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

Conference PaperinLecture Notes in Computer Science · September 2006with4 Reads
DOI: 10.1007/11805816_18 · Source: DBLP
Conference: Advances in Case-Based Reasoning, 8th European Conference, ECCBR 2006, Fethiye, Turkey, September 4-7, 2006, Proceedings
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.
    • "us on the automatic selection of noun phrases as documents descriptors to build an FCA based IR system. Automatic attribute selection is important when using FCA in a free text document retrieval framework. Optimal attributes as document descriptors should produce smaller, clearer and more browsable concept lattices with better clustering features. Garcia et al. (2006) use FCA to perform semantic annotation of web pages with domain ontologies. Similarity matching techniques from Case Based Reasoning can be applied to retrieve these annotated pages as cases. Liu et al. (2007) use FCA to optimize a personal news search engine to help users obtain the news content they need rapidly. The proposed techniqu"
    [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.
    Full-text · Conference Paper · Jan 2011
    • "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. "
    [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.
    Full-text · Chapter · Jan 2007
  • [Show abstract] [Hide abstract] ABSTRACT: This is the second part of a large survey paper in which we analyze recent literature on Formal Concept Analysis (FCA) and some closely related disciplines using FCA. We collected 1072 papers published between 2003-2011 mentioning terms related to Formal Concept Analysis in the title, abstract and keywords. We developed a knowledge browsing environment to support our literature analysis process. We use the visualization capabilities of FCA to explore the literature, to discover and conceptually represent the main research topics in the FCA community. In this second part, we zoom in on and give an extensive overview of the papers published between 2003 and 2011 which applied FCA-based methods for knowledge discovery and ontology engineering in various application domains. These domains include software mining, web analytics, medicine, biology and chemistry data.
    Full-text · Article · Nov 2013
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