In this paper we analyze the effectiveness of using linguistic knowledge from coreference and anaphora resolution for improving the performance for supervised keyphrase extraction. In order to verify the impact of these features, we define a baseline keyphrase extraction system and evaluate its performance on a standard dataset using different machine learning algorithms. Then, we consider new sets of features by adding combinations of the linguistic features we propose and we evaluate the new performance of the system. We also use anaphora and coreference resolution to transform the documents, trying to simulate the cohesion process performed by the human mind. We found that our approach has a slightly positive impact on the performance of automatic keyphrase extraction, in particular when considering the ranking of the results.

Summary form only given. The invited talks are: How to Compare Alternative Architectures by Radia Perlman of Intel; Portals 4: Enabling Application/Architecture Co-Design for High-Performance Interconnects by Ron Brightwell of Sandia National Laboratories; and Electronic-Photonic Integration within Switches and Routers by Mike Watts of MIT. Brief author biographies are also included.

This paper presents a smart resource-aware multimedia sensor network. We illustrate a surveillance system which supports human operators, by automatically detecting the complex events and giving the possibility to recall the detected events and searching them in an intelligent search engine. Four subsystems have been implemented, the tracking and detection system, the network configuration system, the reasoning system and an advanced archiving system in an annotated multimedia database.

Collaborative tagging represents for the Web a potential way for organizing and sharing information and for heightening the capabilities of existing search engines. However, because of the lack of automatic methodologies for generating the tags and supporting the tagging activity, many resources on the Web are deficient in tag information, and recommending opportune tags is both a current open issue and an exciting challenge. This paper approaches the problem by applying a combined set of techniques and tools (that uses tags, domain ontologies, keyphrase extraction methods) thereby generating tags automatically. The proposed approach is implemented in the PIRATES (Personalized Intelligent tag Recommender and Annotator TEStbed) framework, a prototype system for personalized content retrieval, annotation, and classification. A case study application is developed using a domain ontology for software engineering. © 2010 Wiley Periodicals, Inc.

Web 2.0 applications innovate traditional informative services providing Web users with a set of tools for publishing and sharing information. Social bookmarking systems are an interesting example of this trend where users generate new contents. Unfortunately, the growing amount of available resources makes hard the task of accessing to relevant information in these environments. Recommender systems face this problem filtering relevant resources connected to users' interests and preferences. In particular, collaborative filtering recommender systems produce suggestions using the opinions of similar users, called the neighbors. The task of finding neighbors is difficult in environment such as social bookmarking systems, since bookmarked resources belong to different domains. In this paper we propose a methodology for partitioning users, tags and resources into domains of interest. Filtering tags and resources in accordance to the specific domains we can select a different set of neighbors for each domain, improving the accuracy of recommendations.

Abstract Although no unified definition of the concept of search strategy in Information Retrieval (IR) exists so far, its importance is manifest: nonexpert users, directly interacting with an IR system, apply a limited portfolio of simple actions; they do not know how to react in critical situations; and they often do not even realize that their difficulties are due to strategic problems. A user interface to an IR system should therefore provide some strategic help, focusing user's attention on strategic issues and providing tools to generate better strategies. Because neither the user nor the system can autonomously solve the information problem, but they complement each other, we propose a collaborative coaching approach, in which the two partners cooperate: the user retains the control of the session and the system provides suggestions. The effectiveness of the approach is demonstrated by a conceptual analysis, a prototype knowledge‐based system named FIRE, and its evaluation through informal laboratory experiments.

Article Evaluating user interfaces to information retrieval systems: a case study on user support Share on Authors: Giorgio Brajnik Dipartimento di Matematica e Informatica, University of Udine, Via delle Scienze, 206, Loc. Rizzi - 33100 Udine - ITALY Dipartimento di Matematica e Informatica, University of Udine, Via delle Scienze, 206, Loc. Rizzi - 33100 Udine - ITALYView Profile , Stefano Mizzaro Dipartimento di Matematica e Informatica, University of Udine, Via delle Scienze, 206, Loc. Rizzi - 33100 Udine - ITALY Dipartimento di Matematica e Informatica, University of Udine, Via delle Scienze, 206, Loc. Rizzi - 33100 Udine - ITALYView Profile , Carlo Tasso Dipartimento di Matematica e Informatica, University of Udine, Via delle Scienze, 206, Loc. Rizzi - 33100 Udine - ITALY Dipartimento di Matematica e Informatica, University of Udine, Via delle Scienze, 206, Loc. Rizzi - 33100 Udine - ITALYView Profile Authors Info & Claims SIGIR '96: Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrievalAugust 1996 Pages 128–136https://doi.org/10.1145/243199.243249Online:18 August 1996Publication History 46citation1,584DownloadsMetricsTotal Citations46Total Downloads1,584Last 12 Months16Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The basic concepts of the multimodeling approach to the representation of physical systems are presented. Emphasis is placed on the exploitation of many, diverse models of a system for the execution of complex problem solving tasks, such as interpretation, diagnosis, design, simulation, etc. The considered models are based on different ontologies, representational assumptions, epistemological types, and aggregation levels. After a brief survey of the techniques adopted for representing structural and behavioral knowledge, attention is focused on function and teleology. A novel approach is proposed for defining, representing, and using these two types of knowledge which play a fundamental role both from the representation and reasoning perspectives. The fundamental claim is that while teleological knowledge concerns the specific purposes for which the system has been designed, functional knowledge is devoted to bridge the gap between such abstract purposes and the actual structure and behavior of the system, through the concepts of phenomena, processes, and functional roles. A clear definition is provided of all the various epistemological and ontological links existing between the different models.>

The requirements of a user modeling component for an expert interface are analyzed, and the main points of a proposed approach to user modeling are stated. The authors focus on a knowledge-based system, called UM-tool, devoted to creating, maintaining, and using explicit user models within an expert interface. UM-tool supports a novel approach to user modeling, which is based both on the use of stereotypes and on a dynamic reclassification scheme. The architecture of the system is described, the organization and content of its knowledge bases are illustrated, and the modeling mechanisms utilized are presented in detail. An example of the use of UM-tool in the frame of the information-retrieval-natural-language-interface (IR-NLI II) expert interface devoted to supporting end users in accessing online information retrieval services is discussed, focusing on the specific role of the user modeling component. An evaluation of the proposed approach and a critical comparison with related work are presented. Future research directions are outlined.>

This paper addresses the problem of building expert interfaces to information retrieval systems. In particular, the problem of augmenting the capabilities of such interfaces with user modeling features is discussed and the main benefits of this approach are outlined. The paper presents a prototype system called IR-NLI II, devoted to model by means of artificial intelligence techniques the human intermediary to information retrieval systems. The overall organization of the IR-NLI II system is presented, together with a short description of the two main modules implemented so far, namely the Information Retrieval Expert Subsystem and the User Modeling Subsystem. An example of interaction with IR-NLI II is described. Perspectives and future research directions are finally outlined.