LEADER 05458nam 22007815 450 001 996465736103316 005 20240327172821.0 010 $a3-319-49493-7 024 7 $a10.1007/978-3-319-49493-7 035 $a(CKB)3710000001079929 035 $a(DE-He213)978-3-319-49493-7 035 $a(MiAaPQ)EBC5595750 035 $a(PPN)19886843X 035 $a(EXLCZ)993710000001079929 100 $a20170225d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aReasoning Web: Logical Foundation of Knowledge Graph Construction and Query Answering$b[electronic resource] $e12th International Summer School 2016, Aberdeen, UK, September 5-9, 2016, Tutorial Lectures /$fedited by Jeff Z. Pan, Diego Calvanese, Thomas Eiter, Ian Horrocks, Michael Kifer, Fangzhen Lin, Yuting Zhao 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XIV, 259 p. 37 illus.) 225 1 $aInformation Systems and Applications, incl. Internet/Web, and HCI ;$v9885 311 $a3-319-49492-9 327 $aUnderstanding Author Intentions: Test Driven Knowledge Graph Construction -- Inseparability and Conservative Extensions of Description Logic Ontologies: A Survey -- Navigational and Rule-Based Languages for Graph Databases -- LOD Lab: Scalable Linked Data Processing -- Inconsistency-Tolerant Querying of Description Logic Knowledge Bases -- From Fuzzy to Annotated Semantic Web Languages -- Applying Machine Reasoning and Learning in Real World Applications. 330 $aThis volume contains some lecture notes of the 12th Reasoning Web Summer School (RW 2016), held in Aberdeen, UK, in September 2016. In 2016, the theme of the school was ?Logical Foundation of Knowledge Graph Construction and Query Answering?. The notion of knowledge graph has become popular since Google started to use it to improve its search engine in 2012. Inspired by the success of Google, knowledge graphs are gaining momentum in the World Wide Web arena. Recent years have witnessed increasing industrial take-ups by other Internet giants, including Facebook's Open Graph and Microsoft's Satori. The aim of the lecture note is to provide a logical foundation for constructing and querying knowledge graphs. Our journey starts from the introduction of Knowledge Graph as well as its history, and the construction of knowledge graphs by considering both explicit and implicit author intentions. The book will then cover various topics, including how to revise and reuse ontologies (schema of knowledge graphs) in a safe way, how to combine navigational queries with basic pattern matching queries for knowledge graph, how to setup a environment to do experiments on knowledge graphs, how to deal with inconsistencies and fuzziness in ontologies and knowledge graphs, and how to combine machine learning and machine reasoning for knowledge graphs. 410 0$aInformation Systems and Applications, incl. Internet/Web, and HCI ;$v9885 606 $aDatabase management 606 $aArtificial intelligence 606 $aMathematical logic 606 $aInformation storage and retrieval 606 $aApplication software 606 $aData mining 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aMathematical Logic and Formal Languages$3https://scigraph.springernature.com/ontologies/product-market-codes/I16048 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aComputer Appl. in Administrative Data Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/I2301X 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 615 0$aDatabase management. 615 0$aArtificial intelligence. 615 0$aMathematical logic. 615 0$aInformation storage and retrieval. 615 0$aApplication software. 615 0$aData mining. 615 14$aDatabase Management. 615 24$aArtificial Intelligence. 615 24$aMathematical Logic and Formal Languages. 615 24$aInformation Storage and Retrieval. 615 24$aComputer Appl. in Administrative Data Processing. 615 24$aData Mining and Knowledge Discovery. 676 $a025.04 702 $aPan$b Jeff Z$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCalvanese$b Diego$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aEiter$b Thomas$f1966-$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHorrocks$b Ian$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKifer$b Michael$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLin$b Fangzhen$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZhao$b Yuting$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465736103316 996 $aReasoning Web: Logical Foundation of Knowledge Graph Construction and Query Answering$92830573 997 $aUNISA