LEADER 04483nam 2200637 a 450 001 9910482959503321 005 20200520144314.0 010 $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 $a20061019d2006 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aSemantics, web and mining $ejoint international workshops, EWMF 2005 and KDO 2005, Porto, Portugal, October 3 and 7, 2005 : revised selected papers /$fMarkus Ackermann ... [et al.] (eds.) 205 $a1st ed. 2006. 210 $aBerlin $cSpringer$dc2006 215 $a1 online resource (X, 196 p.) 225 1 $aLecture notes in computer science. Lecture notes in artificial intelligence,$x0302-9743 ;$v4289 225 1 $aLNCS sublibrary. SL 7, Artificial intelligence 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 computer science.$pLecture notes in artificial intelligence ;$v4289. 410 0$aLNCS sublibrary.$nSL 7,$pArtificial intelligence. 517 3 $aEWMF 2005 517 3 $aKDO 2005 606 $aSemantic Web$vCongresses 606 $aData mining$vCongresses 606 $aOntologies (Information retrieval)$vCongresses 615 0$aSemantic Web 615 0$aData mining 615 0$aOntologies (Information retrieval) 676 $a025.04 701 $aAckermann$b Markus$01757541 712 12$aKDO 2005$f(2005 :$ePorto, Portugal) 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910482959503321 996 $aSemantics, web and mining$94195421 997 $aUNINA