Vincenzo Loia

P-FCM: a proximity-based fuzzy clustering for user-centered web applications

Abstract In last years, the Internet and the web have been evolved in an astonishing way. Standard web search services play an important role as useful tools for the Internet community even though they suffer from a certain difficulty. The web continues its growth, making the reliability of Internet-based information and retrieval systems more complex. Nevertheless there has been a substantial analysis of the gap between the expected information and the returned information, the work of web search engine is still very hard. There are different problems concerning web searching activity, one among these falls in the query phase. Each engine provide an interface which the user is forced to learn. Often, the searching process returns a huge list of answers that are irrelevant, unavailable, or outdated. The tediosity of querying, due to the fact the queries are too weak to cope with the user’s expressiveness, has stimulated the designers to enrich the human-system interaction with new searching metaphors. One of these is the searching of “similar” pages, as offered by Google, Yahoo and others. The idea is very good, since the similarity gives an easy and intuitive mechanism to express a complex relation. We believe that this approach could become more effective if the user can rely on major flexibility in expressing the similarity dependencies with respect the current and available possibilities. In this paper we introduce a novel method for considering and processing the user-driven similarity during web navigation. We define an extension of fuzzy C-means algorithm, namely proximity fuzzy C-means (P-FCM) incorporating a measure of similarity or dissimilarity as user’s feedback on the clusters. We present the theoretical framework of this extension and then we observe, through a suite of web-based experiments, how significant is the impact of user’s feedback during P-FCM functioning. These observations suggest that the P-FCM approach can offer a relatively simple way of improving the web page classification according with the user interaction with the search engine.

RSS-based e-learning recommendations exploiting fuzzy FCA for Knowledge Modeling

Nowadays, Web 2.0 focuses on user generated content, data sharing and collaboration activities. Formats like Really Simple Syndication (RSS) provide structured Web information, display changes in summary form and stay updated about news headlines of interest. This trend has also affected the e-learning domain, where RSS feeds demand for dynamic learning activities, enabling learners and teachers to access to new blog posts, to keep track of new shared media, to consult Learning Objects which meet their needs. This paper presents an approach to enrich personalized e-learning experiences with user-generated content, through a contextualized RSS-feeds fruition. The synergic exploitation of Knowledge Modeling and Formal Concept Analysis techniques enables the design and development of a system that supports learners in their learning activities by collecting, conceptualizing, classifying and providing updated information on specific topics coming from relevant information sources. An agent-based layer supervises the extraction and filtering of RSS feeds whose topics cover a specific educational domain.

OWL-FC: an upper ontology for semantic modeling of Fuzzy Control

This work introduces an OWL-based upper ontology, called OWL-FC (Ontology Web Language for Fuzzy Control), capable to support a semantic definition of Fuzzy Control. It focuses on the fuzzy rules representation by providing domain independent ontology, supporting interoperability and favoring domain ontologies re-usability. The main contribution is that OWL-FC exploits Fuzzy Logic in OWL to model vagueness and uncertainty of the real world. Moreover, OWL-FC enables automatic discovery and execution of fuzzy controllers, by means of context aware parameter setting: appropriate controllers can be activated, depending on the parameters proactively identified in the work environment. In fact, the semantic modeling of concepts allows the characterization of constraints and restrictions for the identification of the right matches between concepts and individuals. OWL-FC ontology provides a wide, semantic-based interoperability among different domain ontologies, through the specification of fuzzy concepts, independently by the application domain. Then, OWL-FC is coherent to the Semantic Web infrastructure and avoids inconsistencies in the ontology.

Customized Query Response for an Improved Web Search

Although search engines represent the main means to access on-line data, the increasing demand in terms of performance, precision and relevance in the information retrieval is too far from to being acceptable. The gap existing between the wanted information and the gathered information is often bound to hindrances of semantic rather than syntactic nature. The continuing growth of the Internet usage and contents makes difficult the information access, making the task of information retrieval highly critical.

Capturing Digest Emotions by Means of Fuzzy Linguistic Aggregation

Distilling sentiments and moods hidden in the written (natural) language is a challenging issue which attracts research and commercial interests, aimed at studying the users behavior on the Web and evaluating the public attitudes towards brands, social events, political actions. The understanding of the written language is a very complicated task: sentiments and opinions are concealed in the sentences, typically associated to adjectives and verbs; then the intrinsic meaning of some textual expressions is not amenable to rigid linguistic patterns. This work presents a framework for detecting sentiment and emotion from text. It exploits an affective model known as Hourglass of Emotions, a variant of Plutchik’s wheel of emotions. The model defines four affective dimensions, each one with some activation levels, called ‘sentic levels’ that represent an emotional state of mind and can be more or less intense, depending on where they are placed in the corresponding dimension. Our approach draws from the Computational Intelligence area to provide a conceptual setting to sentiment and emotion detection and processing. The novelty is the fuzzy linguistic modeling of the Hourglass of Emotions: dimensions are modeled as fuzzy linguistic variables, whose linguistic terms are the sentic levels (emotions). This linguistic modeling naturally enables the use of fuzzy linguistic aggregation operators (from Computing with Words paradigm), such as LOWA (Linguistic Ordered Weighted Averaging) that inherently accomplishes an aggregation of the emotions in order to get an emotional expression that synthesizes a set of emotions associated with different sentic levels and activation intensities. The whole process for the emotion detection and synthesis is described through its main tasks, from the text parsing up to emotions extraction, returning a predominant emotion, associated with each dimension of the Hourglass of Emotions. An ad-hoc ontology has been designed to integrate lexical information and relations, along with the Hourglass model.

Data-Information-Concept Continuum From a Text Mining Perspective

The recent Web panorama reveals a tangible proliferation of “social” data, in form of posts, opinions, feelings, experiences. Most of the available data is unstructured text, unsuitable to be processed by computers, especially due to ambiguity and vagueness of the natural language. Research developments highlight the difficulty in capturing semantics of terms, linguistic expressions, and sentences and their consequent representation as a finite concept. This article presents an open-minded overview of the Text Mining approaches, targeted at transforming unstructured textual data into explicit knowledge, with a special focus on the conceptualization, i.e., the concept identification by analysing syntactic and semantic relations among terms as well as the contextual surrounding information. Different knowledge granulation is described in a layered knowledge model, where the term, the information and the concept represent the basic knowledge granules that cover most Text Mining approaches, in an evolving knowledge continuum.

Text Mining Basics in Bioinformatics

Biomedical scientific literature is becoming a valuable information source that includes a huge amount of novel research findings. Nevertheless, the unstructured nature of the publications stresses the importance of extracting embedded information to support literature-based analysis enabling the development of applications, such as Information Retrieval, Document Classification, Summarization, and so forth. There is a need to integrate several methods addressing, for instance, linguistic analysis enabling the system to mine information from the text at different abstraction levels, at document level or sentence. This article provides an overview of the text-mining in Bioinformatics introducing methods, applications, existing solutions and, finally, pointing out what are the emerging challenges of this research area.