A Constraint-based Recommender System via RDF Knowledge Graphs

Knowledge graphs, represented in RDF, are able to model entities and their relations by means of ontologies. The use of knowledge graphs for information modeling has attracted interest in recent years. In recommender systems, items and users can be mapped and integrated into the knowledge graph, which can represent more links and relationships between users and items. Constraint-based recommender systems are based on the idea of explicitly exploiting deep recommendation knowledge through constraints to identify relevant recommendations. When combined with knowledge graphs, a constraint-based recommender system gains several benefits in terms of constraint sets. In this paper, we investigate and propose the construction of a constraint-based recommender system via RDF knowledge graphs applied to the vehicle purchase/sale domain. The results of our experiments show that the proposed approach is able to efficiently identify recommendations in accordance with user preferences

Improving Semantic Similarity Measure Within a Recommender System Based-on RDF Graphs

In today's era of information explosion, more users are becoming more reliant upon recommender systems to have better advice, suggestions, or inspire them. The measure of the semantic relatedness or likeness between terms, words, or text data plays an important role in different applications dealing with textual data, as in a recommender system. Over the past few years, many ontologies have been developed and used as a form of structured representation of knowledge bases for information systems. The measure of semantic similarity from ontology has developed by several methods. In this paper, we propose and carry on an approach for the improvement of semantic similarity calculations within a recommender system based-on RDF graphs

A Personalized Recommender System Based-on Knowledge Graph Embeddings

Knowledge graphs have proven to be effective for modeling entities and their relationships through the use of ontologies. The recent emergence in interest for using knowledge graphs as a form of information modeling has led to their increased adoption in recommender systems. By incorporating users and items into the knowledge graph, these systems can better capture the implicit connections between them and provide more accurate recommendations. In this paper, we investigate and propose the construction of a personalized recommender system via knowledge graphs embedding applied to the vehicle purchase/sale domain. The results of our experimentation demonstrate the efficacy of the proposed method in providing relevant recommendations that are consistent with individual users

Syst\`eme de recommandations bas\'e sur les contraintes pour les simulations de gestion de crise

In the context of the evacuation of populations, some citizens/volunteers may want and be able to participate in the evacuation of populations in difficulty by coming to lend a hand to emergency/evacuation vehicles with their own vehicles. One way of framing these impulses of solidarity would be to be able to list in real-time the citizens/volunteers available with their vehicles (land, sea, air, etc.), to be able to geolocate them according to the risk areas to be evacuated, and adding them to the evacuation/rescue vehicles. Because it is difficult to propose an effective real-time operational system on the field in a real crisis situation, in this work, we propose to add a module for recommending driver/vehicle pairs (with their specificities) to a system of crisis management simulation. To do that, we chose to model and develop an ontology-supported constraint-based recommender system for crisis management simulations.Comment: in French languag

ONE STEP SYNTHESIS OF WATER-DISPERSIBLE CoFe2O4 MAGNETIC NANOPARTICLES USING TRIETHYLENETETRAMINE AS SOLVENT AND STABILISING LIGAND

Magnetic CoFe2O4 nanoparticles were synthesised by one step synthetic method through thermal decomposition of Co and Fe precursors in triethylenetetramine solvent at high temperature. The advantage of this method is the ability to make monodisperse nanoparticles with high water-dispersibility and stability. The particle size can be tuned in the range of 7-11.3 nm by varying synthetic conditions. The obtained particles with small DLS size (less than 21 nm) are ready to disperse and stable in aqueous solution for weeks without any surface modification

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

L’analyse de la structure de représentation du discours pour le français

In the rise of the internet, user-generated content from social networking services is becoming a giant source of information that can be useful to businesses on the aspect where users are viewed as customers or potential customers for companies. Exploitation of user-generated texts can help identify their feelings, intentions, or reduce the effort of the agents who are responsible for collecting or receiving information on social networking services. As part of this thesis, the content of texts such as speeches, statements, conversations from interactive communication on social media platforms become the main data object of our study. We deepen an analysis of structures and components of sentences in texts on the basis of Combinatory Categorial Grammar (CCG) and the Discourse Representation Structure (DRS). We propose a method for extracting a CCG tree from the dependency structure of the sentence, and a general architecture to build a bridge of relationship between syntaxes and semantics of French sentences. As a result, our study achieves representations of natural language texts in a new form of first order logic or the box of DRS.Dans l’essor d’internet, les contenus générés par les utilisateurs à partir des services de réseaux sociaux deviennent une source géante d’informations qui peuvent être utile aux entreprises sur l’aspect où les utilisateurs sont considérés comme des clients ou des clients potentiels pour les entreprises. L’exploitation des textes générés par les utilisateurs peut aider à identifier leurs sentiments, leurs intentions, ou réduire l’effort des agents qui sont responsables de recueillir ou de recevoir des informations sur les services de réseaux sociaux. Dans le cadre de cette thèse, les contenues de textes tels que discours, énoncés, conversations issues de la communication interactive sur les plateformes de réseaux sociaux deviennent l’objet de données principales de notre étude. Nous approfondissons une analyse de structures et composants des phrases dans les textes sur la base de la Grammaire Catégoriel Combinatoire (GCC) et la théorie des représentations du discours. Nous proposons une méthode pour l’extraction d’un arbre de GCC à partir de l’arbre dépendante de la phrase, et une architecture générale pour construire un pont de relation entre les syntaxes et les sémantiques des phrases françaises. Par conséquent, notre étude obtient de la représentation de textes de la langue naturel sous une nouvelle forme de la logique du premier ordre ou la boîte de la structure des représentations du discours

CCG Supertagging Using Morphological and Dependency Syntax Information

International audienceAfter presenting a new CCG supertagging algorithm based on morphological and dependency syntax information, we use this algorithm to create a CCG French Tree Bank corpus (20,261 sentences) based on the FTB corpus by Abeillé et al. We then use this corpus, as well as the Groningen Tree Bank corpus for the English language, to train a new BiLSTM+CRF neural architecture that uses (a) morphosyntactic input features and (b) feature correlations as input features. We show experimentally that for an inflected language like French, dependency syntax information allows significant improvement of the accuracy of the CCG supertagging task, when using deep learning techniques

Towards a DRS Parsing Framework for French

International audienceCombinatory Categorial Grammars provide a transparent interface between surface syntax and underlying semantic representation. Discourse Representation Theory allows the handling of meaning across sentence boundaries. Based on the foundations of these two theories along with the work of Johan Bos on the Boxer framework for English language, we propose an approach to the task of semantic parsing with Discourse Representation Structure for the French language. By giving an example of discourse analysis on French sentences and experimenting on 4,525 sentences taken from the French Treebank corpus, we demonstrate and evaluate the outcomes of our framework

Apport des ontologies pour le calcul de la similarit\'e s\'emantique au sein d'un syst\`eme de recommandation

Measurement of the semantic relatedness or likeness between terms, words, or text data plays an important role in different applications dealing with textual data such as knowledge acquisition, recommender system, and natural language processing. Over the past few years, many ontologies have been developed and used as a form of structured representation of knowledge bases for information systems. The calculation of semantic similarity from ontology has developed and depending on the context is complemented by other similarity calculation methods. In this paper, we propose and carry on an approach for the calculation of ontology-based semantic similarity using in the context of a recommender system.Comment: in French languag