Sabrina Senatore

Hierarchical web resources retrieval by exploiting Fuzzy Formal Concept Analysis

In recent years, knowledge structuring is assuming important roles in several real world applications such as decision support, cooperative problem solving, e-commerce, Semantic Web and, even in planning systems. Ontologies play an important role in supporting automated processes to access information and are at the core of new strategies for the development of knowledge-based systems. Yet, developing an ontology is a time-consuming task which often needs an accurate domain expertise to tackle structural and logical difficulties in the definition of concepts as well as conceivable relationships. This work presents an ontology-based retrieval approach, that supports data organization and visualization and provides a friendly navigation model. It exploits the fuzzy extension of the Formal Concept Analysis theory to elicit conceptualizations from datasets and generate a hierarchy-based representation of extracted knowledge. An intuitive graphical interface provides a multi-facets view of the built ontology. Through a transparent query-based retrieval, final users navigate across concepts, relations and population.

P-FCM: a proximity—based fuzzy clustering

Abstract In this study, we introduce and study a proximity-based fuzzy clustering. As the name stipulates, in this mode of clustering, a structure “discovery” in the data is realized in an unsupervised manner and becomes augmented by a certain auxiliary supervision mechanism. The supervision mechanism introduced in this algorithm is realized via a number of proximity “hints” (constraints) that specify an extent to which some pairs of patterns are regarded similar or different. They are provided externally to the clustering algorithm and help in the navigation of the search through the set of patterns and this gives rise to a two-phase optimization process. Its first phase is the standard FCM while the second step is concerned with the gradient-driven minimization of the differences between the provided proximity values and those computed on a basis of the partition matrix computed at the first phase of the algorithm. The proximity type of auxiliary information is discussed in the context of Web mining where clusters of Web pages are built in presence of some proximity information provided by a user who assesses (assigns) these degrees on a basis of some personal preferences. Numeric studies involve experiments with several synthetic data and Web data (pages).

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.

Fuzzy Clustering With Viewpoints

In this study, we introduce a certain knowledge-guided scheme of fuzzy clustering in which domain knowledge is represented in the form of so-called viewpoints. Viewpoints capture a way in which the user introduces his/her point of view at the data by identifying some representatives, which, being treated as externally introduced prototypes, have to be included in the clustering process. More formally, the viewpoints (views) augment the original, data-based objective function by including the term that expresses distances between data and the viewpoints. Depending upon the nature of domain knowledge, the viewpoints are represented either in a plain numeric format (considering that there is a high level of specificity with regard to how one establishes perspective from which the data need to be analyzed) or through some information granules (which reflect a more relaxed way in which the views at the data are being expressed). The detailed optimization schemes are presented, and the performance of the method is illustrated through some numeric examples. We also elaborate on a way in which the clustering with viewpoints enhances fuzzy models and mechanisms of decision making in the sense that the resulting constructs reflect the preferences and requirement that are present in the modeling environment.

Towards an automatic fuzzy ontology generation

In recent years, the success of Semantic Web is strongly related to the diffusion of numerous distributed ontologies enabling shared machine readable contents. Ontologies vary in size, semantic, application domain, but often do not foresee the representation and manipulation of uncertain information. Here we describe an approach for automatic fuzzy ontology elicitation by the analysis of web resources collection. The approach exploits a fuzzy extension of Formal Concept Analysis theory and defines a methodological process to generate an OWL-based representation of concepts, properties and individuals. A simple case study in the Web domain validates the applicability and the flexibility of this approach.

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.

Friendly web services selection exploiting fuzzy formal concept analysis

This work describes a system for supporting the user in the discovery of semantic web services, taking into account personal requirements and preference. Goal is to model an ad-hoc service request by selecting conceptual terms rather than using strict syntax formats. Through a concept-based navigation mechanism indeed, the user discovers conceptual terminology associated to the web resources and uses it to generate an appropriate service request which syntactical matches the names of input/output specifications. The approach exploits the fuzzy formal concept analysis for modeling concepts and relative relationships elicited from web resources. After the request formulation and submission, the system returns the list of semantic web services that match the user query.

Similarity-based SLD resolution and its role for web knowledge discovery

This work presents the implementation of an extension of SLD resolution towards approximate reasoning and its implementation in an extended Prolog system. The proposed refutation procedure overcomes failures in the unification process by exploiting similarity relations defined between predicate and constant symbols. This enables to compute approximate solutions, with an associated approximation degree, when failures of the exact inference process occur. In this paper we outline the main ideas of this approach and we present an extended PROLOG interpreter, named SiLog, which implements this inference procedure. Then we point out on a web-based platform, usable for knowledge discovery, that exploits as inner feature the similarity-based SLD resolution.

Approximate TF-IDF based on topic extraction from massive message stream using the GPU

The Web is a constantly expanding global information space that includes disparate types of data and resources. Recent trends demonstrate the urgent need to manage the large amounts of data stream, especially in specific domains of application such as critical infrastructure systems, sensor networks, log file analysis, search engines and more recently, social networks. All of these applications involve large-scale data-intensive tasks, often subject to time constraints and space complexity. Algorithms, data management and data retrieval techniques must be able to process data stream, i.e., process data as it becomes available and provide an accurate response, based solely on the data stream that has already been provided. Data retrieval techniques often require traditional data storage and processing approach, i.e., all data must be available in the storage space in order to be processed. For instance, a widely used relevance measure is Term Frequency-Inverse Document Frequency (TF-IDF), which can evaluate how important a word is in a collection of documents and requires to a priori know the whole dataset.To address this problem, we propose an approximate version of the TF-IDF measure suitable to work on continuous data stream (such as the exchange of messages, tweets and sensor-based log files). The algorithm for the calculation of this measure makes two assumptions: a fast response is required, and memory is both limited and infinitely smaller than the size of the data stream. In addition, to face the great computational power required to process massive data stream, we present also a parallel implementation of the approximate TF-IDF calculation using Graphical Processing Units (GPUs).This implementation of the algorithm was tested on generated and real data stream and was able to capture the most frequent terms. Our results demonstrate that the approximate version of the TF-IDF measure performs at a level that is comparable to the solution of the precise TF-IDF measure.

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.