Readings in information retrieval.

In the last few years, many smart objects found in the physical world are interconnected and communicate through the existing internet infrastructure which creates a global network infrastructure called the Internet of Things (IoT). Research has shown a substantial development of solutions for a wide range of devices and IoT platforms over the past 6-7 years. However, each solution provides its own IoT infrastructure, devices, APIs, and data formats leading to interoperability issues. Such interoperability issues are the consequence of many critical issues such as vendor lock-in, impossibility to develop IoT application exposing cross-platform, and/or cross-domain, difficulty in plugging non-interoperable IoT devices into different IoT platforms, and ultimately prevents the emergence of IoT technology at a large-scale. To enable seamless resource sharing between different IoT vendors, efforts by several academia, industry, and standardization bodies have emerged to help IoT interoperability, i.e., the ability for multiple IoT platforms from different vendors to work together. This paper performs a comprehensive survey on the state-of-the-art solutions for facilitating interoperability between different IoT platforms. Also, the key challenges in this topic is presented.

/pdf/interoperability-in-internet-of-things-taxonomies-and-open-3ve4smuwsl.pdf

Interoperability in Internet of Things: Taxonomies and Open Challenges

The Semantic Web contains an enormous amount of information in the form of knowledge bases (KB). To make this information available, many question answering (QA) systems over KBs were created in the last years. Building a QA system over KBs is difficult because there are many different challenges to be solved. In order to address these challenges, QA systems generally combine techniques from natural language processing, information retrieval, machine learning and Semantic Web. The aim of this survey is to give an overview of the techniques used in current QA systems over KBs. We present the techniques used by the QA systems which were evaluated on a popular series of benchmarks: Question Answering over Linked Data. Techniques that solve the same task are first grouped together and then described. The advantages and disadvantages are discussed for each technique. This allows a direct comparison of similar techniques. Additionally, we point to techniques that are used over WebQuestions and SimpleQuestions, which are two other popular benchmarks for QA systems.

/pdf/core-techniques-of-question-answering-systems-over-knowledge-ztjne1c44r.pdf

Core techniques of question answering systems over knowledge bases: a survey

Semantic Question Answering (SQA) removes two major access requirements to the Semantic Web: the mastery of a formal query language like SPARQL and knowledge of a specific vocabulary. Because of the complexity of natural language, SQA presents difficult challenges and many research opportunities. Instead of a shared effort, however, many essential components are redeveloped, which is an inefficient use of researcher’s time and resources. This survey analyzes 62 different SQA systems, which are systematically and manually selected using predefined inclusion and exclusion criteria, leading to 72 selected publications out of 1960 candidates. We identify common challenges, structure solutions, and provide recommendations for future systems. This work is based on publications from the end of 2010 to July 2015 and is also compared to older but similar surveys.

/pdf/survey-on-challenges-of-question-answering-in-the-semantic-4ywnhlotki.pdf

Survey on challenges of Question Answering in the Semantic Web

Application of deep learning to cybersecurity: A survey

Smart objects are now present in our everyday lives, and the Internet of Things is expanding both in number of devices and in volume of produced data. These devices are deployed in dynamic ecosystems, with spatial mobility constraints, intermittent network availability depending on many parameters e.g. battery level or duty cycle, etc. To capture knowledge describing such evolving systems, open, shared and dynamic knowledge representations are required. These representations should also have the ability to adapt over time to the changing state of the world. That is why we propose IoT-O, a core-domain modular IoT ontology proposing a vocabulary to describe connected devices and their relation with their environment. First, existing IoT ontologies are described and compared to requirements an IoT ontology should be compliant with. Then, after a detailed description of its modules, IoT-O is instantiated in a home automation use case to illustrate how it supports the description of evolving systems.

https://hal.archives-ouvertes.fr/hal-01467853/document

IoT-O, a Core-Domain IoT Ontology to Represent Connected Devices Networks

Afin de faciliter la mise en place de systemes pouvant gerer de grandes quantites d'information et de s'adresser a un maximum d'utilisateurs, le fonctionnement du noyau des Systemes de Recherche d'Information (SRI) est generalement independant du contexte. La Recherche d'Information contextuelle vise a replacer l'utilisateur au coeur des SRI en rendant explicites certains elements du contexte qui peuvent influencer sur les performances des systemes. Dans ce cadre, nous proposons un modele a base de deux ontologies dont l'objectif est de representer deux aspects primordiaux du contexte d'une recherche : le theme du besoin et la tâche de recherche choisie par l'utilisateur. Des ontologies legeres de domaine sont utilisees pour representer la connaissance associee a ces deux aspects du contexte. L'originalite de notre modele repose sur le fait que les deux aspects distincts mais complementaires du contexte sont lies par l'utilisation d'elements communs aux deux ontologies. L'integration du modele dans le SRI intervient dans deux phases du processus de recherche. Par la proposition d'un mecanisme d'indexation semantique utilisant l'ensemble de la connaissance representee dans les deux ontologies, il est integre a la phase de representation des documents. De plus, le modele est integre a la phase d'acces aux documents via la navigation dans les ontologies. Cette navigation repose sur deux niveaux d'acces a l'information. Le niveau concept donne a l'utilisateur une vue globale sur la collection de documents et sur la connaissance associee, alors que le niveau instance donne un acces aux informations specifiques contenues dans les documents. L'utilisation d'ontologies en RI pose une autre problematique qui est la reutilisation de la connaissance deja modelisee. En effet, de nombreuses ressources terminologiques (comme les thesaurus) ou conceptuelles (ontologies) existent dans differents domaines. Nous avons etudie la reutilisabilite de telles ressources selon deux perspectives : le choix d'une ontologie legere en fonction de son adequation au corpus a indexer et l'elaboration d'une ontologie legere a partir d'un thesaurus normalise et d'un corpus de reference. Une originalite de nos travaux concernant l'evaluation de l'adequation reside dans la prise en compte de l'ensemble des relations definies dans les ontologies et non pas seulement des relations taxonomiques. Concernant l'elaboration d'une ontologie legere a partir d'un thesaurus, une de nos contributions est de proposer un mecanisme semi-automatique pour capturer la connaissance representee dans le thesaurus et la mettre a jour a partir de documents de reference. Dans le cadre d'un projet en cooperation avec des astronomes (Masses de Donnees en Astronomie), nous avons pu evaluer un certain nombre des techniques que nous proposons. Un prototype illustre egalement l'apport de nos contributions.

/pdf/ontologies-de-domaine-pour-la-modelisation-du-contexte-en-2jw301zyym.pdf

Ontologies de domaine pour la modélisation du contexte en recherche d'Information

Simple ontology alignments, largely studied in the literature, link a single entity of a source ontology to a single entity of a target ontology. One of the limitations of these alignments is, however, their lack of expressiveness which can be overcome by complex alignments. While diverse state-of-the-art surveys mainly review the matching approaches in general, to the best of our knowledge, there is no study about the specificities of the complex matching problem. In this paper, an overview of the different complex matching approaches is provided. It proposes a classification of the complex matching approaches based on their specificities (i.e., type of correspondences, guiding structure). The evaluation aspects and the limitations of these approaches are also discussed. Insights for future work in the field are provided.

Survey on complex ontology matching

Traditional information retrieval systems aim at satisfying most users for most of their searches, leaving aside the context in which the search takes place. We propose to model two main aspects of context: The themes of the user's information need and the specific data the user is looking for to achieve the task that has motivated his search. Both aspects are modeled by means of ontologies. Documents are semantically indexed according to the context representation and the user accesses information by browsing the ontologies. The model has been applied to a case study that has shown the added value of such a semantic representation of context.

Modeling context through domain ontologies

Our purpose is to hide the complexity of formulating a query expressed in a graph query language such as SPARQL. We propose a mechanism allowing queries to be expressed in a very simple pivot language, mainly composed of keywords and relations between keywords. Our system associates the keywords with the corresponding elements of the ontology (classes, relations, instances). Then it selects pre-written query patterns, and instanciates them with regard to the keywords of the initial query. Several possible queries are generated, ranked and then shown to the user. These queries are presented by means of natural language sentences. The user then selects the query he/she is interested in and the SPARQL query is built.

Nathalie Hernandez

Papers

IoT-O, a Core-Domain IoT Ontology to Represent Connected Devices Networks

Ontologies de domaine pour la modélisation du contexte en recherche d'Information

Survey on complex ontology matching

Modeling context through domain ontologies

A semantic web interface using patterns: the SWIP system