A RDF-based Framework for User Profile Creation and Management

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解释rdf资源描述框架

解释rdf资源描述框架资源描述框架（Resource Description Framework) RDF--Web 数据集成的元数据解决方案一．引言在现今的社会中，信息无处不在，从这些信息中获取对自己有用的信息并不是件容易的事。

当然也有例外的，比如，在图书馆里你可以根据书名或作者名或关键字的信息找到藏书号，从而很容易找到所要的书，在音像店里你可以根据片名、主演等信息方便的找到自己所要的影碟。

这两个系统有一个共同的特点--它们都是建立在元数据之上。

元数据是关于数据的数据或关于信息的信息。

例如：书的文本就是书的数据，而书名、作者、版权数据都是书的元数据。

元数据并不一定就是用来检索的，也可用于内部的管理，如图书馆系统可以为书定义被借次数这个元数据，以了解书的被借阅情况，确定是否要增加副本数。

元数据的使用，可以大大提高系统的检索和管理的效率。

网络是个大的数据库，它里面包含的数据比起图书馆和音像店来可要复杂的多，五花八门，什么都有，但有一个问题--网络基本上没有元数据。

那搜索引擎是怎么工作的呢？其实，搜索引擎中除极少数如Yahoo!外，基本上都是采用网页的全文检索来提供检索服务，这就可想而之其查准率之低了。

Yahoo! 将其收集到的网站及网页分门别类加以索引和文摘（由人工完成），从而大大提高了查准率，这也是其流行的一个重要原因。

但对如此浩瀚的信息海洋若都采用人工标引显然是不现实的，所以我们用Yahoo!检索的时候查全率不如象Altavista、Infoseek这样的搜索引擎高，原因是其收录的网站网页数量有限。

如果网络上的资源在创建之初就都使用元数据来描述其自身的信息，那不就可以省去人工标引的麻烦吗？是的，但是怎样用元数据来描述，这得有个标准，W3C提出的用于描述Web资源的RDF（Resource Description Framework 资源描述框架）就是这样的一个标准，RDF给出了Web数据集成的元数据解决方案。

知识图谱基础之三元组：PythonRDFLib实例入门

知识图谱基础之三元组：PythonRDFLib实例⼊门本⽂转⾃，该页可在线运⾏本节将简要地带您⼊门Python中的RDF操作库rdflib，三元组是⼀个在知识图谱中重要的基础性概念，通过在线安装该库，并在线运⾏两个⽰例，来理解该库的使⽤。

理解本节的前提，您需要有Python基础，RDF基础。

什么是RDF？RDF是W3C的资源描述框架(RDF)。

RDF提供了⼀种表达数据图并与其他⼈(可能更重要的是与机器)共享的标准⽅法。

由于它是W3C“推荐”(通过任何其他措施得出的⾏业标准)，因此围绕RDF出现了⼤量⼯具和服务。

RDF的历史可以追溯到1990年，当时蒂姆·伯纳斯·李(Tim Berners-Lee)撰写了⼀项提案，⽂档之间存在不同类型的链接，这使超⽂本更易于计算机⾃动理解。

输⼊的链接未包含在第⼀个HTML规范中，但在元内容框架(Meta Content Framework(MCF))，⽤于描述元数据和组织Web的系统，由 Ramanathan Guha于1990年代后期在Apple和Netscape任职时，由Tim Bray开发了XML表⽰形式。

W3C⼀直在寻找通⽤的元数据表⽰形式，并且MCF中的许多想法都在1999年的第⼀个RDF W3C建议书中找到了⾃⼰的⽅法。

此后，对标准进⾏了修订，如今的软件和⼯具反映了这些改进。

安装RDFLibRDFLib是开源的，并在存储库中维护。

PyPi列出了当前版本和先前版本The best way to install RDFLib is to use pip3!pip3 install rdflib -i https:///simple/它是如何⼯作该软件包使⽤了各种Python习惯⽤法，这些惯⽤法提供了将RDF引⼊以前从未使⽤过RDF的Python程序员的合适⽅法。

RDFLib公开的⽤于RDF的主要接⼝是。

RDFLib图不是已排序的容器。

它们具有普通set操作(例如添加三元组)以及搜索三元组并以任意顺序返回它们的⽅法。

语义网关键技术概述

语义网关键技术概述李洁，丁颖(中国矿业大学计算机科学与技术学院，江苏徐州 221008)摘要：语义网是对 WWW 的延伸，其目标是使得 Web 上的信息具有计算机可以理解的语义，并为人们提供各种智能服务。

在介绍语义网概念及其体系结构的基础上，对 3 大关键技术：XML 、RDF 、Ontology 作一简要的概述，讨论了其要解决的主要问题。

在把握当前研究现状的基础上，明确今后主要的研究方向与重点问题。

关键词：语义网; 可扩展标识语言; 资源描述框架; 本体; 描述语言中图法分类号：TP311文献标识码：A文章编号：1000-7024 (2007) 08-1831-03Survey of sematic web key techniquesLI Jie, DING Ying(College of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221008, China )Abstract ：The semantic web is an extension of WWW. Its goal is making computer understand an d process data on the web an d providin g various intelligent services. On the basis of introducin g the concept and system structure of semantic web, three key techniques: XML, RDF, Ontology is summarized and main problems to be solved is discussed. And the later research direction and key problems based on the current research actuality is specified.Key words ：semantic web; XML; RDF; ontology; description language表 1 解析 Tim Berners-Lee 的语义网结构0 引言随着互联网的飞速发展和广泛应用，其缺陷也逐渐暴露出来，如搜索引擎智能程度低，搜索出来的结果往往不是用户真正需要的，网页功能单调等等。

[生活]计算机专业英语词汇缩写大全

[生活]计算机专业英语词汇缩写大全计算机专业英语词汇缩写大全计算机专业英语词汇缩写大全(J-Z)2010年01月06日星期三 12:47J J2EE — Java 2 Enterprise Edition J2ME — Java 2 Micro Edition J2SE — Java 2 Standard Edition JAXB — Java Architecture for XML Binding JAX-RPC — Java XML for Remote Procedure Calls JAXP — Java API for XML Processing JBOD — Just a Bunch of Disks JCE — Java Cryptography Extension JCL — Job Control Language JCP — Java Community Process JDBC — Java Database Connectivity JDK — Java Development KitJES — Job Entry SubsystemJDS — Java Desktop SystemJFC — Java Foundation Classes JFET — Junction Field-Effect Transistor JFS — IBM Journaling File System JINI — Jini Is Not InitialsJIT — Just-In-TimeJMX — Java Management Extensions JMS — Java Message Service JNDI — Java Naming and Directory Interface JNI — Java Native InterfaceJPEG — Joint Photographic Experts Group JRE — Java Runtime Environment JS — JavaScriptJSON — JavaScript Object NotationJSP — Jackson Structured Programming JSP — JavaServer PagesJTAG — Joint Test Action Group JUG — Java Users Group JVM — Java Virtual Machine jwz — Jamie ZawinskiKK&R — Kernighan and Ritchie KB — KeyboardKb — KilobitKB — KilobyteKB — Knowledge BaseKDE — K Desktop Environment kHz — KilohertzKISS — Keep It Simple, Stupid KVM — Keyboard, Video, Mouse LL10N — LocalizationL2TP — Layer 2 Tunneling Protocol LAMP — Linux Apache MySQL Perl LAMP — Linux Apache MySQL PHP LAMP — Linux Apache MySQL Python LAN —Local Area Network LBA — Logical Block Addressing LCD — Liquid Crystal Display LCOS — Liquid Crystal On Silicon LDAP — Lightweight Directory Access ProtocolLE — Logical ExtentsLED — Light-Emitting Diode LF — Line FeedLF — Low FrequencyLFS — Linux From Scratch lib — libraryLIF — Low Insertion Force LIFO — Last In First Out LILO — Linux LoaderLKML — Linux Kernel Mailing List LM — Lan ManagerLGPL — Lesser General Public License LOC — Lines of CodeLPI — Linux Professional Institute LPT — Line Print Terminal LSB — Least Significant Bit LSB — Linux Standard Base LSI — Large-Scale IntegrationLTL — Linear Temporal Logic LTR — Left-to-RightLUG — Linux User Group LUN — Logical Unit Number LV — Logical VolumeLVD — Low Voltage Differential LVM — Logical Volume Management LZW — Lempel-Ziv-Welch MMAC — Mandatory Access Control MAC — Media Access Control MAN —Metropolitan Area Network MANET — Mobile Ad-Hoc Network MAPI —Messaging Application Programming InterfaceMb — MegabitMB — MegabyteMBCS — Multi Byte Character Set MBR — Master Boot RecordMCA — Micro Channel Architecture MCSA — Microsoft Certified Systems AdministratorMCSD — Microsoft Certified Solution DeveloperMCSE — Microsoft Certified Systems Engineer MDA — Mail Delivery AgentMDA — Model-Driven Architecture MDA — Monochrome Display Adapter MDF — Main Distribution FrameMDI — Multiple Document Interface ME — [Windows] Millennium Edition MF — Medium FrequencyMFC — Microsoft Foundation Classes MFM — Modified Frequency Modulation MGCP — Media Gateway Control Protocol MHz — Megahertz MIB — Management Information Base MICR — Magnetic Ink Character Recognition MIDI — Musical Instrument Digital Interface MIMD —Multiple Instruction, Multiple Data MIMO — Multiple-Input Multiple-Output MIPS — Million Instructions Per Second MIPS — Microprocessor without Interlocked Pipeline StagesMIS — Management Information Systems MISD — Multiple Instruction, Single Data MIT — Massachusetts Institute of Technology MIME —Multipurpose Internet Mail ExtensionsMMDS — Mortality Medical Data System MMI — Man Machine Interface. MMIO — Memory-Mapped I/OMMORPG — Massively Multiplayer Online Role-Playing GameMMU — Memory Management Unit MMX — Multi-Media Extensions MNG —Multiple-image Network Graphics MoBo — MotherboardMOM — Message-Oriented Middleware MOO — MUD Object OrientedMOSFET — Metal-Oxide Semiconductor FET MOTD — Message Of The Day MPAA — Motion Picture Association of America MPEG — Motion Pictures Experts Group MPL — Mozilla Public License MPLS —Multiprotocol Label Switching MPU — Microprocessor Unit MS — Memory StickMS — MicrosoftMSB — Most Significant Bit MS-DOS — Microsoft DOSMT — Machine TranslationMTA — Mail Transfer AgentMTU — Maximum Transmission Unit MSA — Mail Submission Agent MSDN — Microsoft Developer Network MSI — Medium-Scale Integration MSI — Microsoft InstallerMUA — Mail User AgentMUD — Multi-User DungeonMVC — Model-View-ControllerMVP — Most Valuable Professional MVS — Multiple Virtual Storage MX — Mail exchangeMXF — Material Exchange Format NNACK — Negative ACKnowledgement NAK — Negative AcKnowledge Character NAS — Network-Attached Storage NAT — Network Address Translation NCP — NetWare Core ProtocolNCQ — Native Command Queuing NCSA — National Center for Supercomputing ApplicationsNDPS — Novell Distributed Print Services NDS — Novell Directory Services NEP — Network Equipment Provider NEXT — Near-End CrossTalk NFA — Nondeterministic Finite Automaton GNSCB — Next-Generation Secure Computing BaseNFS — Network File SystemNI — National InstrumentsNIC — Network Interface Controller NIM — No Internal Message NIO — New I/ONIST — National Institute of Standards and TechnologyNLP — Natural Language Processing NLS — Native Language Support NP — Non-Deterministic Polynomial-TimeNPL — Netscape Public License NPU — Network Processing Unit NS —NetscapeNSA — National Security Agency NSPR — Netscape Portable Runtime NMI — Non-Maskable Interrupt NNTP — Network News Transfer Protocol NOC — Network Operations Center NOP — No OPerationNOS — Network Operating System NPTL — Native POSIX Thread Library NSS — Novell Storage Service NSS — Network Security Services NSS —Name Service SwitchNT — New TechnologyNTFS — NT FilesystemNTLM — NT Lan ManagerNTP — Network Time Protocol NUMA — Non-Uniform Memory Access NURBS — Non-Uniform Rational B-Spline NVR - Network Video Recorder NVRAM — Non-Volatile Random Access Memory OOASIS — Organization for the Advancement of StructuredInformation StandardsOAT — Operational Acceptance Testing OBSAI — Open Base Station Architecture InitiativeODBC — Open Database Connectivity OEM — Original Equipment Manufacturer OES — Open Enterprise ServerOFTC — Open and Free Technology Community OLAP — Online Analytical Processing OLE — Object Linking and Embedding OLED — Organic LightEmitting Diode OLPC — One Laptop per Child OLTP — Online Transaction Processing OMG — Object Management Group OO — Object-Oriented OO — Open OfficeOOM — Out of memoryOOo — OOP — Object-Oriented Programming OPML — Outline Processor Markup Language ORB — Object Request Broker ORM — Oject-Relational Mapping OS — Open SourceOS — Operating SystemOSCON — O'Reilly Open Source Convention OSDN — Open Source Developer Network OSI — Open Source Initiative OSI — Open Systems Interconnection OSPF — Open Shortest Path First OSS — Open Sound SystemOSS — Open-Source SoftwareOSS — Operations Support System OSTG — Open Source Technology Group OUI — Organizationally Unique Identifier PP2P — Peer-To-PeerPAN — Personal Area Network PAP — Password Authentication Protocol PARC — Palo Alto Research Center PATA — Parallel ATAPC — Personal ComputerPCB — Printed Circuit BoardPCB — Process Control BlockPCI — Peripheral Component Interconnect PCIe — PCI ExpressPCL — Printer Command Language PCMCIA — Personal Computer Memory Card InternationalAssociationPCM — Pulse-Code ModulationPCRE — Perl Compatible Regular Expressions PD — Public Domain PDA — Personal Digital Assistant PDF — Portable Document Format PDP — Programmed Data Processor PE — Physical ExtentsPEBKAC — Problem Exists Between Keyboard And ChairPERL — Practical Extraction and Reporting LanguagePGA — Pin Grid ArrayPGO — Profile-Guided Optimization PGP — Pretty Good PrivacyPHP — PHP: Hypertext Preprocessor PIC — Peripheral Interface Controller PIC — Programmable Interrupt Controller PID — Proportional-Integral-Derivative PID — Process IDPIM — Personal Information Manager PINE — Program for Internet News & EmailPIO — Programmed Input/Output PKCS — Public Key Cryptography Standards PKI — Public Key Infrastructure PLC — Power Line Communication PLC — Programmable Logic Controller PLD — Programmable Logic Device PL/I — Programming Language One PL/M — Programming Language for MicrocomputersPL/P — Programming Language for Prime PLT — Power Line Telecoms PMM — POST Memory ManagerPNG — Portable Network Graphics PnP — Plug-and-PlayPoE — Power over EthernetPOP — Point of PresencePOP3 — Post Office Protocol v3 POSIX — Portable Operating System Interface POST — Power-On Self TestPPC — PowerPCPPI — Pixels Per InchPPP — Point-to-Point Protocol PPPoA — PPP over ATMPPPoE — PPP over EthernetPPTP — Point-to-Point Tunneling Protocol PS — PostScriptPS/2 — Personal System/2PSU — Power Supply UnitPSVI — Post-Schema-Validation Infoset PV — Physical VolumePVG — Physical Volume GroupPVR — Personal Video RecorderPXE — Preboot Execution Environment PXI — PCI eXtensions for Instrumentation QQDR — Quad Data RateQA — Quality AssuranceQFP — Quad Flat PackageQoS — Quality of ServiceQOTD — Quote of the DayQt — Quasar ToolkitQTAM — Queued Teleprocessing Access Method RRACF — Resource Access Control Facility RAD — Rapid Application Development RADIUS — Remote Authentication Dial In User Service RAID — Redundant Array of Independent Disks RAID — Redundant Array of Inexpensive Disks RAIT — Redundant Array of Inexpensive Tapes RAM —Random Access MemoryRARP — Reverse Address Resolution Protocol RAS — Remote Access ServiceRC — Region CodeRC — Release CandidateRC — Run CommandsRCS — Revision Control SystemRDBMS — Relational Database Management SystemRDF — Resource Description Framework RDM — Relational Data Model RDS — Remote Data ServicesREFAL — REcursive Functions Algorithmic LanguageREST — Representational State Transfer regex — Regular Expression regexp — Regular Expression RF — Radio FrequencyRFC — Request For CommentsRFI — Radio Frequency Interference RFID — Radio Frequency Identification RGB — Red, Green, BlueRGBA — Red, Green, Blue, Alpha RHL — Red Hat LinuxRHEL — Red Hat Enterprise Linux RIA — Rich Internet Application RIAA — Recording Industry Association of AmericaRIP — Raster Image Processor RIP — Routing Information Protocol RISC — Reduced Instruction Set Computer RLE — Run-Length Encoding RLL — Run-Length LimitedRMI — Remote Method Invocation RMS — Richard Matthew Stallman ROM — Read Only MemoryROMB — Read-Out Motherboard RPC — Remote Procedure Call RPG —Report Program Generator RPM — RPM Package ManagerRSA — Rivest Shamir Adleman RSI — Repetitive Strain Injury RSS —Rich Site Summary, RDF Site Summary, or Really SimpleSyndicationRTC — Real-Time ClockRTE — Real-Time EnterpriseRTL — Right-to-LeftRTOS — Real Time Operating System RTP — Real-time Transport Protocol RTS — Ready To SendRTSP — Real Time Streaming Protocol SSaaS — Software as a Service SAN — Storage Area NetworkSAR — Search And Replace[1]SATA — Serial ATASAX — Simple API for XMLSBOD — Spinning Beachball of Death SBP-2 — Serial Bus Protocol 2 sbin — superuser binarySBU — Standard Build UnitSCADA — Supervisory Control And Data AcquisitionSCID — Source Code in Database SCM — Software Configuration Management SCM — Source Code Management SCP — Secure Copy SCPI — Standard Commands for Programmable Instrumentation SCSI — Small Computer System Interface SCTP — Stream Control Transmission Protocol SD — Secure DigitalSDDL — Security Descriptor Definition LanguageSDI — Single Document InterfaceSDIO — Secure Digital Input OutputSDK — Software Development KitSDL — Simple DirectMedia LayerSDN — Service Delivery NetworkSDP — Session Description ProtocolSDR — Software-Defined RadioSDRAM — Synchronous Dynamic Random Access MemorySDSL — Symmetric DSLSE — Single EndedSEAL — Semantics-directed Environment Adaptation Language SEI — Software Engineering InstituteSEO — Search Engine OptimizationSFTP — Secure FTPSFTP — Simple File Transfer ProtocolSFTP — SSH File Transfer ProtocolSGI — Silicon Graphics, IncorporatedSGML — Standard Generalized Markup LanguageSHA — Secure Hash AlgorithmSHDSL — Single-pair High-speed Digital Subscriber LineSIGCAT — Special Interest Group on CD-ROM Applications andTechnologySIGGRAPH — Special Interest Group on GraphicsSIMD — Single Instruction, Multiple DataSIMM — Single Inline Memory ModuleSIP — Session Initiation ProtocolSIP — Supplementary Ideographic PlaneSISD — Single Instruction, Single Data SLED — SUSE LinuxEnterprise Desktop SLES — SUSE Linux Enterprise Server SLI — Scalable Link Interface SLIP — Serial Line Internet Protocol SLM — Service Level Management SLOC — Source Lines of Code SPMD — Single Program, Multiple Data SMA — SubMiniature version A SMB — Server Message Block SMBIOS — System Management BIOS SMIL — Synchronized Multimedia Integration LanguageS/MIME — Secure/Multipurpose Internet Mail ExtensionsSMP — Supplementary Multilingual Plane SMP — Symmetric Multi-Processing SMS — Short Message Service SMS — System Management Server SMT — Simultaneous Multithreading SMTP — Simple Mail Transfer Protocol SNA — Systems Network Architecture SNMP — Simple Network Management Protocol SOA — Service-Oriented Architecture SOE — Standard Operating Environment SOAP — Simple Object Access Protocol SoC — System-on-a-ChipSO-DIMM — Small Outline DIMM SOHO — Small Office/Home OfficeSOI — Silicon On InsulatorSP — Service PackSPA — Single Page Application SPF — Sender Policy Framework SPI —Serial Peripheral Interface SPI — Stateful Packet Inspection SPARC —Scalable Processor Architecture SQL — Structured Query Language SRAM —Static Random Access Memory SSD — Software Specification Document SSD - Solid-State DriveSSE — Streaming SIMD Extensions SSH — Secure ShellSSI — Server Side Includes SSI — Single-System Image SSI — Small-Scale Integration SSID — Service Set Identifier SSL — Secure Socket Layer SSP — Supplementary Special-purpose Plane SSSE — Supplementary Streaming SIMD Extensionssu — superuserSUS — Single UNIX Specification SUSE — Software und System-Entwicklung SVC — Scalable Video Coding SVG — Scalable Vector Graphics SVGA — Super Video Graphics Array SVD — Structured VLSI Design SWF —Shock Wave FlashSWT — Standard Widget Toolkit Sysop — System operatorTTAO — Track-At-OnceTB — TerabyteTcl — Tool Command Language TCP — Transmission Control Protocol TCP/IP — Transmission Control Protocol/Internet ProtocolTCU — Telecommunication Control Unit TDMA — Time Division Multiple Access TFT — Thin Film Transistor TI — Texas Instruments TLA — Three-Letter Acronym TLD — Top-Level DomainTLS — Thread-Local Storage TLS — Transport Layer Security tmp —temporaryTNC — Terminal Node Controller TNC — Threaded Neill-Concelman connector TSO — Time Sharing OptionTSP — Traveling Salesman Problem TSR — Terminate and Stay Resident TTA — True Tap AudioTTF — TrueType FontTTL — Transistor-Transistor Logic TTL — Time To LiveTTS — Text-to-SpeechTTY — TeletypeTUCOWS — The Ultimate Collection of Winsock SoftwareTUG — TeX Users GroupTWAIN - Technology Without An Interesting NameUUAAG — User Agent Accessibility Guidelines UAC — User Account Control UART — Universal Asynchronous Receiver/Transmitter UAT — User Acceptance Testing UCS — Universal Character SetUDDI — Universal Description, Discovery, and Integration UDMA — Ultra DMAUDP — User Datagram Protocol UE — User ExperienceUEFI — Unified Extensible Firmware Interface UHF — Ultra High Frequency UI — User InterfaceUL — UploadULA — Uncommitted Logic Array UMA — Upper Memory AreaUMB — Upper Memory BlockUML — Unified Modeling Language UML — User-Mode LinuxUMPC — Ultra-Mobile Personal Computer UNC — Universal Naming Convention UPS — Uninterruptible Power Supply URI — Uniform Resource Identifier URL — Uniform Resource Locator URN — Uniform Resource Name USB — Universal Serial Bus usr — userUSR — U.S. RoboticsUTC — Coordinated Universal Time UTF — Unicode Transformation FormatUTP — Unshielded Twisted Pair UUCP — Unix to Unix CopyUUID — Universally Unique Identifier UVC — Universal Virtual Computer Vvar — variableVAX — Virtual Address eXtension VCPI — Virtual Control Program Interface VR — Virtual RealityVRML — Virtual Reality Modeling Language VB — Visual BasicVBA — Visual Basic for Applications VBS — Visual Basic Script VDSL — Very High Bitrate Digital Subscriber LineVESA — Video Electronics Standards AssociationVFAT — Virtual FATVFS — Virtual File SystemVG — Volume GroupVGA — Video Graphics ArrayVHF — Very High FrequencyVLAN — Virtual Local Area Network VLSM — Variable Length Subnet Mask VLB — Vesa Local BusVLF — Very Low FrequencyVLIW - Very Long Instruction Word— uinvac VLSI — Very-Large-Scale Integration VM — Virtual MachineVM — Virtual MemoryVOD — Video On DemandVoIP — Voice over Internet Protocol VPN — Virtual Private Network VPU — Visual Processing Unit VSAM — Virtual Storage Access Method VSAT — Very Small Aperture Terminal VT — Video Terminal?VTAM — Virtual Telecommunications Access MethodWW3C — World Wide Web Consortium WAFS — Wide Area File ServicesWAI — Web Accessibility Initiative WAIS — Wide Area Information Server WAN — Wide Area NetworkWAP — Wireless Access Point WAP — Wireless Application Protocol WAV — WAVEform audio format WBEM — Web-Based Enterprise Management WCAG — Web Content Accessibility Guidelines WCF — Windows Communication Foundation WDM — Wavelength-Division Multiplexing WebDAV — WWW Distributed Authoring and VersioningWEP — Wired Equivalent Privacy Wi-Fi — Wireless FidelityWiMAX — Worldwide Interoperability for Microwave AccessWinFS — Windows Future Storage WINS- Windows Internet Name Service WLAN — Wireless Local Area Network WMA — Windows Media Audio WMV — Windows Media VideoWOL — Wake-on-LANWOM — Wake-on-ModemWOR — Wake-on-RingWPA — Wi-Fi Protected Access WPAN — Wireless Personal Area Network WPF — Windows Presentation Foundation WSDL — Web Services Description Language WSFL — Web Services Flow Language WUSB — Wireless Universal Serial Bus WWAN — Wireless Wide Area Network WWID — World Wide Identifier WWN — World Wide NameWWW — World Wide WebWYSIWYG — What You See Is What You Get WZC — Wireless Zero Configuration WFI — Wait For InterruptXXAG — XML Accessibility Guidelines XAML — eXtensible Application Markup LanguageXDM — X Window Display Manager XDMCP — X Display Manager Control Protocol XCBL — XML Common Business Library XHTML — eXtensible Hypertext Markup Language XILP — X Interactive ListProc XML —eXtensible Markup Language XMMS — X Multimedia SystemXMPP — eXtensible Messaging and Presence ProtocolXMS — Extended Memory SpecificationXNS — Xerox Network Systems XP — Cross-PlatformXP — Extreme ProgrammingXPCOM — Cross Platform Component Object ModelXPI — XPInstallXPIDL — Cross-Platform IDLXSD — XML Schema Definition XSL — eXtensible Stylesheet Language XSL-FO — eXtensible Stylesheet Language Formatting Objects XSLT — eXtensible Stylesheet Language TransformationsXSS — Cross-Site ScriptingXTF — eXtensible Tag Framework XTF — eXtended Triton Format XUL —XML User Interface Language YY2K — Year Two ThousandYACC — Yet Another Compiler Compiler YAML — YAML Ain't Markup Language YAST — Yet Another Setup Tool ZZCAV — Zone Constant Angular Velocity ZCS — Zero Code Suppression ZIF — Zero Insertion ForceZIFS — Zero Insertion Force Socket ZISC — Zero Instruction Set Computer ZOPE — Z Object Publishing Environment ZMA — Zone Multicast Address。

13-22以用户为中心的场景感知与分析技术

Information Technology Letter Sep. 2010 以用户为中心的场景感知与分析技术叶剑朱珍民陈援非何哲摘要以用户为中心，基于场景感知实现任务计算是普适计算所追求的目标之一。

但怎样从高维、非线性，并且具有一定耦合度的情境数据中提取场景特征，使计算系统能够在复杂的场景变化中完成用户的计算要求，是研究的难点和热点。

本文主要研究在复杂的普适计算环境中，面向以用户为中心的计算需求实现智能场景感知。

研究内容包括建立分布式模糊推理模型和参数学习方法；基于流形学习和云模型方法，研究场景特征分析技术，实现场景特征提取；基于场景感知，建立用户偏好计算模型，实现以用户为中心的普适计算模型。

关键词情境感知场景分析模糊推理用户偏好模型1引言普适计算环境的特点是具有随时随地访问信息和不可见的计算能力，计算透明性和无处不在是普适计算重点强调的技术特性。

因此，普适计算以人为本而不是以计算机为中心，迎合了信息空间与物理空间的融合趋势，成为国际上一个蓬勃发展的研究热点。

在动态的普适计算环境中，用户任务的精确执行与环境的情境（如地点、时间）以及用户的个性化信息（如偏好等）紧密相关，因此用户任务是场景依存的。

场景感知的最终目的就是为普适计算环境中的用户任务计算提供决策支持。

但围绕用户这一中心，重用采集的场景信息，对场景做出准确分析和判断，仍然是普适计算技术实现过程中困扰我们的难题。

它不仅具有重要的理论意义，而且在移动运营增值服务、智能家居、城市综合信息服务领域有着广泛的应用前景。

当前，具有普适计算特征的研究项目对于物理空间特征的定义还停留在情境（又称为上下文）层面，通过对诸如位置、时间、用户身份、计算能力等情境的感知，获得高层情境语义。

这种方式引发了“情境鸿沟”[1][2]问题，使得系统提供的情境信息与用户需要获得的情境之间存在差异。

这一方面是由于传感装置的精度、情境表示方式引起数据表达的不准确（情境通常是数量值和语义概念混杂的数据，情境之间还会相互影响），使情境信息难以完整地体现环境的特点；另一方面，用户需求是一个高层的抽象目标，与情境的抽象级别之间存在显著的不匹配现象，势必会造成情境知识与用户需求之间存在差距，影响用户与普适计算环境之间交互决策的正确性。

基于知识图谱的查询语句重写机制及方法

第39卷第1期2021年1月吉林大学学报(信息科学版)Journal of Jilin University(Information Science Edition)Vol.39No.1Jan.2021文章编号：1671-5896(2020)01-0087-07基于知识图谱的查询语句重写机制及方法刘思培I,蔡一凡2,曹玲玲-侯海婷-鲍家坤-袁鸯I(1.北方信息控制研究院集团有限公司总体部，南京211111；2.吉林大学软件学院，长春130012)摘要：随着语义Web技术和知识图谱的出现，目前查询模式大多要求查询结果与用户查询进行语义级匹配，简单的查询处理过程已经不能满足用户的査询需求。

为此，对知识图谱查询涉及的重写技术和实现方法进行了研究，在定义SPARQL(SPARQL Protocol and RDF Query Language)查询模式的重写规则集合基础上，利用Prolog逻辑程序对SPARQL查询语句进行了重写实现。

在分布式数据存储环境下，通过对LUBM(Lehigh University Benchmark)实验数据的测试分析证实，相比原查询语句，重写后的查询语句能挖掘出知识图谱中更多的语义信息。

关键词：知识图谱；本体；SPARQL查询语言；查询重写中图分类号：TP181;TG156文献标识码：AMechanism and Method of Query Rewriting for Knowledge GraphLIU Sipei1,CAI Yifan2,CAO Lingling1,HOU Haiting1,BAO Jiakun1,YUAN Yang1(1.Overall Department,North Information Control Research Acdemy Group Company Limited,Nanjing211111,China；2.College of Software,Jilin University,Changchun130012,China)Abstract:With the emergence of semantic web technologies and knowledge maps,most of the current query models require semantic matching between query results and user queries.The simple query process can not meet the user's query requirements.Therefore,the rewriting techniques and implementation methods involved in knowledge graph query are studied.On the basis of defining the rewrite rule set of SPARQL(SPARQL Protocol and RDF Query Language)query mode,the SPARQL is rewritten by Prolog.In the distributed data storage environment,through the test analysis of the experimental data of LUBM(Lehigh University Benchmark),it is found that the rewritten query can mine more semantic information in the knowledge map than the original query. Key words:knowledge graph；ontology；SPARQL protocol and RDF query language(SPARQL)；query rewriting0引言知识图谱首先由Google提出，主要由模式层和数据层组成⑷。

元数据格式汇总

元数据格式汇总iii1. DC(都柏林核心元数据)2. CDWA(艺术作品描述目录)3. V AR Core(可视资源委员会核心元数据)4. CDF(频道定义格式)5. ROADS元数据（主题信息服务的资源组织和发现）6. IEEE LOM（IEEE学习对象元数据）7. BibTex(科技文献书目资源格式)8. GEM（教育资源网关）9. CIMI(博物馆信息计算机交换标准框架)10. REACH元数据格式11. EAD(编码文档描述)12. ONIX(在线信息交换)13. EELS(工程电子化图书馆)14. EEVL(爱丁堡工程虚拟图书馆)15. FGDC(联邦地理数据委员会)16. GILS(政府信息定位服务)17. MARC(机读目录格式)18. MOA2（美国的创建II）19. MCF(元内容框架)20. PICA+(荷兰图书馆自动化中心)21. PICS(网络内容选择平台)22. TEI Header(文本编码先导计划)23. SOIF(概略对象交换格式)24. IAFA/WHIOS++Templates(因特网匿名FTP文件库版式)25. ICPSR SGML Codebook(政治和社会研究方面的校际联盟)26. LDAP DIF(轻便型目录获取协议)27. RFC 1807(书目记录格式)28. URCs(统一资源特征)29. SGML(通用标准标记语言)30. Warwick Framework(Warwick框架)31. Web Collections(网站集合)32. XML(可扩展标记语言)33. RDF(资源描述框架)1.DC(都柏林核心元数据)名称：Dublin Core Metadata,DC简介：都柏林核心元数据是一个由计算机专家、网络专家和图书馆专家等人员所组成的非正式小组开发的，目的是要建立一个广泛的元数据元素集，可以描述任何网络信息资源，并足够的简单以至任何作者无需专门的培训就可以创建自己文件的元数据。

基于RDF的教育资源描述

基于RDF的教育资源描述
姓名导师
张书涵夏幼明教授
1 2 3
元数据与教育资源建设技术规范
XML语言与资源描述框架RDF 基于RDF模型的半结构化知识表示
教育资源特征提取与分类算法
4
1
元数据与教育资源技术规范
■ 什么是元数据？元数据，最常见的宽泛定义是关于数据的数据，具体讲的是关于数据的结构化数据。元数据可以出现在数据内部、独立于数据、伴随
具。
1
元数据与教育资源技术规范
■ 现有的教育资源元数据标准
3、中国网络教育技术标准英文名称China E-Learning Technology Standards，简称CELTS。它于2001年由中国教育信息化技术标准委员会提出，是一个具有中国特色的网络技术标准。
1
元数据与教育资源技术规范
■ 现有的教育资源元数据标准
3、中国网络教育技术标准
《学习对象元数据规范CELTS-3》《教育资源建设技术规范CELTS-41》《基础教育教学资源元数据规范CELTS-42》
2
XML语言与资源描述框架RDF
■ 什么是资源描述框架RDF？ RDF是一种元数据框架，它借助网络实现机器可识别
应用程序之间的互操作性，使网络资源自动化处理。 RDF的主要目标是为了解决互联网中信息的语义化，它
支持对元数据语义的描述以及元数据之间的互操作性，在
应用中也支持基于推理的知识发现而不是全文匹配检索。
2
XML语言与资源描述框架RDF
■ RDF框架
RDF数据模型、RDF模式和RDF语法
1、RDF数据模型
RDF数据模型形成对资源的形式描述，通过使用标记图
（或“节点和弧”图）表示，包括：资源、属性、属性值

自动化系统与集成面向制造的数字孪生框架第4部分：信息交换-最新国标

自动化系统与集成面向制造的数字孪生框架第4部分：信息交换1 范围本文件规定了参考体系结构中实体之间信息交换的技术要求。

以下网络中信息交换的要求属于本文件范围：——连接用户实体和数字孪生实体的用户网络；——连接数字孪生实体内子实体的业务网络；——将设备通信实体连接到数字孪生实体和用户实体的接入网络；——将设备通信实体与可观测制造元素相连接的邻近网络。

2 规范性引用文件下列文件中的内容通过文中的规范性引用而构成本文件必不可少的条款。

其中，注日期的引用文件，仅该日期对应的版本适用于本文件；不注日期的引用文件，其最新版本（包括所有的修改单）适用于本文件。

ISO 23247-1 自动化系统与集成面向制造的数字孪生系统框架第1部分：概述和一般原则（Automation systems and integration — digital twin framework for manufacturing — Part 1: Overview and general principles）ISO 23247-2 自动化系统与集成面向制造的数字孪生系统框架第2部分：参考架构（Automation systems and integration — digital twin framework for manufacturing — Part 2: Reference architecture）3 术语和定义ISO 23247-1界定的以及下列术语和定义适用于本文件。

ISO和IEC维护用于标准化的术语数据库网址如下：——ISO在线浏览平台网址电工百科网址——IEC提供设备通信的（一组）系统或设备。

示例：单元控制器向制造单元内的设备发送指令，并从设备上的传感器收集结果。

ISO 23247-2:2021,3.4][来源：为数字孪生模型提供实现、管理、同步和模拟等功能的（一组）系统。

示例：为制造单元提供模拟、同步以及数据分析的系统。

RDF数据在关系数据库中的表示

摘要：资源描述框架是一个用于描述Ｗｅｂ资源的元数据通用框架，大量的ＲＤＦ数据
操作处理需要数据库的支持。阐述了ＲＤＦ模型、ＲＤＦ语法以及ＲＤＦ数据在关系数据
库中的存储。
关键词：ＲＤＦ；关系数据库；数据存储
中图分类号：ＴＰ３１１．１３２．３
文献标识码：Ａ
资源描述框架（ＲｅｓｏｕｒｃｅＤｅｓｃｒｉｐｔｉｏｎＦｒａｍｅｗｏｒｋ简称ＲＤＦ）是Ｗ３Ｃ提出的一个专门用于表达关于Ｗｅｂ资源的元数据（如Ｗｅｂ页面的标题、作者和时间等）的通用框架。由于ＲＤＦ提供了一种基于ＸＭＬ的语法，可以把数据存储在ＸＭＬ文件中，所以数据库系统为数据的存储和检索提供了一种更好途径。虽然基于内存的存储方式存储速度较快，但却受内存大小的限制，只适应于小规模ＲＤＦ数据；虽然因此基于文件系统的存储方式实现简单，但对于大规模ＲＤＦ数据，其查询效率较低。当前流行的关系数据库技术比较成熟，应用广泛，效率比面向对象数据库高，并且它的事务系统可以确保操作的正确，无须再重新开发。所以，目前利用关系数据库来存储ＲＤＦ数据是可行的［１］。
学院电子商务专业２００７级硕士研究生，湖北省武汉市，４３００７２．
ＴｈｅＤｅｓｉｇｎａｎｄＩｍｐｌｅｍｅｎｔａｔｉｏｎｏｆＢｌｏｇＲｅｃｏｍｍｅｎｄａｔｉｏｎＳｙｓｔｅｍＢａｓｅｄｏｎＣｏｎｔｅｎｔ－ｍｉｎｉｎｇ
ＷＵＫｅ－ｗｅｎ
ＡＢＳＴＲＡＣＴ：Ｃｏｍｂｉｎｉｎｇｔｈｅｔｅｘｔｃｌａｓｓｉｆｉｃａｔｉｏｎｔｅｃｈｎｏｌｏｇｙｉｎｔｈｅｃｏｎｔｅｎｔ－ｍｉｎｉｎｇｗｉｔｈｔｈｅｉｎｔｅｒｅｓｔａｔｔｅｎｕａｔｉｏｎ，ｔｈｉｓｐａｐｅｒｐｕｔｓｆｏｒｗａｒｄｔｈｅｒｅｃｏｍｍｅｎｄａｔｉｏｎｔｅｃｈｎｏｌｏｇｙｏｆｌａｔｅｎｔｂｌｏｇｆｒｉｅｎｄｓａｎｄｂｌｏｇｏｓｐｈｅｒｅｓ，ａｎｄｃａｒｒｉｅｓｏｕｔｔｈｅｐｒｏｔｏｔｙｐｅｉｍｐｌｅｍｅｎｔａｔｉｏｎｔｈｅｒｅｃｏｍｍｅｎｄａｔｉｏｎｓｙｓｔｅｍ．ＫＥＹＷＯＲＤＳ：ｂｌｏｇ；ｃｏｎｔｅｎｔ－ｍｉｎｉｎｇ；ｔｅｘｔｃｌａｓｓｉｆｉｃａｔｉｏｎ；ｒｅｃｏｍｍｅｎｄａｔｉｏｎｓｙｓｔｅｍ

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A RDF-based Framework for UserProﬁle Creation and Management Ignazio Palmisano,Domenico Redavid,Luigi Iannone,Giovanni Semeraro,Marco Degemmis,Pasquale Lops,Oriana Licchelli Dipartimento di Informatica,Universit`a degli Studi di BariCampus,Via Orabona4,70125Bari,Italyemail:{palmisano,redavid,iannone,semeraro,degemmis,lops,licchelli}@di.uniba.it Abstract.The semantic evolution of the Web has an heavy impact ontraditional systems,as the ability to use a formal interoperable languagesimpliﬁes information exchange between diﬀerent systems.In order tofoster information exchange and to easily connect new functionalities tosemantic knowledge bases,in order to be able to use and reuse the valu-able knowledge embedded in the existing systems,we designed a plugin-based framework,and used it to connect together diﬀerent tools andsystems developed in the LACAM laboratory.Our pilot project includesuser proﬁling abilities coming from two components,namely Proﬁle Ex-tractor(PE)and Item Recommender(ITR),and storage capabilitiesimplemented by a repository tool called RDFCore.1IntroductionOne of the main points for the Semantic Web to be useful is interoperabil-ity;Semantic Web applications should be able to exchange informationwith(almost)no human intervention needed.By exchanging informa-tion,we mean exchanging meaningful information,i.e.two applicationsA andB should be able to share not only the bare data(which is al-ready doable in a number of ways,one of which is through standards likeXML),but the associate meaning,in a reliable way.For this to be pos-sible,the process must be described in an unambiguous way;the easiestsolution is then to express the knowledge that the applications want toshare in a formal language,with well deﬁned and logically based seman-tics.With this aim,currently two main languages have been deﬁned byW3C:RDF(Resource Description Framework)1and OWL(Web Ontol-ogy Language).2The use of the Semantic Web languages(OWL Full/DL/Lite)enablesapplications to decouple knowledge from application machinery,thus en-ableng other applications to share the meaning,provided that they canunderstand the same logic language.1/RDF/2/2004/OWL/With this aim,we designed a framework to ease the realization of se-mantic applications.The framework is based on a set of interfaces that abstract some common functionalities,built around the concept ofﬂow of information.2Theﬂow metaphorByﬂow of information,we mean the transmission of information(ex-pressed as ontological information in SW languages,with DL semantics as background)from a source to a consumer for that information(sink).Along the path,the informationﬂow can be modiﬁed in many ways;we identify two main approaches for information change:enrichment and transformation.Enriching an informationﬂow means adding new information to thisﬂow;an example could be the use of inference and deduction rules in order to explicit some implicit knowledge or to add new information coming from background knowledge.Transforming an informationﬂow involves the rewriting of the informa-tion;an example could be the change of background ontology for some data,or the merge of two informationﬂows into a single one,involving the use of ontology alignment techniques.Another kind ofﬂow modiﬁcation is the store of an informationﬂow for later use;this is the typical job of a persistent storage component.A sketch of the resulting architecture is in Figure1.The kind of components that implement these interfaces can then be summarized as:–Source plugins:this kind of plugin creates new information(e.g., wrapping an external source of information,such as a database)–Store plugins:this kind of plugin stores information,enabling both persistent storage and retrieval–Transformer plugins:this kind of plugin modiﬁes the information it is fed with(e.g.,changing class or property deﬁnitions)–Enricher plugins:this kind of plugin diﬀers from the Transformer because it does not modify existing informations,but adds new in-formation(e.g.,an external reasoner could be wrapped in this kindof plugin)–Sink plugin:this kind of plugin does not produce or modify infor-mation in the framework(e.g.,a visualization plugin or an externalapplication that needs to get information from the framework) 3Information Flow LanguageAs already said,the Semantic Web languages for ontology expression are RDF and OWL,with the RDFSchema3language as an intermediate level of expressiveness(and of computational complexity).3/TR/rdf-schema/Fig.1.Architecture SketchRDF is primarily focused on the concepts of resource and property:a resource is an identiﬁable entity,e.g.a human being,a web site,or a building,while a property is a relation between two resources or between a resource and a literal value(e.g.a human being is related to his name).A set of triples(Subject,Predicate,Object)is a RDF Model(or Description).The RDF language is the base for the use of languages with a richer semantic,such as RDFSchema and OWL.OWL includes RDFSchema, in order to reuse the concepts already described there,and is divided into three sublanguages(Lite,DL,Full).While in RDFS the main relation is inheritance,i.e.the deﬁnition of subclass/superclass relations between resources and subproperty/su-perproperty relations for properties,OWL introduces a more complex semantic,e.g.restrictions on properties(it is possible to deﬁne cardinali-ties and data ranges for properties);the main advantage of this language, however,is the well deﬁned semantic of the deﬁned relations;this enables the construction of automatic reasoners that are not limited to a particu-lar domain or to a particular implementation.Since OWL ontologies are expressed in RDF,there is no need for a separate storage layer for OWL data;and,since RDF is an abstract speciﬁcation that can have diﬀerent representations(see RDF/XML,Notation3,N-Triples,Turtle),it is pos-sible to exchange RDF data between application without imposing an a priori representation.As a consequence,the language for the informationﬂow in our framework is RDF.4Framework Test CaseIn order to verify the framework in a practical scenario,we used the deﬁned interfaces to wrap up other components developed in the LACAM lab.The components we included so far are:–RDFCore:a component for RDF storage,wrapped as a store plugin –Proﬁle Extractor:a component for supervised learning of user clas-siﬁcation rules,wrapped up as an enrichment plugin–ITR(ITem Recommender):a component for content based classiﬁ-cation,based on na¨ıve Bayes classiﬁers;from this component,which originally was a Java Web Application,many plugins have been cre-ated:•an enrichment plugin,that encloses the learning abilities of the system,in a way similar to Proﬁle Extractor•a source plugin,that encapsulates the part of the web applica-tion that gains data from users and domain experts •a sink plugin,that contains the result display part of the system The resulting istance of the framework is biased towards the user mod-eling domain,as is easy to see from the description of the systems that foollows;other work in this area has been done,for example UUCM(Uniﬁed User Context Model)[5],which is based on an extensible repre-sentation for models.UUCM provides a simple schema to describe diﬀer-ent dimensions of user models;each dimension can be described through values that can be either simple types(such as strings,numbers,dates) or be typed.In this last case,the type of the value is expressed as classes deﬁned in OWL language.The components we integrated are not tied to a particular ontology,but can be used with any OWL ontology like UUCM.4.1RDFCoreThe RDFCore component,presented in[3],is a component used for RDF descriptions storage and retrieval,including multiuser support and extensible support for query languages.The main modules of RDFCore are DescriptionManager and TripleM-anager.Theﬁrst one gives access to Creation,Retrieval,Updating and Deletion(CRUD)operations on RDF models seen as a whole,while the second component enables the same operations at the single assertion level.Both modules use the Jena Semantic Web Toolkit[2]API to work with RDF models.The component also oﬀers multiuser support;users can choose whether some of the models they own should be private,publicly readable or writable,and can restrict access to single users or groups of users.This support is useful when designing cooperative applications,thus enabling geographically dispersed teams to work together easily.RDFCore has been adopted in the VIKEF Project as the basic compo-nent for RDF metadata storage in the VIKE(Virtual Information and Knowledge Environment)Framework,where its SOAP4-exposed services have been wrapped as a Web Service5for metadata storage,retrieval and querying.In Figure2there is a small sketch of the system architecture.Fig.2.Architecture of the RDFCore system4/2000/xp/Group/5/2002/ws/4.2Proﬁle ExtractorThe Proﬁle Extractor(PE)[1]is a module that classiﬁes users using supervised learning techniques.It can be used to discover users’prefer-ences by analyzing data relative to user interaction or other data that are gathered from diﬀerent data sources,such as data warehouse or transac-tions,in order to infer rules describing the user behavior.More in detail, these data are represented in RDF and refer to a simple ontology de-signed to be used as UUCM value type.The ontology is actually limited in its scope,since the PE component is limited to the use of zero or-der data(vectors of attribute/value pairs),and cannot exploit relational knowledge available in the input data.To build proﬁles,the PE component uses decision rules induced from training data,through the use of well-known Machine Learning tech-niques,such as partition trees.In order for the rules to be inferred in an eﬃcient way,and to maximize the predictive power of the inferred rules, it is necessary to establish what features and attributes,in the avail-able data,are useful to accomplish the learning task,and what data,on the other hand,would not increase the predictive power or could waste computation time.The other main problem concerns the deﬁnition of meaningful classes to learn,which are to be deﬁned before the learning task starts.The problem of learning user preferences can be cast to the problem of inducing general concepts from examples labeled as members(or non-members)of the concepts.In this context,given for example aﬁnite set of categories of interest C={c1,c2,...c n},the task may consist in“learning the target concept T i users interested in the category c i”. In the training phase,the users are positive examples for the categories they like/are interested,and negative examples for the categories they don’t like/have interest.We chose an operational description of the tar-get concept T i,using a collection of rules that match against the features describing a user in order to decide if he/she is a member of T i.Hence, the problem is reduced to the combination of a number of binary classi-ﬁers,in this speciﬁc context.For particular classes,where the expected value is not binary(like/dislike),but has more possible values(likes much/enough/little/nothing),the solution is still valid,but the classiﬁer will not be binary;this could result in a small increase in the required computational time.4.3ITem RecommenderITR(ITem Recommender)implements a probabilistic learning algorithm, the na¨ıve Bayes classiﬁer,relying on a content-based approach.The pro-totype is able to classify documents as interesting or uninteresting for a particular user,on the ground of the textual content of the documents. This approach is analog to the relevance feedback in Information Re-trieval[6],which adapts the query vector by iteratively absorbing users relevance judgments on newly returned documents.In the Information Filtering paradigm,the tuned query vector is actually a proﬁle model,specifying both keywords and their informative power.Based on the con-structed user proﬁle,a new item relevance is measured by computing a similarity measure between the query vector and the item’s feature vec-tor.Learning a user proﬁle generally involves the application of Machine Learning techniques to generate a predictive model based on information that has been previously labeled by the user.To learn user proﬁles,ITR casts the problem as a Text Categorization(TC)problem.The tech-niques used are those that are well-suited for text categorization[7].We consider the problem of learning user proﬁles as a binary TC task: each document has to be classiﬁed as interesting or not w.r.t.the user preferences.Therefore,the set of categories is restricted to c+,that rep-resents the positive class(user-likes),and c−the negative one(user-dislikes).ITR representation is based on bag of concepts(BOC)[8].In this ap-proach each feature corresponds to a single word found in the training set.Theﬁnal outcome of the learning process is a probabilistic model used to classify a new instance in the class c+or c−.The model can be used to build a personal proﬁle that includes those words that turn out to be most indicative of the user’s preferences.4.4Other PluginsOther algorithms developed in the LACAM lab have been ported in the framework or are migrating at the time of writing;in particular, the REDD algorithm[4]has been wrapped in a Transformer plugin,en-abling the applications that use the framework to apply the redundancy detection algorithm on any RDF model they use.REDD is based on blank node semantics,and is able to detect redun-dancies in RDF(and OWL)models,where,for example,multiple blank nodes with no distinguishing features are present in the same model.This is the case,for example,of a remote store that gets updates from other applications;it is possible that one or more applications send the same information more than once,and,while this is not a problem with RDF ground statements(i.e.statements with no blank nodes),since RDF models are deﬁned as triple sets,blank nodes inserted during the life of the model are not recognized as already present;this increases the size of the models without reason,and could also be regarded as an error.Another possible application,which is under experimentation at the time of writing,is the use of REDD to detect redundant deﬁnitions in ontolo-gies;the ongoing project aims at using the framework in the building ofa Prot´e g´e6plugin.5Semantic EvolutionOn the side of algorithm evolution,in the ITR component the update to OWL formalism is strictly related to the switching from keyword-based representation of the user proﬁle to user proﬁles based on concepts 6(bags-of-concepts,BOC,instead of bag-of-words,BOW).While this shift of representation is natural when considering the new environment,we already demonstrated in[8]that the traditional TF-IDF heuristic gains some percents both in precision and recall from the new representation. Moreover,we are currently doing empirical measures on an evolution of TF-IDF that takes into account the hierarchical relations between concepts,that,informally,redeﬁnes the classical deﬁnition of TF-IDF, which is based on sheer concept occurrence number,taking into account that a more speciﬁc term is also an instance of a more general term,and as consequence,so to speak,each occurrence of the more speciﬁc term counts also as an occurrence of the more general term.6AcknowledgmentsThis research was partially funded by the European Commission under the6th Framework Programme IST Integrated Project VIKEF-Vir-tual Information and Knowledge Environment Framework(Contract no. 507173,Priority2.3.1.7Semantic-based Knowledge Systems;more infor-mation at ).References1. 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