LEADER 06419nam 22008775 450 001 996466101403316 005 20200629234306.0 010 $a3-540-47698-9 024 7 $a10.1007/11908678 035 $a(CKB)1000000000284002 035 $a(SSID)ssj0000319997 035 $a(PQKBManifestationID)11272094 035 $a(PQKBTitleCode)TC0000319997 035 $a(PQKBWorkID)10343517 035 $a(PQKB)10958330 035 $a(DE-He213)978-3-540-47698-6 035 $a(MiAaPQ)EBC3068655 035 $a(PPN)123139287 035 $a(EXLCZ)991000000000284002 100 $a20100301d2006 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aSemantics, Web and Mining$b[electronic resource] $eJoint International Workshop, EWMF 2005 and KDO 2005, Porto, Portugal, October 3-7, 2005, Revised Selected Papers /$fedited by Markus Ackermann, Bettina Berendt, Marko Grobelnik, Andreas Hotho, Dunja Mladenic, Giovanni Semeraro, Myra Spiliopoulou, Gerd Stumme, Vojtech Svatek, Maarten van Someren 205 $a1st ed. 2006. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2006. 215 $a1 online resource (X, 196 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v4289 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-47697-0 320 $aIncludes bibliographical references and index. 327 $aEWMF Papers -- A Website Mining Model Centered on User Queries -- WordNet-Based Word Sense Disambiguation for Learning User Profiles -- Visibility Analysis on the Web Using Co-visibilities and Semantic Networks -- Link-Local Features for Hypertext Classification -- Information Retrieval in Trust-Enhanced Document Networks -- Semi-automatic Creation and Maintenance of Web Resources with webTopic -- KDO Papers on KDD for Ontology -- Discovering a Term Taxonomy from Term Similarities Using Principal Component Analysis -- Semi-automatic Construction of Topic Ontologies -- Evaluation of Ontology Enhancement Tools -- KDO Papers on Ontology for KDD -- Introducing Semantics in Web Personalization: The Role of Ontologies -- Ontology-Enhanced Association Mining -- Ontology-Based Rummaging Mechanisms for the Interpretation of Web Usage Patterns. 330 $aFinding knowledge ? or meaning ? in data is the goal of every knowledge d- covery e?ort. Subsequent goals and questions regarding this knowledge di?er amongknowledgediscovery(KD) projectsandapproaches. Onecentralquestion is whether and to what extent the meaning extracted from the data is expressed in a formal way that allows not only humans but also machines to understand and re-use it, i. e. , whether the semantics are formal semantics. Conversely, the input to KD processes di?ers between KD projects and approaches. One central questioniswhetherthebackgroundknowledge,businessunderstanding,etc. that the analyst employs to improve the results of KD is a set of natural-language statements, a theory in a formal language, or somewhere in between. Also, the data that are being mined can be more or less structured and/or accompanied by formal semantics. These questions must be asked in every KD e?ort. Nowhere may they be more pertinent, however, than in KD from Web data (?Web mining?). This is due especially to the vast amounts and heterogeneity of data and ba- ground knowledge available for Web mining (content, link structure, and - age), and to the re-use of background knowledge and KD results over the Web as a global knowledge repository and activity space. In addition, the (Sem- tic) Web can serve as a publishing space for the results of knowledge discovery from other resources, especially if the whole process is underpinned by common ontologies. 410 0$aLecture Notes in Artificial Intelligence ;$v4289 606 $aArtificial intelligence 606 $aComputer communication systems 606 $aDatabase management 606 $aInformation storage and retrieval 606 $aApplication software 606 $aComputers and civilization 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputer Communication Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/I13022 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aInformation Systems Applications (incl. Internet)$3https://scigraph.springernature.com/ontologies/product-market-codes/I18040 606 $aComputers and Society$3https://scigraph.springernature.com/ontologies/product-market-codes/I24040 615 0$aArtificial intelligence. 615 0$aComputer communication systems. 615 0$aDatabase management. 615 0$aInformation storage and retrieval. 615 0$aApplication software. 615 0$aComputers and civilization. 615 14$aArtificial Intelligence. 615 24$aComputer Communication Networks. 615 24$aDatabase Management. 615 24$aInformation Storage and Retrieval. 615 24$aInformation Systems Applications (incl. Internet). 615 24$aComputers and Society. 676 $a025.04 702 $aAckermann$b Markus$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBerendt$b Bettina$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGrobelnik$b Marko$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHotho$b Andreas$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMladenic$b Dunja$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSemeraro$b Giovanni$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSpiliopoulou$b Myra$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aStumme$b Gerd$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSvatek$b Vojtech$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSomeren$b Maarten van$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aKDO 2005$f(2005 :$ePorto, Portugal) 906 $aBOOK 912 $a996466101403316 996 $aSemantics, Web and Mining$9772288 997 $aUNISA