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Case-based reasoning (CBR) is a paradigm for combining problem solving and learning that has become one of the most successful applied subfields of AI in recent years. Now that CBR has become a mature and established technology two necessities have become critical: the availability of tools to build CBR systems, and the accumulated practical experience of(More)
In this paper we describe a domain independent architecture to help in the design of knowledge intensive CBR systems. It is based on the knowledge incorporation from a library of application-independent ontologies and the use of an ontology with the common CBR terminology that guides the case representation and allows the description of flexible, generic(More)
This paper focuses on the design of knowledge intensive CBR systems and introduces a domain-independent architecture to help it. Our approach is based on acquiring the domain knowledge by reusing knowledge from a library of ontologies and integrating it with CBROnto, a task based ontology comprising common CBR terminology. In this paper we focus in(More)
In this article we introduce a novel method of making recommendations to groups based on existing techniques of collaborative filtering and taking into account the group personality composition. We have tested our method in the movie recommendation domain and we have experimentally evaluated its behavior under heterogeneous groups according to the group(More)
Our approach to Case-Based Reasoning (CBR) is to build integrated systems that combine case specific knowledge with models of general domain knowledge. In this paper we describe CBROnto, the CBR ontology we have developed, as a task/method ontology. CBROnto specifies a modelling framework to describe reusable CBR Problem Solving Methods based on the CBR(More)
Our goal is to support system developers in rapid prototyp-ing of Case-Based Reasoning (CBR) systems through component reuse. In this paper, we propose the idea of templates that can be readily adapted when building a CBR system. We define a case base of templates for case-based recommender systems. We devise a novel case-based template recommender, based(More)
In [3], we have presented Description Logics (DLs) as a suitable knowledge representation technology to model the CBR processes. This paper mainly focuses on the adaptation process. We propose a domain independent model to structure the knowledge needed in a CBR system, where adaptation knowledge is explicitly represented. Built upon this model we propose(More)
In this paper we present a system for automatic story generation that reuses existing stories to produce a new story that matches a given user query. The plot structure is obtained by a case-based reasoning (CBR) process over a case base of tales and an ontology of explicitly declared relevant knowledge. The resulting story is generated as a sketch of a(More)
Automatic Text Categorization (TC) is a complex and useful task for many natural language applications, and is usually performed through the use of a set of manually classified documents, a training collection. We suggest the utilization of additional resources like lexical databases to increase the amount of information that TC systems make use of, and(More)