Nowadays, the use of the Internet of Things (IoT) in diverse applications becomes very popular. Accordingly, a proliferation of objects with remote sensing, actuation, analysis, and sharing capabilities will be interconnected on top of heterogeneous communication networks. Their deployment contexts are continuously changed, which imply a change in their descriptions and characteristics. In addition, they are a fundamental source of a huge quantity of gathered data with different encoding formats. Accordingly, this data is badly expressed, understood and exploited by other systems and devices. From this regard, several challenges associated with standardization, interoperability, discovery, security, and description of IoT resources and their corresponding data have emerged. In this context, Semantic Web Technologies (SWT) seem a suitable and an efficient solution to relieve these challenges. Therefore, a Systematic Literature Review (SLR) methodology is performed to investiagte and analyze a set of the most recent and relevant approaches that deal with SWT in the IoT domain. These approaches are discussed and evaluated based on seven different research questions. Finally, future insights and research opportunities are suggested.

Semantic Web Technologies for the Internet of Things: Systematic Literature Review

https://hal.inria.fr/hal-00812704/document

Automatic case acquisition from texts for process-oriented case-based reasoning

Les Journees francophones d'ingenierie des Connaissances ont heberge de nombreux articles lies a l'intelligence artificielle et a la sante au sein des sessions plenieres mais egalement dans les ateliers associes. Ces journees ont permis de creer des forums d'echanges scientifiques et de creer une communaute de recherche active. L'objectif de cet article est de realiser une revue semi-automatique des articles produits dans ce contexte sur la thematique de l'intelligence artificielle et de la sante, d'analyser leur impact sur le domaine et d'identifier les defis a relever. Pour cela, nous avons analyse semi-automatiquement 108 articles publies entre 2010 et 2018, a partir de leurs titre, mots-cles et resume. Nous avons identifie sept grands themes de recherche dont les principaux sont lies a la construction, l'alignement et l'utilisation d'ontologies, la recherche d'informations, les annotations, les recommandations, les bonnes pratiques, le traitement auto-matique de la langue, la fouille de donnees de sante et l'analyse des medias sociaux pour des applications liees a la sante. Cette revue montre l'hybridation des approches symboliques et numeriques pour la creation de methodes et d'outils au coeur de la medecine du futur.

https://hal.archives-ouvertes.fr/hal-02329573/document

Intelligence Artificielle et Santé. Une analyse rétrospective depuis 2010

As a young discipline at the junction of computer science, artificial intelligence and cognitive sciences, knowledge engineering aims at modelling knowledge of a specific domain to operationalise them in a computer system. To this end, it offers theoretical tools, models and empirical methodologies to support knowledge sharing between the user and the system. The work developed here is related to knowledge engineering of a particular type of system: case-based reasoning systems (CBR). A CBR system assists a user in his problem solving task by retrieving a previous successful problem solving experience and by adapting it to the current situation. In this work, we are mainly interested in the interacting system "user - CBR tool". The main research question we address here can be formulated as: what methods and tools have to be developed to support knowledge acquisition in the learning system "user - CBR tool". This issue raises the question of the knowledge of the reasoning process and leads to an analysis at the knowledge level of CBR systems. Another part of the analysis aims at studying the interactions between the user and the CBR tool during the problem solving phases. These aspects are studied at several levels in the different contributions presented in this thesis. Our different experiences and experiments lead us to propose, as a first contribution, a formalisation at general level of interactive knowledge learning in CBR (FIKA). This formalisa- tion relies on the reasoning failures which, as they allow to highlight the gaps in the available knowledge, are used to guide the learning process. Two extensions of this general model have been proposed: IAKA and FRAKAS. IAKA refines the principles proposed in FIKA to permit their immediate implementation in a particular type of system where knowledge can be represented according to a given model (cases and adaptation knowledge in the form of adaptation operators). These principles have been implemented and experimented with in an application developed exclusively for this purpose. FRAKAS proposes similar methods and tools for another type of system where domain knowledge is used to guide adaptation. As for these principles, they have been implemented in a prototype inspired by a real world application. We have conducted a study of strengths and limits of FRAKAS and IAKA and we have investigated possible ways to combine them. A first practical implementation has been made in a CBR application allowing the adaptation of cooking recipes, the project TAAABLE.

https://tel.archives-ouvertes.fr/tel-00364368/document

Interactive and Opportunistic Knowledge Acquisition in Case-Based Reasoning

This paper addresses the issue of adapting cases represented by plain text with the help of formal concept analysis and natural language processing technologies. The actual cases represent recipes in which we classify ingredients according to culinary techniques applied to them. The complex nature of linguistic anaphoras in recipe texts make usual text mining techniques inefficient so a stronger approach, using syntactic and dynamic semantic analysis to build a formal representation of a recipe, had to be used. This representation is useful for various applications but, in this paper, we show how one can extract ingredient–action relations from it in order to use formal concept analysis and select an appropriate replacement sequence of culinary actions to use in adapting the recipe text.

/pdf/text-adaptation-using-formal-concept-analysis-2lgc9d69zz.pdf

Text adaptation using formal concept analysis

This paper presents how the Taaable project addresses the textual case-based reasoning challenge of the CCC, thanks to a combination of principles, methods, and technologies of various fields of knowledge-based system technologies, namely CBR, ontology engineering manual and semi-automatic), data and text-mining using textual resources of the Web, text annotation (used as an indexing technique), knowledge representation, and hierarchical classification Indeed, to be able to reason on textual cases, indexing them by a formal representation language using a formal vocabulary has proven to be useful

https://hal.inria.fr/inria-00337666/document

TAAABLE: Text Mining, Ontology Engineering, and Hierarchical Classification for Textual Case-Based Cooking

Since large monolithic ontologies are di cult to handle and reuse ontology modularization has attracted increasing attention. Several approaches and tools have been developed to support ontology modularization. Despite these e orts, a lack of knowledge about characteristics of modularly organized ontologies prevents further development. This work aims at characterizing modular ontologies. Therefore, we analyze existing modular ontologies by applying selected metrics from software engineering in order to identify recurring structures, i.e. patterns in modularly organized ontologies. The contribution is a set of four patterns which characterize modularly organized ontologies.

/pdf/characterizing-modular-ontologies-1670jdevfb.pdf

Characterizing Modular Ontologies

The textual case-based cooking systemWIKITAAABLE participates to the second Computer cooking contest (CCC). It is an extension of the TAAABLE system that has participated to the first CCC. WIKITAAABLE's architecture is composed of a semantic wiki used for the collaborative acquisition of knowledge (recipe, ontology, adaptation knowledge) and of a case-based inference engine using this knowledge for retrieving and adapting recipes. This architecture allows various modes of knowledge acquisition for case-based reasoning that are studied within the TAAABLE project. In particular, opportunistic adaptation knowledge discovery is an approach for interactive and semi-automatic learning of adaptation knowledge triggered by a feedback from the user.

https://hal.inria.fr/inria-00411508/document

Knowledge Acquisition and Discovery for the Textual Case-Based Cooking system WIKITAAABLE

Taaable is a case-based reasoning system that adapts cooking recipes to user constraints. Within it, the preparation part of recipes is formalised as a graph. This graph is a semantic representation of the sequence of instructions composing the cooking process and is used to compute the procedure adaptation, conjointly with the textual adaptation. It is composed of cooking actions and ingredients, among others, represented as vertices, and semantic relations between those, shown as arcs, and is built automatically thanks to natural language processing. The results of the automatic annotation process is often a disconnected graph, representing an incomplete annotation, or may contain errors. Therefore, a validating and correcting step is required. In this paper, we present an existing graphic tool named \kcatos, conceived for representing and editing decision trees, and show how it has been adapted and integrated in WikiTaaable, the semantic wiki in which the knowledge used by Taaable is stored. This interface provides the wiki users with a way to correct the case representation of the cooking process, improving at the same time the quality of the knowledge about cooking procedures stored in WikiTaaable.

Semi-automatic annotation process for procedural texts: An application on cooking recipes

This paper is a survey about semantic wikis and the use of semantic Web technologies by wiki engines. First, it presents the main notions related to semantic wikis. Then, twelve active semantic wiki projects are compared, according to various viewpoints such as knowledge representation philosophy, use of semantic Web standards and system usability. Finally, a formal concept analysis approach is carried out to provide a guideline for the choice of semantic wiki engine, given a set of needed features. In conclusion, the paper argues about semantic wiki issues such as the weaknesses of some semantic wiki engine interoperability.

/pdf/semantic-wiki-engines-a-state-of-the-art-208w2r26on.pdf

Thomas Meilender

Papers

TAAABLE: Text Mining, Ontology Engineering, and Hierarchical Classification for Textual Case-Based Cooking

Characterizing Modular Ontologies

Knowledge Acquisition and Discovery for the Textual Case-Based Cooking system WIKITAAABLE

Semi-automatic annotation process for procedural texts: An application on cooking recipes

Semantic wiki engines: a state of the art