Recommender systems evaluate and filter the great amount of information available on the Web to assist people in their search processes. A fuzzy evaluation method of SGML documents based on computing with words is presented. Given a DTD, we consider that its elements are not equally informative. This is indicated in the DTD by defining linguistic importance attributes to the more meaningful elements of DTD chosen. Then, the evaluation method generates linguistic recommendations from linguistic evaluation judgements provided by different recommenders on meaningful elements of DTD. To do so, it uses two quantifier guided linguistic aggregation operators, the LWA operator and the LOWA operator, which obtain recommendations taking into account the fuzzy majority of the recommenders ’ judgements. Using the fuzzy linguistic modeling the user-system interaction is facilitated and the assistance of system is improved. The method can be easily extended on the Web to evaluate HTML and XML documents.

Information gathering in Internet is a complex activity. Find the appropriate information, required for the users, on the World Wide Web is not a simple task. Then, Internet users need tools to assist them to obtain the information required. One possibility consists of using distributed intelligent agents in the information gathering process that help the users to cope with the mass of content available on the World Wide Web. The communication between users and agents is very important to the information gathering process be successful. The great variety of representations of the information in Internet is the main obstacle to this communication. The use of the linguistic information provides a more flexibility in the communication among agents and between agents and users. In this paper we propose a distributed intelligent model for gathering information on the Internet where the agents and users may communicate among them using a multi-granular linguistic model. This model provides a greater flexibility and several advantages in the user-system interaction.

Summary. In this paper we define a model of a web multi-agent system that combines the use of Semantic Web technologies (like RDF and OWL languages to describe resources and define ontologies respectively) together with the application of user profiles to carry out its activity. It is designed using fuzzy linguistic techniques which allow dealing with information in a user-friendly way. The system activity is developed in two phases: retrieval phase to gather the documents from the Web, and feedback phase to update the elements used to filter the documents, i.e., user profiles and recommendation values. We focus on the analysis of the feedback phase. With this multi-agent system model the filtering possibilities are increased and, consequently, also the retrieval capabilities on the Web. 1

www.elsevier.com/locate/fss Incorporating ltering techniques in a fuzzy linguistic multi-agent model for information gathering on the web

Abstract. Nowadays, information gathering in Internet is a complex activity and Internet users need systems to assist them to obtain the information required. In an earlier studies [5, 6, 16] we presented different fuzzy linguistic multi-agent models for helping users in their information gathering processes on the Web. In this paper, we present a new fuzzy linguistic multi-agent model to access information on the Web that incorporates the use of fuzzy multi-granular linguistic modeling to improve its user-system interaction and be more user-friendly.

Summary. Nowadays, an abundant amount of information is created and delivered over electronic media. The information gathering in Internet is a complex activity and Internet users need tools to assist them to find the information required. Web multi-agent systems assist the users by gathering from Internet the information that best satisfies their specific needs. In this paper, we analyze some techniques that applied together could provide major advances in the design of these Web multi-agent systems in order to improve their performance: i) information filtering tools and ii) the fuzzy linguistic modelling. Then, we present a model of a fuzzy linguistic multi-agent system for searching and mining the Web that is designed using some filtering tools and a particular fuzzy linguistic modelling, called multi-granular fuzzy linguistic modelling, which is useful when we have different label sets to assess the information.

Much information over the Internet is expressed by natural languages. The management of linguistic information involves an operation of comparison and aggregation. In this paper, based on linguistic aggregation operator Flowa, we discussed linguistic information processing in a multi-agent system over the Internet, in which, aggregating linguistic information and selecting better Web pages (or documents) that fulfill the user's query are discussed.