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A correct initial assessment of disaster consequences is crucial for an adequate decision-making in disaster and emergency management. However, such an initial assessment needs to be correct, but not necessarily fully precise, and thus it can be associated with a fuzzy classification problem in which the set of classes presents a relevant structure. This paper proposes the consideration of a dissimilarity operator in order to introduce such a structure in the classifier's learning and reasoning procedures, leading to an improvement in the classifiers adaptation to the disaster management context features and decision making requirements.

Traditional search engines fail to capture the notion of "perspective" in their search results and at times present the results skewed towards a particular topic. Under most of these cases even query reformulation fails to retrieve desired search results and the underlying reason for such failure is often the bias within the document collection itself (e.g., news articles). A perspective-aware search interface enabling users to look into search results for some "perspective" terms may be of great use for certain information needs. In this paper we describe such a system

Recommender systems rely on the opinions of a community of users to provide "recommendations" that can help users of the same community in discerning content of interest from a wide range of possibilities. Particularly, collaborative information filtering represents one of techniques widely exploited by recommender systems to suggest which items better meet the user needs and preferences. This paper introduces a model for collaborative filtering based on Formal Concept Analysis, a theoretical framework suitable to generate correlations among data through a lattice design. In particular, a fuzzy annotation of the lattice allows discovering similarities among items as well as users, arranged as a ranked list. Copyright 2013 ACM.

The aim of this paper is to evaluate the effectiveness of a categorization approach of search results based on the use of domain ontologies with respect to the application of standard single-label and multi-label classification algorithms. In particular, the approach in (Calegari et al., 2011) is considered, where the categorization process is performed thanks to the adoption of a taxonomy of information represented as a granular view of a domain ontology. Copyright ©2013 SCITEPRESS.

As of today, opinion mining has been widely used to identify the strength and weakness of products (e.g., cameras) or services (e.g., services in medical clinics or hospitals) based upon people's feedback such as user reviews. Feature extraction is a crucial step for opinion mining which has been used to collect useful information from user reviews. Most existing approaches only find individual features of a product without the structural relationships between the features which usually exists. In this paper, we propose an approach to extract features and feature relationship, represented as tree structure called a feature hierarchy, based on frequent patterns and associations between patterns derived from user reviews. The generated feature hierarchy profiles the product at multiple levels and provides more detailed information about the product. Our experiment results based on some popularly used review datasets show that the proposed feature extraction approach can identify more correct features than the baseline model. Even though the datasets used in the experiment are about cameras, our work can be applied to generate features about a service such as the services in hospitals or clinics. © Springer-Verlag 2013.

In this paper a new graph-based model is proposed for the representation of textual documents. Graph-structures are obtained from textual documents by making use of the well-known Part-Of-Speech (POS) tagging technique. More specifically, a simple rule-based (re)classifier is used to map each tag onto graph vertices and edges. As a result, a decomposition of textual documents is obtained where tokens are automatically parsed and attached to either a vertex or an edge. It is shown how textual documents can be aggregated through their graph-structures and finally, it is shown how vertex-ranking methods can be used to find relevant tokens.1 © 2013. The authors-Published by Atlantis Press.

Personalized Web search offers a promising solution to the task of user-tailored information-seeking; however, one of the reasons why it is not widely adopted by users is due to privacy concerns. Over the past few years social networking services (SNS) have re-shaped the traditional paradigm of information-seeking. People now tend to simultaneously make use of both Web search engines and social networking services when faced with an information need. In this paper, using data gathered in a user survey, we present an analysis of the correlation between the users' willingness to personalize Web search and their social network usage patterns. The participants' responses to the survey questions enabled us to use a regression model for identifying the relationship between SNS variables and willingness to personalize Web search. We also performed a follow-up user survey for use in a support vector machine (SVM) based prediction framework. The prediction results lead to the observation that SNS features such as a user's demographic factors (such as age, gender, location), a user's presence or absence on Twitter and Google+, amount of activity on Twitter and Google+ along with the user's tendency to ask questions on social networks are significant predictors in characterising users who would be willing to opt for personalized Web search results. © 2013 Springer-Verlag Berlin Heidelberg.

Social media repositories serve as a significant source of evidence when extracting information related to the reputation of a particular entity (e.g., a particular politician, singer or company). Reputation management experts manually mine the social media repositories (in particular Twitter) for monitoring the reputation of a particular entity. Recently, the online reputation management evaluation campaign known as RepLab at CLEF has turned attention to devising computational methods for facilitating reputation management experts. A quite significant research challenge related to the above issue is to disambiguate tweets with respect to entity names. In fact, finding if a particular tweet is relevant or irrelevant to a particular entity is an important task not satisfactorily solved yet; to address this issue in this paper we use \context phrases" in a tweet and Wikipedia disambiguated articles for a particular entity in an SVM classifier that utilizes features extracted from the Wikipedia graph structure i.e., links into Wikipedia articles and links from Wikipedia articles. Additionally we also use features derived from term-specificity and term-collocation features derived from the Wikipedia article of an entity under investigation. The experimental evaluations do not show a significant improvement over the baseline and other systems outperform our approach; however, manual inspection of feature sets and training data demonstrates the proposed Wikipedia graph-based features may show a promising outcome when used in combination with sophisticated learning algorithms

The aim of this research is to easily monitor the reputation of a company in the Twittersphere. We propose a strategy that organizes a stream of tweets into different clusters based on the tweets' topics. Furthermore, the obtained clusters are assigned into different priority levels. A cluster with high priority represents a topic which may affect the reputation of a company, and that consequently deserves immediate attention. The evaluation results show that our method is competitive even though the method does not make use of any external knowledge resource. © 2013 Springer-Verlag.

XML is the most used language for structuring data and documents, besides being the de-facto standard for data exchange. Keyword based search has been implemented by the XQuery Full-Text language extension, allowing document fragments to be retrieved and ranked via keyword-based matching in the Information Retrieval style. In this demo the implementation of an XQuery extension allowing users to express their vague knowledge of the underlying XML structure is presented. The integration has been performed on top of the BaseX query engine; the work, as initially done in [6] as a proof-of-concept has been further enhanced and extended

Several interpretations can be given to the third truth value in three-valued logics. Here, we consider the case when it refers to the epistemic notion of contradictory, or both true and false at the same time. We study several paraconsistent three-valued logics that carry this concern and show that they can be translated into a fragment of a simple epistemic logic where modalities can only appear in front of literals. This logic is unusual in the sense that necessity modalities distribute over disjunctions instead of conjunctions. An equivalent translation into a fragment of KD modal logic can be obtained by exchanging the role of possibility and necessity modalities, highlighting the perfect symmetry between three-valued logics of contradiction and three-valued logics of incomplete information.

The paper addresses the problem of e-memes (electronic memes) representation and identification in the blogosphere, and it aims at defining the basis of a new conceptual model. First, an ememe is formally defined as a conceptual information granule by means of an OWL schema; then a process is proposed to retrieve from the blogosphere the posts dealing with the ememe. The proposed process extracts from the formal definition of the ememe some keyword-based queries that are submitted to a search engine to retrieve posts that are potentially related to the ememe. The post contents are then filtered by evaluating their fidelity to the ememe conceptual definition by applying a lexicographic analysis and part of speech tagging. Ordered Weighted Averaging (OWA) operators are used to this purpose. Measures of mutation, spread, and longevity of an ememe are also proposed. © 2013 IEEE.

Our journey in the land of Fuzzy Logic started in 1990, at the Italian National Council of Research (CNR), where we were young researchers, who, coming from research experiences in distinct groups, were excited at the idea to start an independent and challenging research activity. We came across the world of Information Retrieval (before Web search engines' birth) not by chance, but because we were both working at a project aimed at defining and implementing an IR system (IRS) for managing the research papers produced by the researchers of CNR

Since typical healthcare consumers may lack sufficient knowledge to evaluate the reliability of health-related contents published online, recent researches are addressing the usefulness of Web page evaluation tools to help these consumers assess the quality of the indications they retrieve online. This paper contributes in this line by proposing an intentionally simple composite index of information quality, the so called Medical Information Reliability (MIR) index. This index takes the attitudes of potential and actual consumers toward information quality into account, and it is intended to be applied to online sources of medical information as "trust indicator" to provide their potential consumers with a simple percentage score by which to evaluate the reliability of what they are consulting. The main idea underlying this index is to consider information quality a multidimensional aspect of an online resource and relate it to the extent such a resource is compliant with explicit requirements formulated by third-party endorsement bodies. The method to calculate the MIR index on a sample of medical sites is presented in a step-by-step manner, and a user study is discussed that validated its application to the domain of the Alternative and Complementary Medicine.

Personalized search is aimed at tailoring the search outcome to users; to this aim user profiles play an important role: the more faithfully a user profile represents the user interests and preferences, the higher is the probability to improve the search process. In the approaches proposed in the literature, user profiles are formally represented as bags of words, as vectors, or as conceptual taxonomies, generally defined based on external knowledge resources (such as the WordNet and the ODP - Open Directory Project). Ontologies have been more recently considered as a powerful expressive means for knowledge representation. The advantage offered by ontological languages is that they allow a more structured and expressive knowledge representation with respect to the above mentioned approaches. A challenging research activity consists in defining user profiles by a knowledge extraction process from an existing ontology, with the main aim of producing a semantically rich representation of the user interests. In this paper a method to automatically define a personal ontology via a knowledge extraction process from the general purpose ontology YAGO is presented; starting from a set of keywords, which are representatives of the user interests, the process is aimed to define a structured and semantically coherent representation of the user topical interests. In the paper the proposed method is described, as well as some evaluations that show its effectiveness. © 2012 Elsevier Ltd. All rights reserved.

The proceedings contain 38 papers. The topics discussed include: hypothesis generation by interactive visual exploration of heterogeneous medical data; designing computer-based clinical guidelines decision support by a clinician; opinion mining on the web 2.0 - characteristics of user generated content and their impacts; using hasse diagrams for competence-oriented learning analytics; towards the detection of deception in interactive multimedia environments; predictive sentiment analysis of tweets: a stock market application; intent recognition using neural networks and kalman filters; an interactive course analyzer for improving learning styles support level; a framework for automatic identification and visualization of mobile device functionalities and usage; crowdsourcing fact extraction from scientific literature; digital archives: semantic search and retrieval; inconsistency knowledge discovery for longitudinal data management: a model-based approach; and random forests for feature selection in non-invasive brain-computer interfacing.

The proceedings contain 24 papers. The topics discussed include: what can fuzzy cluster analysis contribute to clustering of high-dimensional data?; feature selection based on fuzzy mutual information; learning membership functions for fuzzy sets through modified support vector clustering; using fuzzy multilayer perceptrons for the classification of time series; imputation of possibilistic data for structural learning of directed acyclic graphs; adding real coefficients to Lukasiewicz logic: an application to neural networks; possibilistic graphical models: from reasoning to decision making; ranking triangular fuzzy numbers using fuzzy set inclusion index; probability- possibility transformation: application to Bayesian and possibilistic networks; evaluation and ranking of intuitionistic fuzzy quantities; approximation of fuzzy measures using second order measures: estimation of andness bounds; and model-based image interpretation under uncertainty and fuzziness.

News blog hot topics are important for the information recommendation service and marketing. However, information overload and personalized management make the information arrangement more difficult. Moreover, what influences the formation and development of blog hot topics is seldom paid attention to. In order to correctly detect news blog hot topics, the paper first analyzes the development of topics in a new perspective based on W2T (Wisdom Web of Things) methodology. Namely, the characteristics of blog users, context of topic propagation and information granularity are unified to analyze the related problems. Some factors such as the user behavior pattern, network opinion and opinion leader are subsequently identified to be important for the development of topics. Then the topic model based on the view of event reports is constructed. At last, hot topics are identified by the duration, topic novelty, degree of topic growth and degree of user attention. The experimental results show that the proposed method is feasible and effective.

Patent retrieval is a complex challenging task; despite of the numerous patent search applications, more flexible solutions are needed to help users to easily identify relevant patents. This paper presents the PatentLight search tool that offers novel and flexible functionalities to help users looking for relevant patents represented in the XML format. Copyright © 2012 ACM.

In this paper we address the problem of e-memes (electronic memes) representation and identification in the blogosphere by a new conceptual model. We first formally define an ememe by means of an OWL schema, then a process is proposed to retrieve from the blogospheres the posts dealing with the ememe. Some measures are proposed to characterize the identified ememe instances such as their replication fidelity, mutation, spread, and longevity. © 2012 IEEE

This paper presents a formal definition of an extension of the XQuery Full-Text language: the proposed extension consists in adding two new flexible axes, named below and near, which express structural constraints that can be specified by the user. Both constraints are evaluated in an approximate way with respect to a considered path, and their evaluation produces a path relevance score for each retrieved element. The formal syntax and the semantics of the two new axis are presented and discussed.

It is often the case that traditional media provide coverage of a news event on the basis of journalists' viewpoints - a problem termed in the literature as media bias. On the other hand social media have given birth to an alternative paradigm of journalism known as "citizen journalism". We take advantage of citizen journalism to detect the bias in traditional media and propose a simple model for empirical measurement of media bias. Copyright is held by the author/owner(s).

The aim of this contribution is to easily monitor the reputation of a company in the Twittersphere.We propose a strategy that organizes a stream of tweets into different clusters based on the tweets topics. Furthermore, the obtained clusters are prioritized into different priority levels. A cluster with high priority represents a topic which may affect the reputation of a company, and that consequently deserves immediate attention. The evaluation results show that our method is competitive even though the method does not make use of any external knowledge resource.