measures mainly focus on specific aspects of the semantic characterization (e.g. types) or only partially exploit the semantics expressed in the knowledge base. This article presents a framework for defining semantic measures to compare instances defined within an RDF knowledge base. A special type of measure, based on the representation of an instance through projections, is detailed and evaluated through its use in a music band recommender system.

New Strategies in Differential Evolution

Differential Evolution, a quite recent evolutionary optimization algorithm, is gaining more and more popularity among evolutionary algorithms. Proposed as a method for the global continuous optimization, Differential Evolution has been easily modified for mechanical engineering purposes and for handling nonlinear constraints. In this paper we introduce a new type of strategies which increase stability of the algorithm reducing its computational expenses. Also we propose a new principle of strategies’ design. Theoretical discussions lead us to a tradeoff that helps to choose the better strategy. The strategies are illustrated, tested and compared on a classical test suite. We present a part of the testing results.

Subontology Extraction Using Hyponym and Hypernym Closure on is-a Directed Acyclic Graphs

Ontologies are successfully used as semantic guides when navigating through the huge and ever increasing quantity of digital documents. Nevertheless, the size of numerous domain ontologies tends to grow beyond the human capacity to grasp information. This growth is problematic for a lot of key applications that require user interactions such as document annotation or ontology modification/evolution. The problem could be partially overcome by providing users with a subontology focused on their current concepts of interest. A subontology restricted to this sole set of concepts is of limited interest since their relationships can generally not be explicit without adding some of their hyponyms and hypernyms. This paper proposes efficient algorithms to identify these additional key concepts based on the closure of two common graph operators: the least common-ancestor (lca) and greatest common descendant (gcd). The resulting method produces ontology excerpts focused on a set of concepts of interest and is fast enough to be used in interactive environments. As an example, we use the resulting program, called OntoFocus (http://www.ontotoolkit.mines-ales.fr/), to restrict, in few seconds, the large Gene Ontology (~30,000 concepts) to a subontology focused on concepts annotating a gene related to breast cancer.

Robust real-time optimization for the linear oil blending

In this paper we present a robust real-time optimization method for the online linear oil blending process. The blending process consists in determining the optimal mix of components so that the final product satisfies a set of specifications. We examine different sources of uncertainty inherent to the blending process and show how to address this uncertainty applying the Robust Optimization techniques. The polytopal structure of our problem permits a simplified robust approach. Our method is intended to avoid reblending and we measure its performance in terms of the blend quality giveaway and feedstocks prices. The difference between the nominal and the robust optimal values (the price of robustness) provides a benchmark for the cost of reblending which is difficult to estimate in practice. This new information can be used to adjust the level of conservatism in the model. We analyze the feasibility of a blend to be produced from a set of feedstocks when the heel of a previous blend is used in the composition of the new blend. Additional critical information for the control system is then produced.

From Theoretical Framework to Generic Semantic Measures Library

Semantic Measures (SMs) are of critical importance in multiple treatments relying on ontologies. However, the improvement and use of SMs are currently hampered by the lack of a dedicated theoretical framework and an extensive generic software solution. To meet these needs, this paper introduces a unified theoretical framework of graph-based SMs, from which we developed the open source Semantic Measures Library and toolkit, a solution that paves the way for design, computation and analysis of SMs. More information at dedicated website: http://www.semantic-measures-library.org.

Reliability Improvement of Odour Detection Thresholds Bibliographic Data

Odour control is an important industrial issue as it is a criterion in purchase of a material. The minimal concentration of a pure compound allowing to perceive its odour, called Odour Detection Threshold (ODT), is a key of the odour control. Each compound has its own ODT. Literature is the main source to obtain ODT, but a lot of compounds are not reported and, when reported, marred by a high variability. This paper proposes a supervised cleaning methodology to reduce uncertainty of available ODTs and a prediction of missing ODTs on the base of physico-chemical variables.