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Trend of Semantic Technology and its applications, especially Semantic Search

Trend of Semantic Technology and its applications, especially Semantic Search

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  • Trend in Semantic Technology and Semantic Search 의미기술의 동향과 의미 검색 Sung-Kook Han Semantic Technology Research Group, Won Kwang University 2010-01-28 skhan@wku.ac.kr page 1
  • Agenda Information Technology and Semantics Trends in Semantic Technology Overview of Semantic Technology Semantic Search Summary 2010-01-28 skhan@wku.ac.kr 2
  • Information Technology and Semantics
  • Information and Communication  Digitally stored information resources are growing.  Communication between Human and Computer is more common.  Communication devices are diverse. Ubiquitous Information Knowledge World-Wide Web Computing Integration Management Delivery and Share Semantics of Information. 2010-01-28 skhan@wku.ac.kr 4
  • Semantic Gap Sender Concept “Jaguar” Symbol Thing Communication Information “Jaguar” Symbol Thing Concept Receiver 2010-01-28 skhan@wku.ac.kr 5
  • Missing Piece: Semantics Business Process Digital Content Semantics Device Convergence Internet and Web 2010-01-28 skhan@wku.ac.kr 6
  • Related Technologies Controlled Vocabulary + Grouping Classification Controlled Hierarchical Vocabulary + Structure Taxonomy Controlled Term Vocabulary + Relations Thesaurus Controlled Semantic Relation, Vocabulary + Constraints, Axioms, Rules Ontology Ontology + Instances Knowledge Base 2010-01-28 skhan@wku.ac.kr 7
  • Ontology Spectrum: One View Modal Logic strong semantics First Order Logic Technologies has_experience_in works Programs Personnel Company Logical Theory Is Disjoint Subclass of Knowledge Representation Project Management S1 illusion Description Logic with transitivity am Agent Natural Language Task Technical Program AS AS AS Department DAML+OIL, OWL property Telecommunication Leo Semantic Interoperability Director EcDARPA Navy Paulnderleez has WISO UML Assistant Conceptual Model Request Director Intelligence Reza Ann Brad Howard Is Subclass of RDF/S Semantic Interoperability XTM Extended ER Thesaurus Has Narrower Meaning Than ER DB Schemas, XML Schema Animal Structural Interoperability Taxonomy Mammal Reptile Is Sub-Classification of Bird Relational Snake Dog Cat Model, XML Syntactic Interoperability Cocker Spaniel weak semantics Lady 2010-01-28 skhan@wku.ac.kr 8
  • AI and Knowledge Engineering Category Theory Domain Theory Denotational Semantics Truth Maintenance Systems Category Theory : Theoretical CS apps- Denotational Semantics, Type Theory Category Theory : Software Spec EMYCIN KIDS SPECware Expert Dempster-shafer Probabilistic Bayesian Evidence Theory Networks Hybrid KR Distributed MYCIN Systems Inference Reasoning Assumption- Decision Graph Semantic based Systems Theory Knledge Partitioning Networks Frame Problem Game Compilation Knowledge Default Logic Abduction Theory Partitioning GPS BUI Circumscription Microtheories LogicKBs SOAR Agents NetL Non-monotonic Logic Reactive JATlite Frame-based KR Agents KQML Spreading Activation Today Classic Formalization PowerLOOM NSF KDI Distributed KJ-ONE Of Context Actors AI LOOM Formal TOVE DARPA DARPA Blackboard CYC ARPA Ontology HPKB RKF, DAML Architectures WAM Description Logics KADS KSI Ontological OIL Planning 1983 Constraint PARKA 1990 Engineering 2001 Logic Prolog III KIF Ontolingua PARLOG GFP OKBC Prolog II Finite Linear BinProlog Prolog Logic Constraint LIFE Domain OZ Theorem Satisfaction Constraint PARKA-DB Proving Solvers Feature Logics ECLiPSe CHIP 2010-01-28 skhan@wku.ac.kr 9
  • Trends in Semantic Technology
  • 의미 기술의 확산 배경 웹 기술과 웹 2.0의 확산 실용화 단계의 시맨틱 웹 서비스지향 시스템의 의미 기반화 디지털 컨버전스와 유비쿼터스 컴퓨팅 2010-01-28 skhan@wku.ac.kr 11
  • 웹 기술과 웹 2.0의 확산 2010-01-28 skhan@wku.ac.kr 12
  • Web 2.0: People-Services-Data Information People Services Data 1/28/2010 skhan@wku.ac.kr 13
  • Semantic Web “The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation” T. Berners-Lee, J. Hendler, O. Lassila, The Semantic Web”, Scientific American, May 2001 기존 웹을 컴퓨터가 처리할 수 있는 잘 정의된 의미 어휘로 확장하여 컴퓨터-컴퓨터, 컴퓨터-인간의 원활한 상호 작용을 실현하는 웹. Ontology Ontology-Annotated Ontology Annotated Agents Web 1/28/2010 skhan@wku.ac.kr 14
  • Semantic Web Ontology Articulation Toolkit End User Ontology Construction Tool Agents Ontologies Community Portal Inference Engine Annotated Web-Page Annotation Metadata Web-Pages Tool Repository 1/28/2010 skhan@wku.ac.kr 15
  • Semantic Web Layers 1/28/2010 skhan@wku.ac.kr 16
  • 차세대 웹 기술 발전 방향 Web 3.0 Web 4.0 Web 3.0 Web 1.0 Web 2.0 Web 1.0 Web 2.0 1/28/2010 skhan@wku.ac.kr 17
  • 서비스 지향: Service-oriented Web Applications Web 2.0 Web Pages Service Applet/Servlet RIA Global Script Enterprise 2.0 1995 2000 Networking Client-Server Stand-alone Objects Database Components Application Windows GUI Local 1980 1990 Text User-Friendly Rich UI 1/28/2010 skhan@wku.ac.kr 18
  • Service-oriented Architecture (SOA) 1. Point to point systems 2. Message-based middleware with integration broker Partner Partner App B App D Warehouse App A App A (J2EE) Warehouse App B Sales Sales (.Net) App C Service Bus / MOM App C App D (.Net) (J2EE) Adapter Adapter Shared Legacy System Legacy Legacy System Application Application Finance Finance Service Oriented Architecture & Enterprise Service Bus Business Consumer Custom Package Business Rules Process “Above the bus” applications applications Engine Orchestration HTTP Enterprise Service Bus Internet Service Provider Routing Transformation (Process) Services Adapter Adapter orchestration Legacy Shared “Below the bus” System System Author: Peter Campbell, ANZ Banking Group Australia 1/28/2010 skhan@wku.ac.kr 19
  • Semantic SOA 2010-01-28 skhan@wku.ac.kr 20
  • Digital Convergence and Ubiquitous Computing Network Effect / Integration Effect / People Effect / Interoperability Effect Semantics / Ontology u-Home Services Convergence u-Government Media Convergence Device Convergence Network u-Library u-Health Convergence u-Commerce u-Learning 2010-01-28 skhan@wku.ac.kr 21
  • Semantic-based Context Awareness 2010-01-28 skhan@wku.ac.kr 22
  • Semantic Technology: Capability From Project 10X 2010-01-28 skhan@wku.ac.kr 23
  • Semantic Technology: Value Innovation 2010-01-28 skhan@wku.ac.kr 24
  • Overview of Semantic Technology
  • Ontology An ontology is a formal, explicit specification of a shared conceptualization. conceptualization [Borst 1997] Shared Knowledge Common Vocabulary 2010-01-28 skhan@wku.ac.kr 26
  • Ontology in a nutshell  Domain Knowledge Model  A vocabulary for representing knowledge about a domain and for describing specific situations in a domain  classes, properties, predicates, and functions, and a set of relationships that necessarily hold among those vocabulary terms.  Shared formal conceptualizations of particular domains that provide a common interpretation of topics that can be communicated between people and applications.  Also allow definition of axioms and constraints on particular concepts and properties.  Ontological Commitment: General agreement to use a vocabulary  Ontology is social contracts. Concept  Agreed, explicit semantics  Understandable to outsiders Instance Relation  (Often) derived in a community process Function Axiom 2010-01-28 skhan@wku.ac.kr 27
  • Ontology  Concepts  concepts of the domain or tasks, which are usually organized in taxonomies  Example: Person, Car, University,…  Relations  a type of interaction between concepts of the domain  Example: subclass-of, is-a, part-of, hasJob, workWith,…,  Functions  a mapping of relations that return some value  Example : John = Father_of (Mary), 2006 = PublingYear(John, Book),…  Axioms  model sentences that are always true  Example: Cow is larger than a dog., a = a + 0,… Concept  Instances Instance Relation  to represent specific elements  Example : Student called Peter,… Function Axiom 2010-01-28 skhan@wku.ac.kr 28
  • Example: Ontology Define-Class Research-Topic (?Res-Topic) Ontolingua (based on KIF) “Text Description here” :DEF OWL (and (Superclass-of ?Res-Topic <owl:Class rdf:about="http://swrc.ontoware.org/ontology#University"> KA-Through-Machine-Learning <rdfs:subClassOf> ------- <owl:Class rdf:about="http://swrc.ontoware.org/ontology#Organization" /> </rdfs:subClassOf> Knowledge-Management <rdfs:subClassOf> KA-Methodologies <owl:Restriction> Evaluation-of-KA <owl:onProper ty rdf:resource="http://swrc.ontoware.org/ontology#hasParts" /> Knowledge-Elicitation <owl:allValuesFrom> (Has-At-Least Approaches ? Res-Topic 1) <owl:Class rdf:about="http://swrc.ontoware.org/ontology#Department" /> (Cardinality Date-of-last-modification Res-Topic 1) </owl:allValuesFrom> (Has-At-Least Related-Topics ?Res-Topic 1))) </owl:Restriction> </rdfs:subClassOf> <rdfs:subClassOf> F-Logic <owl:Restriction> ResearchTopic :: Object <owl:onProperty rdf:resource="http://swrc.ontoware.org/ontology#student" /> ResearchTopic ( <owl:allValuesFrom> [decsription -> “Text Description here”; <owl:Class rdf:about="http://swrc.ontoware.org/ontology#Student" /> Approaches =>> Topics; </owl:allValuesFrom> DateOfLastModification => DATE; </owl:Restriction> </rdfs:subClassOf> RelatedTopics =>> ResearchTopic]. </owl:Class> KA-Through-Machine-Learning:: ResearchTopic. Reuse :: ResearchTopic. Specification-Languages :: ResearchTopic. -------- Evaluation-of-KA :: ResearchTopic. Knowledge-Elicitation :: ResearchTopic.) 2010-01-28 skhan@wku.ac.kr 29
  • RDF Concept Resource (Document) value Property (Information)) (Metadata) (Tag) resource (subject) property (predicate) value (object) Creator http://www.w3.org/Home/Saron Saron Stone property of the web page web page value of being described the predicate creator 2010-01-28 skhan@wku.ac.kr 30
  • RDF: Data Model  Saron Stone is the creator of the resource http://www.w3.org/Home/Saron. Subject (Resource) http://www.w3.org/Home/Saron Predicate (Property) Creator Object (literal) “Saron Stone" resource (subject) property (predicate) value (object) Creator http://www.w3.org/Home/Saron Saron Stone property of the web page web page value of being described the predicate creator 2010-01-28 skhan@wku.ac.kr 31
  • RDF Schema  RDF Schema  RDF Vocabulary Description Language.  For defining an appropriate RDF vocabulary (classes, properties and constraints) for each specific domain.  Comprises very limited predefined primitives: subClassOf, subPropertyOf, domain and range.  Cannot assert that particular properties are equivalent, transitive, reverse of one another, etc. RDF Schema #Book #Person author Property-Centric approach 2010-01-28 skhan@wku.ac.kr 32
  • RDF Schema Core Classes and Properties rdfs:Resource rdfs:Literal Core Class rdfs:XMLLiteral rdfs:Class rdfs:Property rdfs:DataType rdfs:type rdfs:SubClassOf rdfs:SubPropertyOf Core Property rdfs:domain rdfs:range rdfs:Label rdfs:Comment 2010-01-28 skhan@wku.ac.kr 33
  • OWL  Web Ontology Language (OWL) :  RDF/ RDF Schema에 기반을 둔 웹 정보 자원의 의미 기술 표준 언어  Description Logic (DL) 기반의 논리 언어  다양한 개념 구조 표현 가능  3종류의 OWL  OWL-Lite, OWL-DL, OWL-Full  필요에 따라 선택 2010-01-28 skhan@wku.ac.kr 34
  • Semantic Web Standards RDFa Microformat GRDDL 전종홍 외, 시맨틱웹, TTA Jouranl, No 107, 2006년, 10월 1/28/2010 skhan@wku.ac.kr 35
  • Semantic Search
  • Search!! Search!! 2010-01-28 skhan@wku.ac.kr 37
  • Search Engine Market Share  Google by far comprises the largest share of searches.  Microsoft has been trying to buy Yahoo to increase Microsoft’s search share. As of June 12th, both com panies have ended merger talks.  Now, Microsoft merges Powerset… 2010-01-28 skhan@wku.ac.kr 38
  • Rich Content and Vertical Search Amazon Articles Wikipedia Books Blog Blogs Photos Flickr del.icio.us Events Upcoming.org Book marks Music Last.fm Places Dopplr Movies Netflix Products Microsoft Aura 2010-01-28 skhan@wku.ac.kr
  • Rich Content and Vertical Search Video http://kr.youtube.com/ Map http://maps.live.com/ Blog http://www.google.com/blogsearch People http://www.pipl.com/ 2010-01-28 skhan@wku.ac.kr 40
  • User-Friendly Interface Tree http://www.tafiti.com/ Network http://www.kartoo.com/ Space http://www.quintura.com/ 2010-01-28 skhan@wku.ac.kr 41
  • Information Overload 42
  • Beyond the Limits of Keyword Search Productivity of Search The Intelligent Web Web 4.0 2020 - 2030 Reasoning The Semantic Web Web 3.0 Semantic Search The Social Web 2010 - 2020 The World Wide Web Web 2.0 Natural language search Web 1.0 2000 - 2010 Tagging 1990 - 2000 The Desktop Keyword search Directories PC Era 1980 - 1990 Files & Folders Databases Amount of data By Radar Networks 2010-01-28 skhan@wku.ac.kr 43
  • The Age of Semantic Search 2010-01-28 skhan@wku.ac.kr 44
  • The Age of Semantic Search 2010-01-28 skhan@wku.ac.kr 45
  • Typical Semantic Search Engine Freebase General Search Yahoo! Microsearch, … Powerset Hakia Natural Language Search AskMeNow AskWiki … Kango …now UpTake AdaptiveBlue Vertical Search ReportLinker … SemantiNet Delver Social Networking Search Google Social Graph API … Twine Personalized Search MavinIT PSS … 2010-01-28 skhan@wku.ac.kr 46
  • Search Roles Language Input Index Metadata Design Goals Vocabulary Interaction Algorithms Controlled Vocabulary Interaction Tasks Syntax Feedback Linguistics Knowledge Management Behavior User ?Query Search Interface Search Engine Ask, Browse, or Search Again Content Results  No definitive formulation.  Considerable uncertainty.  Complex interdependencies.  Incomplete, contradictory, and changing requirements.  Stakeholders have radically different world views and different frames for understanding information. 2010-01-28 skhan@wku.ac.kr 47
  • Semantic Search Semantic Search attempts to augment and improve traditional search results by using data from the SW. Syntactic Search Semantic Search Document View Bag-of-Words Vocabularies and Concepts Search Approach Word matching Concept matching Search Process One hot Reasoning / Inference Ontology and Semantic Search  Help user formulate semantic queries  Re-formulate or re-interpret queries  Browse domain  Formulate related queries  Interoperability between search application  Semantic indexing of documents 2010-01-28 skhan@wku.ac.kr 48
  • Semantic Search Problems Optimization : Requires massive parallel computer III Example : “What is the best vocation for me how?” Inference : Requires NLP + Interface Engine + Database II Example : “What US Senator took money from foreign entity?” Natural Language : Requires query analysis Example : “What year was Leonardo Da Vinci born?” I Simple : Solvable with Google Statistical Algorithm Example : “read write web blog” Alex Iskol – Read/Write Web 2010-01-28 skhan@wku.ac.kr 49
  • 5 Core technologies for Semantic Search Semantic Tagging Statistics Concept organization Linguistics Natural language Processing Semantic Web Metadata / Ontology Reasoning Artificial Intelligence 2010-01-28 skhan@wku.ac.kr 50
  • Semantic Search Ontology/Metadata Semantic Annotation Query Processing Semantic Semantic Processing User Interaction Search Reasoning Engine System Architecture Service Architecture 2010-01-28 skhan@wku.ac.kr 51
  • Categorical Features of Semantic Search Engine Stand-alone Maintain an concept index of document Architecture Meta Search Use subordinate search engines Coupling Data of documents refer explicitly to Tight coupling concepts of a specific ontology. between documents and ontologies Loose coupling Not committed to any available ontology Transparent Semantic capabilities invisible to the user. User Interaction Interactive Ask for clarification or recommendation Hybrid Both 2010-01-28 skhan@wku.ac.kr 52
  • Categorical Features of Semantic Search Engine Learning Extract from user interaction dynamically User context Hard-coded Ask for query category Manually The user modifies a query. Query modification Query rewritten A query can be optimized by the system. Graph-based Use graph traversal algorithm anonymous Disregard the vocabulary and the semantics Standard Ontology Synonym, hyponym,… property Construction Domain-specific Domain ontology property Ontology technology Language RDF, OWL,… A survey and classification of semantic search approaches by Christoph Mangold 2010-01-28 skhan@wku.ac.kr 53
  • Technology for Semantic Search Augmenting traditional keyword search with semantic techniques Semantic annotation Complex constraint queries Problem solving Semantic connectivity discovery 54
  • Technology for Semantic Search Augmenting traditional keyword search with semantic techniques WordNet synonym and meronym Keyword Concept RDF Repository 55
  • Technology for Semantic Search Semantic annotation Ontology Semantically annotated Document Document 56
  • Technology for Semantic Search Complex constraint queries Ontology Constraint Query Query 57
  • Technology for Semantic Search Problem solving Ontology Query Reasoning Engine 58
  • Technology for Semantic Search Semantic connectivity discovery Semantic Web 59
  • Evaluation of Semantic Search Search phase Feature Functionality Interface Components • keyword(s) • Single text entry Free text input • natural language • Property-specific fields • Boolean operators Operators • semantic constraints • Application-specific syntax • regular expressions Query construction • disambiguate input • Value list Controlled terms • restrict output • Faceted • select predefined queries • Graph • Suggestion list User feedback • pre-query disambiguation • Semantic auto completion • exact, prefix, substring match Syntactic matching • minimal edit distance • stemming Search algorithm • thesauri expansion Semantic matching • graph traversal • RDFS/OWL reasoning 60
  • Evaluation of Semantic Search Search phase Feature Functionality Interface Components • Text • Graph • Selected property values • Tag cloud Data selection • Class specific template • Map • Display vocabulary • Timeline • Calendar Presentation Ordering • Content and link structure based ranking • Ordered list • Tree • Clustering by property or path Organization • Nested box structure • Dynamic clustering • Cluster map • Post-query disambiguation • Facets User feedback • Query refinement • Tag cloud • Recommendation of related resources • Value list refer to: http://swuiwiki.webscience.org/index.php/Semantic_Search_Survey
  • Applications of Semantic Search Library 2.0 Find books related to “Semantic Search” written by TBL. BPM Find PO web services for car repair parts. Medicine What are side-effects of rifamycin? e-Commerce Search the specifications of RFID chips produced by SamTech. Science Which parameters are seriously changed during CO2 combustion? Search = Generic Task 62
  • Summary  Semantic Search is a kind of Generic tasks. • More than simple document search • Diverse applications in BioInfomatics, EcoScience, Medical Science….  Ontology is a key player of Semantic Search. • RDFa, Microformat, GRDDL,… • RDF, RDF Schema, OWL,… • Ontology Annotation and Population • SPARQL and Query processing,  Multi-disciplinary research and development. • Natural Language Processing and Text Mining • Web Science  User-friendly • Diverse vertical semantic search with domain ontologies • Visualization • Mobile Search 63
  • 의미기술의 동향과 의미 검색 경청해 주시어,감사드립니다. 2010-01-28 skhan@wku.ac.kr 64