Silvia Calegari

Fuzzy Ontology, Fuzzy Description Logics and Fuzzy-OWL

The conceptual formalism supported by an ontology is not sufficient for handling vague information that is commonly found in many application domains. We describe how to introduce fuzziness in an ontology. To this aim we define a framework consisting of a fuzzy ontology based on Fuzzy Description Logic and Fuzzy---Owl.

Personal ontologies: Generation of user profiles based on the YAGO ontology

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.

Fuzzy Ontology and Fuzzy-OWL in the KAON Project

In many application domains is necessary to reason with vague information, but, the conceptual formalism supported by a classical ontology is not sufficient for handling it. Thus, we have developed a suited plug-in of the KAON Project in order to introduce fuzziness in an ontology. This extension is based on corresponding fuzzy ontology and Fuzzy-OWL notions.

Automatic Labeling of Topics

An algorithm for the automatic labeling of topics accordingly to a hierarchy is presented. Its main ingredients are a set of similarity measures and a set of topic labeling rules. The labeling rules are specifically designed to find the most agreed labels between the given topic and the hierarchy. The hierarchy is obtained from the Google Directory service, extracted via an ad-hoc developed software procedure and expanded through the use of the OpenOffice English Thesaurus. The performance of the proposed algorithm is investigated by using a document corpus consisting of 33,801 documents and a dictionary consisting of 111,795 words. The results are encouraging, while particularly interesting and significant labeling cases emerged

Ontology-Based Information Behaviour to Improve Web Search

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.

Towards a Fuzzy Ontology Definition and a Fuzzy Extension of an Ontology Editor

This paper presents an enrichment of classical computational ontologies with fuzzy logic to create fuzzy ontologies. It is a step towards managing vagueness and facing the nuances of natural languages in ontology-based applications. Our proposal is implemented in the KAON ontology editor, that allows to handle ontology concepts in a high-level environment.

PatentLight: a patent search application

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.

A semantic and information retrieval based approach to service contract selection

Service contracts represent the agreement between the service provider and potential service consumers to use a specific service under given conditions; for each service multiple service contracts are available. In this paper we investigate a new approach to support the service contract selection by exploiting preferences both explicitly defined by a user and implicitly inferred from his/her context. The core of our approach is the use of multi-constraint queries expressed on punctual values and on textual descriptions. Both semantic-based and information retrieval (IR) techniques are applied. Experimental evaluations show the effectiveness of the proposed approach.

OCEAN Project A Prototype of AIWBES Based on Fuzzy Ontology

Ocean Project is aimed to realize an Adaptive and Intelligent Web-Based Educational System (AIWBES) working with traditional Learning Management Systems (LMS). It is designed as a collection of open-source libraries (the Omega Framework), so resulting easily customizable and adaptable to the current e-learning platforms. In this new system each course is presented in different ways according to the student’s learning level, through to a combined use of ontologies and fuzzy logic.

Ontologies Help Finding Inspiration: A Practical Approach in Multimedia Information Management

Students working in inspirational learning environments need some help with finding new ideas. An ontology-driven selection facility was developed in the ATELIER project for automatically extract appropriate material from a large knowledge base. This paper introduces the ATELIER project and describes the algorithm we studied for presenting inspirational material to students while using the system. A scenario and literature contextualization are also given.