Information Seek and Retrieval Mechanisms Based on Interactive Dynamics Linking Technology

The paper discusses the approach to dynamic hypertext technology used for query expansion and development. Two schemes of links creation on the fly are presented. The first one, System controlled scheme, generates (computes) hypertext links on retrieved documents output phase. In the second one, User controlled scheme, terms in viewed document are highlighted (and can be further used as a query in selected resource by one click) in accordance with user profile. The link creation mechanism is specified parametrically through the specification of the resource and selected (i.e. controlled) by the user. Linking mechanism is considered as a component of IR&S process, well controlled by user thought graphic interface.     Keywords: Information Retrieval Interface; Dynamic Linking; Hypertext Technology; System-Human Information Processing; Dynamic Query Reformulation

Taxonomy of Usage Issues for Consumer-centric Online Health Information Provision

Consumers are increasingly using Internet portals when searching for relevant health information. Despite the broad range of health information portals (HIPs) available, usage problems with such portals are still widely recognized and reported. In this study, we analyzed usage data from an operational health information portal and identified ways in which these problems can be addressed. While previous usage data and log analysis research has focused more on user behaviors, query structures, and human-computer interaction issues, this study covers more comprehensive issues such as content. We describe a taxonomy of usage issues derived from a literature analysis. We describe how we validated and refined the taxonomy based on analyzing the usage data from an operational health portal. Findings from the usage data indicate that a range of content issues exist that lead to unsuccessful searches. The analysis also highlights that users’ ineffective information seeking strategies are not well supported by the system’s design. We use this taxonomy to propose a usage-driven, consumer-centered approach for dynamic improvements of HIPs. We also discuss the study’s limitations and directions for future research

Sharing Semantic Resources

The Semantic Web is an extension of the current Web in which information, so far created for human consumption, becomes machine readable, “enabling computers and people to work in cooperation”. To turn into reality this vision several challenges are still open among which the most important is to share meaning formally represented with ontologies or more generally with semantic resources. This Semantic Web long-term goal has many convergences with the activities in the field of Human Language Technology and in particular in the development of Natural Language Processing applications where there is a great need of multilingual lexical resources. For instance, one of the most important lexical resources, WordNet, is also commonly regarded and used as an ontology. Nowadays, another important phenomenon is represented by the explosion of social collaboration, and Wikipedia, the largest encyclopedia in the world, is object of research as an up to date omni comprehensive semantic resource. The main topic of this thesis is the management and exploitation of semantic resources in a collaborative way, trying to use the already available resources as Wikipedia and Wordnet. This work presents a general environment able to turn into reality the vision of shared and distributed semantic resources and describes a distributed three-layer architecture to enable a rapid prototyping of cooperative applications for developing semantic resources

Apprentissage de représentation pour des données générées par des utilisateurs

In this thesis, we study how representation learning methods can be applied to user-generated data. Our contributions cover three different applications but share a common denominator: the extraction of relevant user representations. Our first application is the item recommendation task, where recommender systems build user and item profiles out of past ratings reflecting user preferences and item characteristics. Nowadays, textual information is often together with ratings available and we propose to use it to enrich the profiles extracted from the ratings. Our hope is to extract from the textual content shared opinions and preferences. The models we propose provide another opportunity: predicting the text a user would write on an item. Our second application is sentiment analysis and, in particular, polarity classification. Our idea is that recommender systems can be used for such a task. Recommender systems and traditional polarity classifiers operate on different time scales. We propose two hybridizations of these models: the former has better classification performance, the latter highlights a vocabulary of surprise in the texts of the reviews. The third and final application we consider is urban mobility. It takes place beyond the frontiers of the Internet, in the physical world. Using authentication logs of the subway users, logging the time and station at which users take the subway, we show that it is possible to extract robust temporal profiles.Dans cette thèse, nous étudions comment les méthodes d'apprentissage de représentations peuvent être appliquées à des données générées par l'utilisateur. Nos contributions couvrent trois applications différentes, mais partagent un dénominateur commun: l'extraction des représentations d'utilisateurs concernés. Notre première application est la tâche de recommandation de produits, où les systèmes existant créent des profils utilisateurs et objets qui reflètent les préférences des premiers et les caractéristiques des derniers, en utilisant l'historique. De nos jours, un texte accompagne souvent cette note et nous proposons de l'utiliser pour enrichir les profils extraits. Notre espoir est d'en extraire une connaissance plus fine des goûts des utilisateurs. Nous pouvons, en utilisant ces modèles, prédire le texte qu'un utilisateur va écrire sur un objet. Notre deuxième application est l'analyse des sentiments et, en particulier, la classification de polarité. Notre idée est que les systèmes de recommandation peuvent être utilisés pour une telle tâche. Les systèmes de recommandation et classificateurs de polarité traditionnels fonctionnent sur différentes échelles de temps. Nous proposons deux hybridations de ces modèles: la première a de meilleures performances en classification, la seconde exhibe un vocabulaire de surprise. La troisième et dernière application que nous considérons est la mobilité urbaine. Elle a lieu au-delà des frontières d'Internet, dans le monde physique. Nous utilisons les journaux d'authentification des usagers du métro, enregistrant l'heure et la station d'origine des trajets, pour caractériser les utilisateurs par ses usages et habitudes temporelles

Benchmarking the performance of two automated term-extraction systems : LOGOS and ATAO

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.Pour consulter le document d'accompagnement du mémoire, veuillez contacter le Centre de conservation Lionel-Groulx de l'Université de Montréal ([email protected])

Intelligent Data Mining Techniques for Automatic Service Management

Today, as more and more industries are involved in the artificial intelligence era, all business enterprises constantly explore innovative ways to expand their outreach and fulfill the high requirements from customers, with the purpose of gaining a competitive advantage in the marketplace. However, the success of a business highly relies on its IT service. Value-creating activities of a business cannot be accomplished without solid and continuous delivery of IT services especially in the increasingly intricate and specialized world. Driven by both the growing complexity of IT environments and rapidly changing business needs, service providers are urgently seeking intelligent data mining and machine learning techniques to build a cognitive ``brain in IT service management, capable of automatically understanding, reasoning and learning from operational data collected from human engineers and virtual engineers during the IT service maintenance. The ultimate goal of IT service management optimization is to maximize the automation of IT routine procedures such as problem detection, determination, and resolution. However, to fully automate the entire IT routine procedure is still a challenging task without any human intervention. In the real IT system, both the step-wise resolution descriptions and scripted resolutions are often logged with their corresponding problematic incidents, which typically contain abundant valuable human domain knowledge. Hence, modeling, gathering and utilizing the domain knowledge from IT system maintenance logs act as an extremely crucial role in IT service management optimization. To optimize the IT service management from the perspective of intelligent data mining techniques, three research directions are identified and considered to be greatly helpful for automatic service management: (1) efficiently extract and organize the domain knowledge from IT system maintenance logs; (2) online collect and update the existing domain knowledge by interactively recommending the possible resolutions; (3) automatically discover the latent relation among scripted resolutions and intelligently suggest proper scripted resolutions for IT problems. My dissertation addresses these challenges mentioned above by designing and implementing a set of intelligent data-driven solutions including (1) constructing the domain knowledge base for problem resolution inference; (2) online recommending resolution in light of the explicit hierarchical resolution categories provided by domain experts; and (3) interactively recommending resolution with the latent resolution relations learned through a collaborative filtering model

HathiTrust Research Center: Computational Research on the HathiTrust Repository

PIs (exec mgt team): Beth A. Plale, Indiana University; Marshall Scott Poole, University of Illinois Urbana-Champaign ; Robert McDonald, IU; John Unsworth (UIUC) Senior investigators: Loretta Auvil (UIUC); Johan Bollen (IU), Randy Butler (UIUC); Dennis Cromwell (IU), Geoffrey Fox (IU), Eileen Julien (IU), Stacy Kowalczyk (IU); Danny Powell (UIUC); Beth Sandore (UIUC); Craig Stewart (IU); John Towns (UIUC); Carolyn Walters (IU), Michael Welge (UIUC); Eric Wernert (IU

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges