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Seeking support from their environment is important for people suffering from a depression. People usually have different social networks to which they are attached with different ties. In this paper, a computational model is presented that describes the selection of network members for seeking support based on the strength of the tie to people in the network and personal characteristics. The model has been implemented in a simulation environment. Simulations of different scenarios show that specific personality traits and environmental settings indeed lead to a pattern of social disengagement or a preference for strong or weak tie support. A mathematical analysis proofs that such equilibria are indeed a consequence of the model. © 2009 IEEE.

Since a long time information Quality Assessment is a crucial issue in organizations, where it relates to the ability of the organization to adequately fulfil the needs and expectations of its customers and users (Batini et al. 2008). In this context the evaluation of data quality has been the main concern (Batini and Scannapieco 2006). © Springer-Verlag Berlin Heidelberg 2013.

Motivated by the ever increasing difficulties faced by laypeople in retrieving and digesting valid and relevant information to make health-centred decisions, the CLEF eHealth lab series has offered shared tasks to the community in the fields of Information Extraction (IE), management, and Information Retrieval (IR) since 2013. These tasks have attracted large participation and led to statistically significant improvements in processing quality. In 2021, CLEF eHealth is calling for participants to contribute to the following two tasks: Task 1 on IE focuses on IE from noisy text. Participants will identify and classify Named Entities in written ultrasonography reports, containing misspellings and inconsistencies, from a major public hospital in Argentina. Identified entities will then have to be classified, which can be very challenging as it requires to handle lexical variations. Task 2 is a novel extension of the most popular and established task on consumer health search (CHS), aiming at retrieving relevant, understandable, and credible information for patients and their next-of-kins. In this paper we describe recent advances in the fields of IE and IR, and the subsequent offerings of this years CLEF eHealth lab challenges.

In this paper, we provide an overview of the ninth annual edition of the CLEF eHealth evaluation lab. CLEF eHealth 2021 continues our evaluation resource building efforts around the easing and support of patients, their next-of-kins, health care professionals, and health scientists in understanding, accessing, and authoring electronic health information in a multilingual setting. The 2021 lab offered two tasks: Task 1 on multilingual Information Extraction (IE), this year extending to a corpus of Spanish radiology reports; and Task 2 on Consumer Health Search (CHS) that builds on the previous year’s Information Retrieval (IR) tasks. In total, 11 teams took part in these tasks (7 in Task 1 on IE and 4 in Task 2 on IR). Herein, we describe the resources created for these tasks and the evaluation methodology adopted, and we provide a brief summary of the participants of this year’s challenges as well as the results obtained. As in previous years, the organizers have made data, tools, and more specific overview papers associated with the lab tasks available for future research and development.

In recent years, deep learning models have been successfully applied to Information Retrieval (IR), mainly for assessing the topical relevance of documents with respect to queries. However, relevance is a multidimensional concept, which can be assessed based on several criteria, depending on the document type, the domain considered, the search task performed, etc. Given that recent advancements in deep neural networks enable several learning tasks to be solved simultaneously, in this paper we examine the possibility of modeling multidimensional relevance by jointly solving a retrieval task, to learn topical relevance, and a classification task, to learn additional relevance dimensions. To instantiate and evaluate the proposed model, we consider three query-independent relevance dimensions beyond topicality, i.e., readability, trustworthiness, and credibility. The reported findings show that the proposed joint modeling can improve the performance of the retrieval task.

In this paper, we provide an overview of the eight annual edition of the Conference and Labs of the Evaluation Forum (CLEF) eHealth evaluation lab. The Conference and Labs of the Evaluation Forum (CLEF) eHealth 2020 continues our development of evaluation tasks and resources since 2012 to address laypeople’s difficulties to retrieve and digest valid and relevant information in their preferred language to make health-centred decisions. This year’s lab advertised two tasks. Task 1 on Information Extraction (IE) was new and focused on automatic clinical coding of diagnosis and procedure the tenth revision of the International Statistical Classification of Diseases and Related Health Problems (ICD10) codes as well as finding the corresponding evidence text snippets for clinical case documents in Spanish. Task 2 on Information Retrieval (IR) was a novel extension of the most popular and established task in the Conference and Labs of the Evaluation Forum (CLEF) eHealth on Consumer Health Search (CHS). In total 55 submissions were made to these tasks. Herein, we describe the resources created for the two tasks and evaluation methodology adopted. We also summarize lab submissions and results. As in previous years, the organizers have made data and tools associated with the lab tasks available for future research anddevelopment. The ongoing substantial community interest in the tasks and their resources has led to the Conference and Labs of the Evaluation Forum (CLEF) eHealth maturing as a primary venue for all interdisciplinary actors of the ecosystem for producing, processing, and consuming electronic healthinformation.

The second WEPIR 2019 workshop brings together researchers with different backgrounds interested in continuing to explore and advance the evaluation of personalisation in information retrieval. The workshop builds on the first WEPIR workshop held at CHIIR 2018, and will focus on further developing a common understanding of the challenges, requirements and practical limitations of meaningful evaluation of personalisation in information retrieval. In particular, the planned outcome of the workshop is to progress the work from WEPIR 2018 towards the development of concrete proposals for novel and innovative methodologies to support evaluation of personalised information retrieval from both the perspectives of the user experience in interactive search settings, and of user models for personalised information retrieval and their algorithmic incorporation in the search process.

The Social Web represents nowadays the principal means to support and foster social interactions among people through Web 2.0 technologies. Individuals interact in virtual communities to pursue mutual interests or goals, by exchanging multiple kinds of contents (i.e., textual, acoustic, visual), the so-called User-Generated Content (UGC). In this context, the SeCredISData Special Session is especially devoted at discussing the implications that the analysis of big social data has in tackling open issues related to society from different perspectives. On one side, there is the need to push forward the research on emotion and sentiment, and the investigation of affective cognitive models and their possible integration into intelligent systems. On the other side, it is urgent to address the issue of on-line information credibility assessment, in an era where trusted intermediaries have disappeared and people must rely only on their cognitive capacities to judge information. The Special Session is therefore aimed at promoting the development of models and applications able to tackle these issues.

In this paper, we analyze corporate e-mail messages as a medium to convey work tasks. Research indicates that categorization of e-mail could alleviate the common problem of information overload. Although e-mail clients provide possibilities of e-mail categorization, not many users spend effort on proper e-mail management. Since e-mail clients are often used for task management, we argue that intent- and task-based categorizations might be what is missing from current systems. We propose a taxonomy of tasks that are expressed through e-mail messages. With this taxonomy, we manually annotated two e-mail datasets (Enron and Avocado), and evaluated the validity of the dimensions in the taxonomy. Furthermore, we investigated the potential for automatic e-mail classification in a machine learning experiment. We found that approximately half of the corporate e-mail messages contain at least one task, mostly informational or procedural in nature. We show that automatic detection of the number of tasks in an e-mail message is possible with 71% accuracy. One important finding is that it is possible to use the e-mails from one company to train a classifier to classify e-mails from another company. Detecting how many tasks a message contains, whether a reply is expected, or what the spatial and time sensitivity of such a task is, can help in providing a more detailed priority estimation of the message for the recipient. Such a priority-based categorization can support knowledge workers in their battle against e-mail overload. © 2016 Elsevier Inc. All rights reserved.

The purpose of the WEPIR 2018 workshop is to bring together researchers from different backgrounds, interested in advancing the evaluation of personalisation in information retrieval. The workshop focus is on the development of a common understanding of the challenges, requirements and practical limitations of meaningful evaluation of personalisation in information retrieval. The planned outcome of the workshop is the proposal of methodologies to support evaluation of personalised information retrieval from both the perspectives of the user experience in interactive search settings, and of user models for personalised information retrieval and their algorithmic incorporation in the search process.

Government documents must be reviewed to identify and protect any sensitive information, such as personal information, before the documents can be released to the public. However, in the era of digital government documents, such as e-mail, traditional sensitivity review procedures are no longer practical, for example due to the volume of documents to be reviewed. Therefore, there is a need for new technology assisted review protocols to integrate automatic sensitivity classification into the sensitivity review process. Moreover, to effectively assist sensitivity review, such assistive technologies must incorporate reviewer feedback to enable sensitivity classifiers to quickly learn and adapt to the sensitivities within a collection, when the types of sensitivity are not known a priori. In this work, we present a thorough evaluation of active learning strategies for sensitivity review. Moreover, we present an active learning strategy that integrates reviewer feedback, from sensitive text annotations, to identify features of sensitivity that enable us to learn an effective sensitivity classifier (0.7 Balanced Accuracy) using significantly less reviewer effort, according to the sign test (p < 0.01 ). Moreover, this approach results in a 51% reduction in the number of documents required to be reviewed to achieve the same level of classification accuracy, compared to when the approach is deployed without annotation features.

A meme, as defined by Richard Dawkins, is a unit of information, a concept or an idea that spreads from person to person within a culture. Examples of memes can be a musical melody, a catchy phrase, trending news, behavioral patterns, etc. In this article the task of identifying potential memes in a stream of texts is addressed: in particular, the content generated by users of Social Media is considered as a rich source of information offering an updated window on the world happenings and on opinions of people. A textual electronic meme, a.k.a. ememe, is here considered as a frequently replicated set of related words that propagates through the Web over time. In this article an approach is proposed that aims to identify ememes in Social Media streams represented as graph of words. Furthermore, a set of measures is defined to track the change of information in time.

For the evaluation of diversified search results, a number of different methods have been proposed in the literature. Prior to making use of such evaluation methods, it is important to have a good understanding of how diversity and relevance contribute to the performance metric of each method. In this paper, we use the statistical technique ANOVA to analyse and compare three representative evaluation methods for diversified search, namely \(\alpha \)-nDCG, MAP-IA, and ERR-IA, on the TREC-2009 Web track dataset. It is shown that the performance scores provided by those evaluation methods can indeed reflect two crucial aspects of diversity — richness and evenness — as well as relevance, though to different degrees.

In this paper the task of Information Foraging (IF) is considered as a useful paradigm to address Exploratory Search. In the context of IF, a Web navigation strategy is introduced and formalized, and a multi-agent based model is proposed to exploit a collaborative approach to Information Foraging. A system based on this model has been developed, and its evaluations on the ACM and DBLP repositories are reported. Two datasets with different sizes were considered to show the effectiveness and the efficiency of the developed system. Furthermore, comparative evaluations were conducted in order to compare our approach with classical information access approaches. The results are promising and show the ability of the proposed Web Information Foraging system to find relevant Web pages in a very short time.

User preference discovery aims to detect the patterns of user preferences for various topics of interest or items such as movie genre or category. Preferences discovery is a crucial stage in the development of intelligent personalization systems. Although a variety of studies have been proposed in the literature addressing a wide range of applications such as recommender systems or personalized search, only a few of them have considered the management of imprecision in the representation of user and item features. This paper aims to address the above issue by using fuzzy sets. The paper proposes a general framework for preferences discovery through fuzzy sets and fuzzy models and it introduces a new algorithm for representing and discovering fuzzy user interest profile. Based on the results of the empirical evaluation, the proposed approach outperforms two well-known recommendation approaches in terms of well-known quality assessment metrics, namely: discounted cumulative gain, precision, recall, as well as F1-measure.

In a ubiquitous scenario, people are typically confronted with context evolution and changing influences. This may create new needs and may condition the user perception of what is relevant information. Over the years, different approaches have been proposed to design personalized Recommender Systems (RS), but state-of-the-art approaches mostly assume a fixed representation of a user profile; the dynamicity of the user's interests (and the way of expressing them) while interacting with the environment is not considered. Aim of this work is to predict a user's preferences in the tourism domain, to provide personalized and context-aware recommendations. Therefore, we define a user profile model which expresses in a formal way the user's opinions with respect to a particular entity. In particular, the proposed approach formally models the user generated content (UGC) connected to a group of reviews (written by expert users) for each entity, and compares it with a (positive and negative) statistical language model representing the target user profile associated with that entity. The effectiveness of the approach is illustrated on a real-case scenario.

This installment of Trends & Controversies provides an array of perspectives on the latest research in behavior informatics. Longbing Cao introduces the work in "Behavior Informatics: A New Perspective." Then, in "Behavior Computing," Longbing Cao and Thorsten Joachims provide a basic overview of the topic. Next is "Coupled Behavior Representation, Modeling, Analysis, and Reasoning" by Can Wang, Longbing Cao, Eric Gaussier, Jinjiu Li, Yuming Ou, and Dan Luo. The fourth article is "Behavior Analysis in Social Media," by Reza Zafarani and Huan Liu. The fifth article is "Group Recommendation and Behavior," by Guandong Xu and Zhiang Wu. Gabriella Pasi wrote the sixth article, "Web Search and Behavior." The seventh article, "Behaviors of IPTV Users," is by Ya Zhang, Xiaokang Yang, and Hongyuan Zha. Finally, "Should Behavioral Models of Terror Groups Be Disclosed?" is by Edoardo Serra and V.S. Subrahmanian.

Nowadays, review sites are more and more confronted with the spread of misinformation, i.e., opinion spam, which aims at promoting or damaging some target businesses, by misleading either human readers, or automated opinion mining and sentiment analysis systems. For this reason, in the last years, several data-driven approaches have been proposed to assess the credibility of user-generated content diffused through social media in the form of on-line reviews. Distinct approaches often consider different subsets of characteristics, i.e., features, connected to both reviews and reviewers, as well as to the network structure linking distinct entities on the review-site in exam. This article aims at providing an analysis of the main review- and reviewer-centric features that have been proposed up to now in the literature to detect fake reviews, in particular from those approaches that employ supervised machine learning techniques. These solutions provide in general better results with respect to purely unsupervised approaches, which are often based on graph-based methods that consider relational ties in review sites. Furthermore, this work proposes and evaluates some additional new features that can be suitable to classify genuine and fake reviews. For this purpose, a supervised classifier based on Random Forests have been implemented, by considering both well-known and new features, and a large-scale labeled dataset from which all these features have been extracted. The good results obtained show the effectiveness of new features to detect in particular singleton fake reviews, and in general the utility of this study.

The Social Web supports and fosters social interactions by means of different social media, which allow the spread of the so called User-Generated Content (UGC). In this context, characterized by the absence of trusted third parties that verify the reliability of the sources and the believability of the content generated, the issue of assessing the credibility of the information diffused by means of social media is receiving increasing attention. In the literature, this issue has been mainly tackled as a classification problem; information is categorized into genuine and fake, usually by implementing or applying classifiers that consider multiple kinds of features (mainly textual and non-textual) to be evaluated in terms of credibility. In this article, unlike prior research, textual features are considered separately with respect to other kinds of features during the classification process. In particular, an Ensemble Method that combines the results produced by two text classifiers and the ones returned by another classifier acting on non-textual features is proposed. This allows to have better results with respect to the use of a single classifier on multiple features together. The effectiveness of the Ensemble Method has been assessed in the context of review sites, by means of a labeled dataset gathered from the Yelp.com site, where on-line reviews are already classified as recommended and not recommended.

In this paper an adaptive hierarchical fuzzy clustering algorithm is presented, named Hierarchical Data Divisive Soft Clustering (H2D-SC). The main novelty of the proposed algorithm is that it is a quality driven algorithm, since it dynamically evaluates a multi-dimensional quality measure of the clusters to drive the generation of the soft hierarchy. Specifically, it generates a hierarchy in which each node is split into a variable number of sub-nodes, determined by an innovative quality assessment of soft clusters, based on the evaluation of multiple dimensions such as the cluster's cohesion, its cardinality, its mass, and its fuzziness, as well as the partition's entropy. Clusters at the same hierarchical level share a minimum quality value: clusters in the lower levels of the hierarchy have a higher quality; this way more specific clusters (lower level clusters) have a higher quality than more general clusters (upper level clusters). Further, since the algorithm generates a soft partition, a document can belong to several sub-clusters with distinct membership degrees. The proposed algorithm is divisive, and it is based on a combination of a modified bisecting K-Means algorithm with a flat soft clustering algorithm used to partition each node. The paper describes the algorithm and its evaluation on two standard collections.

Web Search Engines provide a huge number of answers in response to a user query, many of which are not relevant, whereas some of the most relevant ones may not be found. In the literature several approaches have been proposed in order to help a user to find the information relevant to his/her real needs on the Web. To achieve this goal the individual Information Behavior can been analyzed to ’keep’ track of the user’s interests. Keeping information is a type of Information Behavior, and in several works researchers have referred to it as the study on what people do during a search on the Web. Generally, the user’s actions (e.g., how the user moves from one Web page to another, or her/his download of a document, etc.) are recorded in Web logs. This paper reports on research activities which aim to exploit the information extracted from Web logs (or query logs) in personalized user ontologies, with the objective to support the user in the process of discovering Web information relevant to her/his information needs. Personalized ontologies are used to improve the quality of Web search by applying two main techniques: query reformulation and re-ranking of query evaluation results. In this paper we analyze various methodologies presented in the literature aimed at using personalized ontologies, defined on the basis of the observation of Information Behaviour to help the user in finding relevant information.

Geographic data on the Web has intensely increased in recent years. As a consequence, several applications that allow the search of Geographic Information (GI) on the Web have emerged, such as geographic web services, geographic search engines, Web GIS, etc. Till recently, most of research on GI management on the Web was focused on the storage, handling and Web mapping of GI by relying on keywords based query for searching. Nevertheless, GI specific characteristics, and web-user needs, require the development of novel web search methodologies. This paper focuses on spatial web-querying, which, integrated in a Web GIS context, allows retrieving relevant layers of interest that meet user needs. A layer is composed of objects that pertain to the same semantic domain, for example, roads and bridges may compose a transportation network theme. By exploiting the semantic characterization of each layer, in order to retrieve them, we propose in this paper a querying approach empowered by the use of ontologies, which are currently at the heart of many applications of Knowledge Engineering, especially the Semantic Web. A web GIS application prototype, has been developed by using a variety of open GIS software; this prototype is dedicated to the touristic geographic urban domain, and it exemplifies the proposed approach.

In recent years, after the proposal of the Internet of Things and, later, of the Web of Things, context awareness is a central issue at various levels including devices, communications and applications. In this chapter we present an overview of the current state of research in autonomic communications by focusing on aspects of context awareness on which the emerging new paradigm of Context-Aware Autonomic Communication systems is based. As an example of application we also provide a synthetic analysis of the problem of contextual search and of the issues related to defining contextual search systems.

Innovative contribution to aggregated search.Innovative contribution to the definition of interfaces for visualizing search results.Graph-based visualization of multimedia search results.Multimodal access to media objects.User based evaluation. Nowadays, together with the increasing spread of the content available online, users' information needs have become more complex. To fulfill them, users strongly rely on Web search engines, but traditional ways of presenting search results are often unsatisfactory. In fact, Web pages carry information that exists in multiple media formats, such as text, audio, image and video objects. Vertical search engines and medium-specific search services do not provide users with an integrated view of search results. Furthermore, multiple media objects are in most of the cases highly semantically interlinked, but the connections between them are not sufficiently exploited to provide a further exploration of the retrieved objects. To address these issues, in this paper we propose a graph-based approach aimed at providing users with the possibility to dynamically visualize and explore a search result space built over a repository of multimedia documents containing interconnected multiple media objects. To do this, we represent the search result space via a graph-based data model, where both the retrieved multimedia documents and connected relevant media objects are considered. Media objects, among them, are connected via different kinds of similarity relationships, which depend on the low-level features and metadata taken into consideration to access the media objects. The approach and the connected visualization and exploration interface have been implemented and tested on a publicly available dataset, and they have been evaluated by means of a usability test.

News blogs provide valuable insights into various news events and serve as "opinion pieces" with respect to the different happenings around the world. This demonstration paper presents a visual analytics system for these blogs, the system aims to facilitate news consumers by clustering the various blogposts along different topical dimensions. Moreover, the predictive module within the system provides an indication of how likely it is for topics in a particular "opinion piece" to resurface on the blog section of the particular news website and this helps in identification of the crucial stories worth keeping an eye on.

A flexible model for evaluating soft query with unequal preferences in fuzzy databases is proposed. We assume that conditions with unequal preferences have an exclusive meaning like in the request “find a holiday accommodation such that big apartments are preferred to high rating hotels.” In this case it is assumed that the aggregator of the soft conditions is an implicit OR. Conversely, conditions with unequal importance have an inclusive meaning, like in the query “find a house to rent that is cheap (most important), big (important), new (fairly important).” In this case the implicit aggregator is an AND. What we propose in this article is to model preferences as modifiers of the semantics of the evaluation function of the conditions. Because the soft conditions are aggregated by an OR, the more a soft condition is preferred, the more its evaluation function tolerates a greater undersatisfaction of the soft condition. The proposed approach is formalized by considering two alternative semantics of the evaluation function: the first semantics defines the evaluation function by means of a generalized fuzzy inclusion measure, and the second one as a generalized similarity measure. These functions are parameterized so that their modification is simply achieved by tuning the functions' parameters. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 665–689, 2007.

The present work is related to Web intelligence and more precisely to medical information foraging. We present here a novel approach based on agents technology for information foraging. An architecture is proposed, in which we distinguish two important phases. The first one is a learning process for localizing the most relevant pages that might interest the user. This is performed on a fixed instance of the Web. The second takes into account the openness and the dynamicity of the Web. It consists on an incremental learning starting from the result of the first phase and reshaping the outcomes taking into account the changes that undergoes the Web. The whole system offers a tool to help the user undertaking information foraging. We implemented the system using a group of cooperative reactive agents and more precisely a colony of artificial bees. In order to validate our proposal, experiments were conducted on MedlinePlus, a benchmark dedicated for research in the domain of Health. The results are promising either for those related to Web regularities and for the response time, which is very short and hence complies the real time constraint.

Relevance is a central notion in Information Retrieval, but it is considered to be a difficult concept to define. We analyse brain signals for the first 800 milliseconds (ms) of a relevance assessment process to answer the question "when relevance is happening in the brain?" with the belief that it will lead to better operational definitions of relevance. For this purpose, we devised a user study in which we captured the brain response of 20 participants. Using a 64-channel EEG device, we measured the electrophysiological activity of the brain while the subjects were in the phase of giving an explicit judgement about the relevance of presented images according to a given topic. Analyses were then performed over different time windows of the recorded EEG signals using repeated measures ANOVA. Data reveal significant variation between relevance and non-relevance within the EEG signals from the presentation of the image to 800 milliseconds afterwards. At an early stage these differences were located at frontal and posterior electrode sites. However, at later stages these differences were located in central, centro-parietal and centro-frontal areas. Our findings are an important step towards (i) a better understanding of the concept of relevance and (ii) a more effective implicit feedback systems.

We present in this paper a novel approach based on multi-agent technology for Web information foraging. We proposed for this purpose an architecture in which we distinguish two important phases. The first one is a learning process for localizing the most relevant pages that might interest the user. This is performed on a fixed instance of the Web. The second takes into account the openness and dynamicity of the Web. It consists on an incremental learning starting from the result of the first phase and reshaping the outcomes taking into account the changes that undergoes the Web. The system was implemented using a colony of artificial ants hybridized with tabu search in order to achieve more effectiveness and efficiency. To validate our proposal, experiments were conducted on MedlinePlus, a real website dedicated for research in the domain of Health in contrast to other previous works where experiments were performed on web logs datasets. The main results are promising either for those related to strong Web regularities and for the response time, which is very short and hence complies the real time constraint.

Among the existing global optimization algorithms, Particle Swarm Optimization (PSO) is one of the most effective when dealing with non-linear and complex high-dimensional problems. However, the performance of PSO is strongly dependent on the choice of its settings. In this work we propose a novel and self-tuning PSO algorithm - called Proactive Particles in Swarm Optimization (PPSO) - which exploits Fuzzy Logic to calculate the best setting for the inertia, cognitive factor and social factor. Thanks to additional heuristics, PPSO automatically determines also the best setting for the swarm size and for the particles maximum velocity. PPSO significantly differs from other versions of PSO that exploit Fuzzy Logic, since specific settings are assigned to each particle according to its history, instead of being globally defined for the whole swarm. Thus, the novelty of PPSO is that particles gain a limited autonomous and proactive intelligence, instead of being simple reactive agents. Our results show that PPSO outperforms the standard PSO, both in terms of convergence speed and average quality of solutions, remarkably without the need for any user setting.

In this paper an ordinal Information Retrieval model is proposed, which is formalised within fuzzy set theory and is based on the notion of linguistic granules of information. Linguistic expressions are defined to represent and manage the importance of both the index terms as descriptors of the information items and the query terms (content selectors) as descriptors of users' needs. The advantage of this approach with respect to the (numeric) fuzzy IR models is that the query evaluation mechanism and the definition of the importance semantics are simplified.

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 are in need of automated methods for mining the social media repositories (in particular Twitter) to monitor the reputation of a particular entity. A quite significant research challenge related to the above issue is to disambiguate tweets with respect to entity names. To address this issue in this paper we use "context phrases" in a tweet and Wikipedia disambiguated articles for a particular entity in a random forest classifier. Furthermore, we also utilize the concept of "relatedness" between tweet and entity using the Wikipedia category-article structure that captures the amount of discussion present inside a tweet related to an entity. The experimental evaluations show a significant improvement over the baseline and comparable performance with other systems representing strong performance given that we restrict ourselves to features extracted from Wikipedia. © Springer International Publishing Switzerland 2014.

In this paper, a model for Group Decision Making is proposed and defined in a linguistic context. A multi-person multi-criteria decision problem is considered, in which a group of experts is involved in the evaluation of the performances of a set of alternatives with respect to a predefined set of criteria. The objective is to evaluate a consensual judgement and a consensus degree on each alternative. Both the experts' evaluations of the alternatives and the degree of consensus are expressed linguistically. A ''soft'' consensus degree referred to a fuzzy majority of the experts is proposed based on the concept of linguistic quantifier. The entire process is defined in a linguistic domain and modeled within fuzzy set theory of Ordered Weighted Average (OWA) operators

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.

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.

National audience; 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.

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.

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.

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.

A “softening” of the hard Boolean scheme for information retrieval is presented. In this approach, information retrieval is seen as a multicriteria decision-making activity in which the criteria to be satisfied by the potential solutions, i.e., the archived documents, are the requirements expressed in the query. the retrieval function is then an overall decision function evaluating the degree to which each potential solution satisfies a query consisting of information requirements aggregated by operators. Linguistic quantifiers and a connector dealing with primary and optional criteria are defined and introduced in the query language in order to specify the aggregation criteria of the single query requirements. These criteria make it possible for users to express queries in a simple and self-explanatory manner. In particular, linguistic quantifiers are defined which capture the intrinsic vagueness of information needs. © 1995 John Wiley & Sons, Inc.

Finding domain specific key terms/phrases from a given set of documents is a challenging task. A domain may be defined as an area of interest over a collection of documents which may not be explicitly defined but implicitly observable in those documents. When considering a collection of documents related to academic research, examples of key terms/phrases may be Information Retrieval", "Marine Biology", etc. In this paper a technique for extracting important key terms/phrases in a considered topical domain is proposed using external evidence from the titles of Wikipedia articles and the Wikipedia category graph. We performed some experiments over the document collection of Web sites of different post-graduate schools. Our preliminary evaluations show promising results for the detection of domain specific key terms/phrases from the given set of domain focused Web pages.

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).

To overcome the “one size fits all” behavior of most search engines, in recent years a great deal of research has addressed the problem of defining techniques aimed at tailoring the search outcome to the user context to the aim of improving the quality of search. The main idea is to produce context-dependent and user-tailored search results. Search tasks are subjective, and often complex; the user-system interaction based on keyword-based querying and on the presentation of search results as a list of web pages ordered according to their estimated relevance is often unsatisfactory. In this paper a short overview of the main issues related to contextual search are outlined.

From a user perspective, data and services provide a complementary view of an information source: data provide detailed information about specific needs, while services execute processes involving data and returning an informative result as well. For this reason, users need to perform aggregated searches to identify not only relevant data, but also services able to operate on them. At the current state of the art such aggregated search can be only manually performed by expert users, who first identify relevant data, and then identify existing relevant services. In this paper we propose a semantic approach to perform aggregated search of data and services. In particular, we define a technique that, on the basis of an ontological representation of both data and services related to a domain, supports the translation of a data query into a service discovery process. In order to evaluate our approach, we developed a prototype that combines a data integration system with a novel information retrieval-based Web Service discovery engine (XIRE). The results produced by a wide set of experiments show the effectiveness of our approach with respect to IR approaches, especially when Web Service descriptions are expressed by means of a heterogeneous terminology. © 2010 Elsevier B.V. All rights reserved.

This chapter is about majority modelling in the context of group (multi-expert) decision making, to the aim of defining a decision strategy which takes into account the individual opinions of the decision makers. The concept of majority plays in this context a key role: what is often needed is an overall opinion which synthesizes the opinions of the majority of the experts. The reduction of the individual experts' opinions into a representative value (which we call the majority opinion) is usually performed through an aggregation process. In this chapter we describe two distinct approaches to the definition and consequent computation of a majority opinion within fuzzy set theory, where majority can be expressed by a linguistic quantifier (such as most). We first consider the case where linguistic quantifiers are associated with aggregation operators; in this case a majority opinion is computed by aggregating the individual opinions. To model this semantics of linguistic quantifiers the Induced Ordered Weighted Averaging operators (IOWA) are used with a modified definition of their weighting vector. We then consider a second case where the concept of majority is modelled as a vague concept. Based on this interpretation a formalization of a fuzzy majority opinion as a fuzzy subset is described.

Strategies for multisource spatial data fusion have generally to cope with distinct kinds of uncertainty, related to both the trust of the information source, the imperfection of spatial data, and the vagueness of the fusion strategy itself. In this chapter we propose a consensual fusion method that allows to flexibly model several fusion strategies ranging from a risk-taking to a risk-adverse attitude, and capable to cope with both data imprecision and source reliability. Uncertainty and imprecision in spatial data are represented by associating a fuzzy value with each spatial unit. The fusion function models a consensual dynamics and is parameterized so as to consider a varying spatial neighborhood of the data to fuse. Moreover the fusion has a quantifier-guided nature, reflecting the concept of a fuzzy majority and works on imprecise values to compute an imprecise result. It is formalized by a generalized OWA operator defined in the paper for aggregating imprecise values with distinct importance. The consensual fusion works so that the greater the trust score of the source and its agreement with the other sources, the more influent (important) is the data from the source in determining the consensual values. Thus the obtained fused map is determined in each location by a distinct majority of the sources, those that locally are in agreement. In cases where the data are affected by uncertainty one can require to fuse them so as to compute a result affected by at most a given maximum uncertainty level.

This paper discusses and evaluates an agent model that is able to manipulate the visual attention of a human, in order to support naval crew. The agent model consists of four submodels, including a model to reason about a subject's attention. The model was evaluated based on a practical case study which was formally analysed and verified using automated checking tools. Results show how a human subject's attention is manipulated by adjusting luminance, based on assessment of the subject's attention. These first evaluations of the agent show a positive effect. © 2009 IEEE.

In the last decade, new approaches focused on modelling uncertainty over complex relational data have been developed. In this paper one of the most promising of such approaches, known as Probabilistic Relational Models (PRMs), has been investigated and extended in order to measure and include uncertainty over relationships. Our extension, called PRMs with Relational Uncertainty, has been evaluated on real-data for web document classification purposes. Experimental results shown the potentiality of the proposed methods of capturing the real “strength” of relationships and the capacity of including this information into the probability model.

In this paper an original soft hierarchical Fuzzy Clustering algorithm is proposed, named Hierarchical Hyper-spherical Divisive Fuzzy C-Means (H2D-FCM), with the following characteristics: it generates a "soft" hierarchy in which a document can belong to several child clusters of a node, and the clusters in the same hierarchical level are more specific (general) than the clusters in the upper (lower) level. The proposed algorithm is a divisive algorithm based on a modified bisective K-Means, applying a modified probabilistic Fuzzy C Means algorithm to divide each node into child-nodes. The algorithm determines the proper number of cluster to generate at the first level based on an entropy measure and decides if a node can be further split based on a "density" measure. The paper presents the algorithm and its evaluations on two standard collections. © 2009 IEEE.