Extraction of Adaptation Knowledge from Internet Communities
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Extraction of Adaptation Knowledge from Internet Communities

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Extraction of Adaptation Knowledge from Internet Communities Extraction of Adaptation Knowledge from Internet Communities Presentation Transcript

  • FGWM’09 Fachgruppentreffen Wissensmanagement at LWA09, TU Darmstadt, 2009-09-22 Extraction of Adaptation Knowledge from Internet Communities * Norman Ihle, Alexandre Hanft, and Klaus-Dieter Althoff University of Hildesheim Institute for Computer Science Intelligent Information Systems Lab [lastname]@iis.uni-hildesheim.de * This is an extended version of the paper presented at the Workshop ”WebCBR: Reasoning from Experiences on the Web” at ICCBR’09
  • FGWM @ LWA‘2009 | 2009-09-22 2 Outline Motivation CookIIS CommunityCook A system for model-based knowledge extraction from Internet-Communities Evaluation Conclusion & Outlook Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 3 Motivation Adaptation is the „Reasoning“ in CBR [Kolodner 1997] Most CBR-Systems avoid adaptation [Schmidt et al. 2003; Minor 2006] Adaptation Knowledge Acquisition (AKA) is cost intensive and time consuming − Experts hardly available − Small number of research papers and systems − Most systems focus on the case-base as source of knowledge The Internet is a large source of knowledge, especially user- generated content Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 4 The Cooking Domain The cooking domain is well suited for adaptation, because: The context can be described easily: 1. all ingredients can be listed with exact amount and quality 2. ingredients can be obtained in standardized quantity and in comparable quality 3. kitchen machines and tools are available in a standardized manner 4. (in case of a failure) the preparation of a meal can start over every time again from the same initial situation (except that we have more experience in cooking after each failure) Cooking is about creativity and variation Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 5
  • FGWM @ LWA‘2009 | 2009-09-22 6 CookIIS System for the retrieval and adaptation of recipes in the cooking domain http://cookiis.iis.uni-hildesheim.de Competes in the ComputerCookingContest Given recipes Different tasks and requirements − Identification of negations, type of meal and origin of the dish − Handling of certain diets − Creation of a three course menu Developed using the empolis:Information Access Suite (e:IAS) Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 7 CookIIS knowledge model Most important component: modelled ingredients 11 different classes, about 1000 concepts Modelled in English and German with synonyms Concepts organised in taxonomies Combined similarity Other components: tools, origins, methods, etc. Overall about 2000 modelled concepts Rules for the recognition of the origin of the dish Rules for the recognition of the type of meal Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 8 Adaptation in CookIIS Model-based approach: Replace unwanted ingredients with similar ones Similarity is mainly based on taxonomies and using a set-function offered by e:IAS Rule Engine: − Parent and Child concepts are retrieved as well as sibling concepts − Too many similar ingredients are retrieved In many cases the approach is not appropriate Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 9 Cooking Communities A number of Internet communities deal with cooking knowledge Users upload recipes and discuss them inside comments They express affirmation, critics and what they changed for their own variation of the recipe (their personal adaptation) If they vary the recipe, they name ingredients Idea: using the CookIIS knowledge model to extract those ingredients Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 10 CommunityCook: Classification Idea Comments can be classified according to extracted ingredients into three categories: NEW: all ingredients that are discussed, but are not part of the recipe Add some ingredient OLD: all ingredients that are discussed and are part of the recipe “more”/ “less” of an ingredient, explanation for an ingredient OLDANDNEW: some ingredients that are discussed are part of the recipe and others not Replacement of ingredients == adaptation Specialisation (“for cheese I took parmesan”) Latter category can be an adaptation suggestion, especially if ingredients are of the same class of the knowledge model Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 11 CommunityCook: Crawling Crawling of a large German cooking community: About 76.000 recipes with 286.000 related comments HTML source code Extraction of necessary data by building filters with the help of an open source tool (HTMLParser) Recipe title Single ingredients (amount, measurement, name) Comments Statistics Saved into a database Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 12 CommunityCook: Text Mining case bases Configuring e:IAS with two case-bases: recipe, comment Cases representation is based on the modelled ingredients of the CookIIS knowledge model Use e:IAS TextMiner to fill cases with concepts from text Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 13 CommunityCook: perform Classification In the next step we retrieved one recipe and all comments relating to that recipe Each comment classified into on of the three categories Additionally we tried to find phrases in the text that support and specify the classification assigned a score to determine the confidence of the classification If a pair of ingredients of the same class is found we also analyse if one concept is the parent concept of the other no adaptation, but specialisation About 35.000 comments classified as OLDANDNEW, 16.000 with the subcategory “adaptation” Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 14 CommunityCook: Aggregation One way: Aggregation of all classified comments belonging to the recipe We counted the number of same classifications per recipe and aggregated the score by calculating the average and assigning a bonus for every classification Second way: also aggregated all classified ingredients without regarding the recipe (statistical) Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 15 CommunityCook: Realization Transformation of data: Building of adaptation suggestions in database-rows to easily retrieve those With regard to the recipe and without (“independent”) Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 16 CommunityCook: Integration into CookIIS 6200 different adaptation suggestions are available for 570 different ingredients Using the two most common adaptation suggestions per ingredient (without regard of the recipe) to create adaptation suggestions Integration into CookIIS-Workflows: − If no adaptation suggestion is created with community data, the model-based adaptation is used Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 17 CommunityCook: Realization Query: Chicken, but no cream Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 18 Starting Evaluation First look: The class “supplement” of the knowledge model − Too many different kinds of ingredients are in these class so that the adaptation suggestions are not adequate The class “basic” of the knowledge model − Basic ingredients like flour or egg are just hard to replace both not in the review, not used in CookIIS two different evaluations: One evaluation to review the classification scheme − Do the classified ingredients represent what was expressed in the original comment? One evaluation to review the extracted knowledge − Are the created adaptation suggestion applicable? Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 19 Evaluation Evaluation of the extracted knowledge: Expert survey − Real chefs review the adaptation suggestions for recipes − Questionnaire with recipe and adaptation suggestions − One adaptation suggestion that was extracted from comments belonging to that recipe (“dependent”) − Two adaptation suggestions without regard of the recipe (“independent”) each with two ingredients as replacement suggestion (as in CookIIS) 50 Questionnaires with 50 dependent and 100 pairs of independent ingredients Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 20 Evaluation: overall Applicability Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 21 Evaluation 1st vs. 2nd suggestion Only 11 of the 100 independent adaptation suggestions included no ingredient that can be used as substitution Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 22 Evaluation: Quality Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 23 Future work Further improvements on the knowledge model Usage of adaptation suggestions that were extracted from recipes similar to the current one Adding some semantic analysis to improve accuracy Usage of comments with other classifications for building variations of recipes Check the applicability in other domains Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 24 Related work Systems that give preparation advises for meals: CHEF [Hammond 1986] JULIA [Hinrichs 1992] Adaptation knowledge aquisition: DIAL [Leake et. al 1996] CABAMAKA [d'Aquin et al. 2007] IAKA [Cordier et al. 2008] Using the web as knowledge source in CBR: SEASALT [Bach et al. 2007] EDIR [Plaza 2008] Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 25 Conclusion Adaptation knowledge is hard to acquire The World Wide Web is a large source for knowledge CommunityCook is a system the extracts adaptation knowledge from web communities in the domain of cooking uses an existing knowledge model The evaluation shows the applicability of the extracted knowledge Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 26 Thank you for your attention! Questions? Extraction of Adaptation Knowledge from Internet Communities
  • FGWM @ LWA‘2009 | 2009-09-22 27 Literature [Bach et al., 2007] Kerstin Bach, Meike Reichle, and Klaus-Dieter Althoff. A domain independent system architecture for sharing experience. In Alexander Hinneburg, editor, Proceedings of LWA 2007, Workshop Wissens- und Erfahrungsmanagement, pages 296– 303, Sep. 2007. [Cordier et al., 2008] Am´elie Cordier, B´eatrice Fuchs, L´eonardo Lana de Carvalho, Jean Lieber, and Alain Mille. Opportunistic acquisition of adaptation knowledge cases - the iaka approach. In Althoff et al. [2008], pages 150–164. [d’Aquin et al., 2007] Mathieu d’Aquin, Fadi Badra, Sandrine Lafrogne, Jean Lieber, Amedeo Napoli, and Laszlo Szathmary. Case base mining for adaptation knowledge acquisition. In Manuela M. Veloso, editor, IJCAI, pages 750–755. Morgan Kaufmann, 2007. [Hammond, 1986] Kristian J. Hammond. Chef: A model of case-based planning. In American Association for Artificial Intelligence, AAAI-86, Philadelphia, pages 267–271, 1986. [Hinrichs, 1992] Thomas R. Hinrichs. Problem solving inopen worlds. Lawrence Erlbaum, 1992. [Leake et al. 1996]: D. Leake, A. Kinley und D. Wilson: Acquiring Case-Adaptation Knowledge: A hybrid Approach, in: Proceedings of theThirteenth National Conference on Artificial Intelligence, S. 684-689, AAAI Press, 1996. [Minor 2006]: M. Minor: Erfahrungsmanagement mit fallbasierten Assistenzsystemen, Dissertation, Humbolt-Universitat zu Berlin, 2006. [Plaza, 2008] Enric Plaza. Semantics and experience in the future web. In Althoff et al. [2008], pages 44–58. invited talk. [Schmidt et al. 2003]: R. Schmidt, O. Vorobieva und L. Gierl: Case-based Adaptation Problems in Medicine, in: U. Reimer (Hrsg.): Proceedings of WM2003: Professionelles Wissensmanagement – Erfahrungen und Visionen, Kollen-Verlag, 2003.