Improving Annotation in the Semantic Web and Case Authoring in Textual CBR.
ABSTRACT 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.
- SourceAvailable from: Sergei O. Kuznetsov[Show abstract] [Hide abstract]
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.Advances in Data Mining. 11th Industrial Conference, ICDM 2011, New York, USA, September/August 2011, Poster and Industry Proceedings, Workshop on Data Mining in Life Sciences; 01/2011
- [Show abstract] [Hide abstract]
ABSTRACT: In order to reach as many players as possible, videogames usually allow the user to choose the difficulty level. To do it, game designers have to decide the values that some game parameters will have depending on that decision. In simple videogames this is almost trivial: minesweeper is harder with longer board sizes and number of mines. In more complex games, game designers may take advantage of data mining to establish which of all the possible parameters will affect positively to the player experience. This paper describes the use of Formal Concept Analysis to help to balance the game using the logs obtained in the tests made prior the release of the game.12/2006: pages 217-230;
- [Show abstract] [Hide abstract]
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.Science of Computer Programming 01/2014; 79:126–145. · 0.55 Impact Factor