Understanding and Managing Medical Data and Knowledge Dynamics

For several decades, Artificial Intelligence is concerned with the definition of the concept of knowledge in order to exploit it for various purposes such as information retrieval, decision support or semantic interoperability. This is mainly done thanks to knowledge representation models such as ontologies aiming at representing and organising the concepts of a given domain. However, the evolution of these models and its impact on depending artefacts remain open research problems.The biomedical domain is specific in a sense that its associated knowledge is complex and constantly evolving as underlined by the ever increasing number of published scientific communications. This is why I have focus on this domain and its specificities have been the various lines I have followed during my research work.Three main thematics have focused my efforts over the past years:1- Biomedical knowledge representation2- The management of the evolution of biomedical knowledge representation models 3- The validation of biomedical knowledge representation models These topics are more largely detailed in this manuscriptDepuis plusieurs décennies, le domaine de l’Intelligence Artificielle s’intéresse à définir la notion de connaissance afin de l’exploiter à des fins diverses dans plusieurs cadres d’application tels que la recherche d’information, l’aide à la décision ou encore l’interopérabilité sémantique. Ceci est en partie réalisé grâce à l’utilisation de modèles de représentation des connaissances tels que les ontologies permettant la spécification d’une conceptualisation. Cependant, les aspects liés à l'évolution des connaissances et des modèles qui leur sont associés restent largement inexplorés et demeurent des problèmes de recherche ouverts.Le domaine biomédical est un domaine très riche dans la mesure où les connaissances qu’il intègre sont complexes et en perpétuelle évolution comme le démontre le nombre sans cesse croissant de communications scientifiques publiées au quotidien. C’est pour ces raisons qu’il a suscité mon intérêt et ses spécificités ont constitué la ligne directrice de mes activités de recherche.Trois grandes thématiques ont focalisé mes efforts au cours de ces dernières années et ont concentré la majeure partie de mes contributions scientifiques et collaborations dans ce domaine particulier.1- La représentation des connaissances en santé.2- La gestion de l'évolution des modèles de représentation des connaissances biomédicales.3- La validation des modèles de représentation des connaissances biomédicales.Mes travaux autour de ces trois thématiques sont détaillés dans ce manuscrit

Towards natural language question generation for the validation of ontologies and mappings

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)The increasing number of open-access ontologies and their key role in several applications such as decision-support systems highlight the importance of their validation. Human expertise is crucial for the validation of ontologies from a domain point-of-view. However, the growing number of ontologies and their fast evolution over time make manual validation challenging. Methods: We propose a novel semi-automatic approach based on the generation of natural language (NL) questions to support the validation of ontologies and their evolution. The proposed approach includes the automatic generation, factorization and ordering of NL questions from medical ontologies. The final validation and correction is performed by submitting these questions to domain experts and automatically analyzing their feedback. We also propose a second approach for the validation of mappings impacted by ontology changes. The method exploits the context of the changes to propose correction alternatives presented as Multiple Choice Questions. Results: This research provides a question optimization strategy to maximize the validation of ontology entities with a reduced number of questions. We evaluate our approach for the validation of three medical ontologies. We also evaluate the feasibility and efficiency of our mappings validation approach in the context of ontology evolution. These experiments are performed with different versions of SNOMED-CT and ICD9. Conclusions: The obtained experimental results suggest the feasibility and adequacy of our approach to support the validation of interconnected and evolving ontologies. Results also suggest that taking into account RDFS and OWL entailment helps reducing the number of questions and validation time. The application of our approach to validate mapping evolution also shows the difficulty of adapting mapping evolution over time and highlights the importance of semi-automatic validation.The increasing number of open-access ontologies and their key role in several applications such as decision-support systems highlight the importance of their validation. Human expertise is crucial for the validation of ontologies from a domain point-of-vi7115FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)2014/14890-

Analyzing the Evolution of Semantic Correspondences between SNOMED CT and ICT-9-CM

International audienceThe combined use of Knowledge Organizations Systems (KOS) including ontologies, terminologies or codification schemas has widespread in e-health systems over the past decades due to semantic interoperability reasons. However, the dynamic aspect of KOS forces knowledge engineers to maintain KOS elements, as well as semantic correspondences between KOS up-to-date. This is crucial to keep the underlying systems exploiting these KOS consistent over time. In this paper we provide a pragmatic analysis of the evolution of mappings between SNOMED CT and ICD-9-CM affected by the evolution of these two KOS

Requirements for Implementing Mappings Adaptation Systems

International audienceOntologies, or more generally speaking, Knowledge Organization Systems (KOS) have been developed to support the correct interpretation of shared data in collaborative applications. The quantity and the heterogeneity of domain knowledge often require several KOS to describe their content. In order to assure unambiguous interpretation, overlapped concepts of different, but domain-related KOS are semantically connected via mappings. However, in various domains, KOS periodically evolve creating the necessity of reviewing the validity of associated mappings. The size of KOS remains a barrier for a manual review of mappings, and rather requires the support of (semi-) automatic solutions. This article describes our experiences in understanding how KOS evolution affects mappings. We present our lessons learned from various empirical experiments, and we derive primary elements and requirements for improving the automation of mapping maintenance

Complaint Ontology Pattern - COP

In this paper we present an ontology design pattern to conceptualize complaints - an important domain still uncovered by ODPs. The proposed Complaint Ontology Pattern (COP) has been designed based on the analysis of free text complaints from available complaint datasets (banking, air transport, automobile) among other knowledge sources. We present a detailed use case from consumer disputes. We evaluate the pattern by annotating the complaints from our use case and by discussing how COP aligns to existing ontologies

Une approche adaptative pour la recherche d'information sur le web

The advent of the Web in the early 90s has deeply upset our society. This new media has rapidly become the greatest database in the world. Moreover, the ever increasing popularity of the Web engendered a huge dynamics with respect to Web data. Actually, by virtue of knowledge evolution, data is permanently added, deleted or updated from the Web which raises important issues regarding Web information retrieval. Existing Web search engines are neither able to take knowledge evolution into account when users submit their queries nor able to understand users' needs in order to return the most relevant information to users. The Semantic Web, proposed in 2001 and which aims at giving a sense to Web data in order to make it machine understandable, helps to improve Web search but knowledge evolution is still problematic. In this work, we address the problem of taking knowledge evolution for improving Web search in the sense of relevance of the returned results. The advocated solution is based on the use of ontologies, cornerstone of the Semantic Web, for representing both the domain targeted by the query and the profile of the user who submit the query. Ontologies are considered as knowledge that is evolving over time. In consequence, the ontology evolution problem has to be tackled as regards the evolution of the targeted domain but also with respect to the evolution of users' profile. First of all, we introduce a new paradigm: adaptive ontology as well as a process for making adaptive ontologies smoothly follow evolution of a domain. The so-defined model relies on the adaptation of ideas developed in the field of psychology and biology to the knowledge engineering field. Then, we propose an approach exploiting adaptive ontologies for improving Web information retrieval. To this end, we first introduce data structures, WPGraphs and W3Graphs, for representing Web data. We then introduce the ASK query language tailored for the extraction of relevant information from these structures. We also propose a set of query enrichment rules based on the exploitation of ontological elements as well as adaptive ontologies characteristics of the ontology representing the domain targeted by the query and the one representing the view of the user on the domain. Lastly, we introduce a tool for managing adaptive ontologies and for searching relevant information on the Web as well as an experimental validation of the introduced concepts. We based our validation on the definition of a realistic case study devoted to the retrieval of scientific articles published at the International World Wide Web series of conference.Depuis son avènement au début des années 1990, le Web a profondément bouleversé la société contemporaine et ce à plusieurs niveaux. Ce nouvel outil est rapidement devenu incontournable et s'est affirmé comme la plus grande base de données du monde. La popularité sans cesse croissante du Web a généré une dynamique très importante principalement au niveau des données qu'il renferme. En effet, en vertu de l'évolution des connaissances du monde réel, de nouvelles informations sont rajoutées, d'autres retirées et certaines sont modifiées sans cesse sur le Web posant ainsi des problèmes pour retrouver l'information pertinente. Les moteurs de recherche existants ne sont pas capables d'une part de prendre en compte l'évolution des connaissances du Web lorsqu'un utilisateur pose une requête et d'autre part, de comprendre les besoins en information de l'utilisateur pour lui retourner les pages Web répondant à ces besoins. L'apparition du paradigme du Web Sémantique, visant à donner un sens aux données du Web pour les rendre compréhensibles par les machines grâce à l'utilisation d'ontologies, contribue à l'amélioration de la recherche documentaire sur le Web. Cependant, les problèmes posés par l'évolution restent peu pris en compte. Dans ces travaux, nous nous sommes intéressés à la prise en compte de l'évolution des données du Web dans le but d'améliorer, en terme de pertinence des résultats, la recherche documentaire sur le Web. La solution que nous proposons est basée sur les ontologies, fondement du Web Sémantique, pour représenter les connaissances du domaine de recherche visé par des requêtes ainsi que les vues des utilisateurs sur ce domaine. Dans la solution que nous préconisons, les ontologies sont vues comme des connaissances qui évoluent au cours du temps. Cette solution nous a obligé à considérer l'évolution des ontologies sous deux aspects différents : de manière générale par rapport au domaine visé par la requête et de manière plus particulière par rapport aux points de vue des utilisateurs. En premier lieu, nous proposons un modèle d'ontologies adaptatives ainsi qu'un processus d'adaptation permettant aux ontologies de s'adapter aux évolutions des connaissances d'un domaine. Le modèle ainsi défini s'appuie sur des idées émises dans les domaines de la psychologie et des sciences naturelles. Ensuite, nous proposons une exploitation de ce type d'ontologie pour améliorer la recherche documentaire sur le Web. Nous introduisons tout d'abord, des structures de données (les WPGraphs et W3Graphs) pour la représentation des données du Web, puis le langage de requête ASK adapté à ces structures pour l'extraction des données pertinentes. Nous proposons également un ensemble de règles d'enrichissement des requêtes ASK basé sur les relations ontologiques et les éléments propres aux ontologies adaptatives des ontologies représentant le domaine visé par la requête et celle représentant les vues des utilisateurs sur le domaine. Pour finir nous proposons un outil pour la gestion des ontologies adaptatives et la recherche d'information sur le Web ainsi qu'une validation expérimentale des concepts introduits. Cette dernière est basée sur un cas d'étude réaliste pour la recherche d'articles scientifiques publiés à la conférence internationale World Wide Web

Experimental Assessment of the TARGET Adaptive Ontology-based Web Search Framework

International audienceFinding relevant information on the Web can be a complex task for most of the users. Although Web search applications are improving, they still need to be more intelligent to adapt to the search domain targeted by users, the evolution of this domain and users' characteristics. In this paper, we present an experimental assessment of the TARGET framework for improving the relevance of the documents when users are searching the Web by using adaptive ontologies. This is done first by introducing the TARGET approach. We will briefly present the used ontologies and their ability to adapt the domain evolution. Then, we detail the TARGET tool used in our experimentations. This includes its architecture, its ability to carry out the ontology adaptation process as well as the way it searches the Web and ranks the returned results. Finally, we discuss the results obtained using the tool through the presentation of our case study devoted to the retrieval of scientific articles