Conference Paper

Improving Annotation in the Semantic Web and Case Authoring in Textual CBR

Complutense University of Madrid, Madrid, Madrid, Spain
DOI: 10.1007/11805816_18 Conference: Advances in Case-Based Reasoning, 8th European Conference, ECCBR 2006, Fethiye, Turkey, September 4-7, 2006, Proceedings
Source: DBLP


This paper describes our work in textual Case-Based Reasoning within the context of Semantic Web. Semantic Annotation of plain
texts is one of the core challenges for building the Semantic Web. We have used different techniques to annotate web pages
with domain ontologies to facilitate semantic retrieval over the web. Typical similarity matching techniques borrowed from
CBR can be applied to retrieve these annotated pages as cases. We compare different approaches to do such annotation process:
manually, automatically based on Information Extraction (IE) rules, and completing the IE rules within the rules that result
from the application of Formal Concept Analysis over a set of manually annotated cases. We have made our experiments using
the textual CBR extension of the jCOLIBRI framework.

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    • "In general, FCA may be useful in knowledge discovery in databases [9]. In previous papers [3] [8] [2] we have used FCA as a particular and very adequate technique to organize and discover knowledge embedded in Case Based Reasoning (CBR) applications. FCA can be applied to any collection of items described by properties and provides a way to identify groupings of objects with shared properties. "
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    ABSTRACT: Formal Concept Analysis (FCA) is an unsupervised clustering technique and many scientific papers are devoted to applying FCA in Information Retrieval (IR) research. We collected 103 papers published between 2003-2009 which mention FCA and information retrieval in the abstract, title or keywords. Using a prototype of our FCA-based toolset CORDIET, we converted the pdf-files containing the papers to plain text, indexed them with Lucene using a thesaurus containing terms related to FCA research and then created the concept lattice shown in this paper. We visualized, analyzed and explored the literature with concept lattices and discovered multiple interesting research streams in IR of which we give an extensive overview. The core contributions of this paper are the innovative application of FCA to the text mining of scientific papers and the survey of the FCA-based IR research.
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    ABSTRACT: This paper describes the jcolibri2 framework for building Case-based reasoning (CBR) systems. CBR is a mature subfield of artificial intelligence based on the reuse of previous problem solutions–cases–to solve new ones. However, up until now, it lacked a reference toolkit for developing such systems. jcolibri2 aims to become that toolkit and to foster the collaboration among research groups. This software is the result of the experience collected over several years of framework development and evolution. This experience is explained in the paper, together with a description of the specialized CBR tools that can be implemented with jcolibri: CBR with textual cases, recommenders, knowledge/data intensive applications or distributed architectures.
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