Semantic Technology: State of the arts and Trends

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Overview of the current state of the arts of semantic technology and future trends …

Overview of the current state of the arts of semantic technology and future trends
Linked Open Data + Context-aware Services = Killer Apps of Semantic Technology

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  • 1. Semantic Technology:The State of the Art and Research Directions Sung-Kook Han 2010.12.03
  • 2. OutlineReview of Semantic TechnologyHot Issues:  Linked Data  Context-awareFuture Research Trends and Conclusions
  • 3. Semantic Technology Semantic technology has been a distinct research field for more than 40 years.  Formal Logic (since Russell and Frege)  Knowledge Representation Systems in AI  Semantic Networks and ATN (William Woods, 1975)  DARPA and European Commission programs in information integration  Development of simple tractable logics  Relational Algebras and Schemas in Database Systems Library Science (classifications, thesauri, taxonomies) New challenges of Semantic Technology: Semantic Web  A massive store of information that computers cannot use  A way to get around needing the “big data warehouse”  Another place where “a little semantics can go a long way”... cf: The Relationship Between Web 2.0 And the Semantic Web - Dr. Mark Greaves, Vulcan, Inc.
  • 4. Ontology Spectrum strong semantics Modal Logic has_experience_in works Company First Order Logic Technologies Knowledge Representation Programs Personnel Logical Theory Is Disjoint Subclass Management S1 illusion Agent Natural Language Project am AS Description Logic of with transitivity Program AS AS DepartmentTelecommunication Task Technical Paulnderleez Leo DAML+OIL, OWL property Semantic Director EcDARPA has WISO Interoperability Request Reza Assistant Director Navy Intelligence UML Ann Brad Howard Conceptual Model 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 Based on Leo Obrst, The Ontology Spectrum & Semantic Models 2010-11-27 skhan@wku.ac.kr 4
  • 5. Semantic Technology Intelligence Integration InteroperabilityMachine-processible Digital Semantics Information Resources Web resources Ontology Services Semantic Image Metadata Audio/Video Technology controlled Documents vocabulary
  • 6. Web Technology Web of machine-processible Data Common vocabularies: Metadata and Ontology Query and reasoningClassic Web Web of Services Internet of ServicesWeb of DocumentsHTML as document formatHTTP URLs as globally unique IDsHyperlinks to connect everything Social Web Connect human-being Web as a platform Programmable APIs and proprietary interfaces Mashups based on a fixed set of data sources
  • 7. Semantic Web  Standardizations  Trio of Semantic Web  Metadata / Ontology: RDF, RDF, OWL  Query Language: SPARQL  Rule Language: RIF (SWRL)  SKOS, RDFa, GRRDL, WSMO,…  SOAP/ REST  Tools and Systems  Authoring, Reasoning Engines,…  835 items in Sweet Tools  Best Practices  Linked Open Data  Semantic MediaWiki  NEPOMUK, SIOC, Garlik  W3C Semantic Web Use cases Sweet Tools: http://www.mkbergman.com/new-version-sweet-tools-sem-web/ W3C Semantic Web Case Studies and Use Cases: http://www.w3.org/2001/sw/sweo/public/UseCases/2010-11-27 Sung-Kook Han 7
  • 8. Semantic ApplicationsSemantic Wave 2008, Industry Roadmap to Web 3.0, Project10X http://www.mkbergman.com/new-version-sweet-tools-sem-web/
  • 9. Web 2.0  Resharpen the way of viewing the Web  Web as the platform  Web as the social media  Web as the collaboration tool  Web as ……  Web 2.0 Manifestation  Openness / Sharing  Participation / Collaboration  Web 2.0 Syndrome  Library 2.0  Government 2.0  Enterprise 2.0  ……  New Web applications  wiki, blog, RSS,…2010-11-27 Sung-Kook Han 9
  • 10. Web 2.0 Developers
  • 11. Semantic Web Today Major future issues: • Vocabularies • Scalability • Provenance • Personal Infospheres • Mobile and Real World Networks
  • 12. Web 2.0 APIs TodayNo Single global space: Web APIs slice the Web into Walled Gardens. • Mashups of APIs are proprietary. • No links between data. MashUp Web Web Web API API API A B C Christian Bizer: Pay-as-you-go Data Integration (21/9/2010)
  • 13. The Web is Dead?? http://www.wired.com/magazine/2010/08/ff_webrip/
  • 14. Long Live the Web !http://www.scientificamerican.com/article.cfm?id=long-live-the-web
  • 15. Lessons Learned Data is more important than API code.  Data is the Intel Inside.  Open data is more important than open source Structured data is more valuable than unstructured.  We should seek to structure our data well.  Metadata will play a core role of data structure. A little semantics goes a long way.  Beware the usefulness of shallow ontology shown in LOD. Linking data and services are essential.  Link every thing. Rich user experiences are the key for adaption.  We should consider mobile computing and personalization.  Visualize and navigate.
  • 16. Linked Open Data
  • 17. Web of Documents A global file systems of documents (document silos on the Web). Implicit semantics of content and links Designed for human consumption Disconnected data
  • 18. Linked Data: Web of Data Goal: Web-scale Data Integration  Alternative to classic data integration systems in order to cope with growing number of data sources.  Querying Across Data Sources Global distributed database RDF  Extend the Web with a single global data space  Giant Global Graph (GGG) Demonstrate the possibility of Semantic Web  By using RDF to publish structured data RDF  By setting links between data single RDF universal information space. RDF RDF RDF
  • 19. Semantic Web: Web of Data The vision of a Semantic Web:  building a global Web of machine-readable data  Berners-Lee, Hendler & Lassila, 2001; Marshall & Shipman, 2003The first step is putting data on the Web in a form that machines cannaturally understand, or converting it to that form. This creates what I call aSemantic Web - a web of data that can be processed directly or indirectly bymachines. Therefore, while the Semantic Web, or Web of Data, is the goal orthe end result of this process, Linked Data provides the means to reach thatgoal. -- Tim Berners-Lee, et al., http://linkeddata.org/docs/ijswis-special-issue, Jan, 2009 Linked Data Foundation  can lower the barrier to reuse, integration and application of data from multiple, distributed and heterogeneous sources.  the more sophisticated proposals associated with the Semantic Web vision, such as intelligent agents, may become a reality.
  • 20. Linked Data Principles Use URIs as names for things. Use HTTP URIs so that people can look up those names. When someone looks up a URI, provide useful RDF information. Include RDF statements that link to other URIs so that they can discover related things. Community effort to  publish existing open license datasets as Linked Data on the Web  interlink things between different data sources  develop clients that consume Linked Data from the Web
  • 21. Linked Data Model dbp-prop:title The Lord of the rings http://.../isbn/46316 Flexible graph-based model: RDF graph skos:subject dbp-prop:author English novels dbp-prop:publisher The HTTP protocol brings together identification dbp-prop:name and retrieval again. foaf:homepage dbpidia:Allen&Unwin J.R.R. Tolkien opencyc:headquarter dbp-prop:city Deeper into the Web wkp-en:J.R.R.Tolkien London fb:guid…..92df7URI: global primary key fb:creatorskos:subject = http://www.w3.org/2004/02/skos/core#subject fb:street_addressdbp-prop:title = http://dbpedia.org/property/title Marivie 83 Alexander St 83 Alexander
  • 22. Browsing Data Model
  • 23. Summary: the Web of Linked Data A global, distributed database built on a simple set of standards  RDF, URI, HTTP Explicit semantics of content and links Resources are connected by semantic links.  creating a single global data graph that span data sources  enables the discovery of new data sources Provides for data co-existence  Anyone can publish data to the Web of Linked Data  Data publishers are not constrained in choice of vocabularies with which to represent data. Designed for computer first, humans later
  • 24. LOD Data sets on the Web 25 billion RDF triples, which are interlinked by around 395 million RDF links (Sep. 2010). http://richard.cyganiak.de/2007/10/lod/lod-datasets_2010-09-22_colored.svg
  • 25. Supporting Technologies Linked Data Browsers  Provide for navigating between data sources and for exploring the dataspace.  Tabulator Browser (MIT, USA), Marbles (FU Berlin, DE), OpenLink RDF Browser (OpenLink, UK), Zitgist RDF Browser (Zitgist, USA), Disco Hyperdata Browser Berlin, Fenfire (DERI, Irland) Web of Data Search Engines  Crawl the data space and provide best-effort query answers over crawled data.  Falcons (IWS, China), Sig.ma (DERI, Ireland), Swoogle (UMBC, USA), VisiNav (DERI, Ireland), Watson (Open University, UK), TAP, Sindice
  • 26. Supporting Technologies Describing data set  the discovery and usage of linked datasets  voiD, Ding Registry  an open registry of data and content packages  CKAN Linking tool  discovering relationships between data items within different Linked Data sources  SILK Mapping tool  mapping database to RDF triples  Triplify, D2R Server LOD platform  D2R Server, Virtuoso Universal Server, Talis Platform, Pubby, …
  • 27. Data.Gov
  • 28. Europeana European digital library: Europeana: This European Commission initiative encompasses not only libraries but also museums, archives and other holders of cultural heritage material.http://version1.europeana.eu/web/europeana-project
  • 29. Linked Library Cloud Libraries have been producing metadata for ages. Libraries (often) produce high- quality metadata. Library develops many metadata standards such as DC, SKOS, BIBO, OAI-ORE including MARC 21, MODS, FRBR,.. Integrate Library Catalogues on global scale http://code4lib.org/conference/2010/singer
  • 30. Linking Open Drug Data linking the various sources of drug data together to answer interesting scientific and business questions.  Survey publicly available data sets about drugs  Publish and interlink these data sets on the Web  Explore interesting questions that could be answered if the data sets are linked. 8 million RDF triples, which are interlinked by more than 370,000 RDF links (As of August 2009)
  • 31. BBC Semantic Project Publish program / music data as RDF/XML or RDFa Build semantically linked and annotated web pages about artists and singers whose songs are played on BBC radio stations. semantically interconnected
  • 32. DBpedia Mobile Show map with information about nearby locations Linked data browser GPS + Google Maps + Dbpedia + Flickr + Revyu
  • 33. Attention by Search Engines Yahoo!  crawls Linked Data in its RDFa serialization as well as Microformat  Yahoo Search Monkey to make search results more useful and visually appealing Google  use Social Graph API  is developing Google Squared and Google Fusion Table  merged MetaWeb  manage Freebase, a DBpedia/YAGO competitor
  • 34. Linked Open Commerce
  • 35. LOD: Next StepLinking, Integration and Fusion by Semantic Technology
  • 36. Research Agenda User Interfaces and Interaction Paradigms Application Architectures Schema Mapping and Data Fusion Link Maintenance Licensing Trust, Quality and Relevance Privacy• see more details in IJSWIS Special Issue on Linked Data (http://www.ijswis.org/)
  • 37. Context-Aware
  • 38. Context: Concepts ??? Shoes !!! Objects… Services… Objects (including users) embody the establishing meaning. The meaning arises according to the context in the course of action. The services should be autonomously provided by means of the context.
  • 39. Context: Concepts Service CloudSearch and find if you want!! You may need these. I will deliver them.• Developers’ view • Users’ view
  • 40. Context: Usability Web of Data Web of Services IaaSLinked Open Data PaaSDomain Ontolgies SaaS CKAN SIndice WSMO voiD USDL Context Multi-tenant, ubiquitous rich experience devices
  • 41. Context-aware ComputingGartners top10 technologies for 2011
  • 42. Context: Definition Context:  Context is any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and application themselves. [A. Day and G. Abowd, 1999]  Typically , Location information, Proximity to devices, Places, Time, Personal information, Environment factors as weather, temperature, traffic, Status information of devices, Behavior of the user (e.g. talking, sleeping, walking, …), User preferences, Personal fitness / health, Tasks, Business process, … Context is a essential, foundational information in human-computer interaction.
  • 43. Context: Examples
  • 44. Context Modeling Context Model  Define and store context data in a machine processable form Properties of context information  may come from disparate sources and has a relatively transient lifetime.  exhibits a range of temporal characteristics.  Static vs. dynamic  may be imperfect.  Out of date  Faulty information from sensors  Unknown (due to disconnection)  has many alternative representations  is highly interrelated and dependent  sometimes should be persistence  Long lived context (history,...) vs. Short lived context (temperature,..)
  • 45. Context Modeling Language Context Modeling Language (CML): a tool to assist designers with the task of exploring and specifying the context requirements of a context- aware application.  CML is based on Object-Role Modeling (ORM), which was developed for conceptual modeling of databases.  CML provides a graphical notation designed to support the software engineer in analysing and formally specifying The model captures:  the different classes and sources of context facts  dependencies between context fact types  imperfect information using quality metadata and the concept of alternatives for capturing conflicting assertions  associations between users and communication channels and devices;  histories for certain fact types and constraints on those histories.
  • 46. Context Modeling Language
  • 47. Standard Ontology For Pervasive Computing SOUPA: Standard Ontology for Ubiquitous and Pervasive Applications  FOAF : People Profile, and Relationship  DAML-Time: Time, and Scheduling  RCC, OpenCyc: Description, Analysis Place and context  MoGATU-BDI, COBRA-ONT: Display and Analysis of Knowledge  Policy ontology (Rei): High Level Rules, Access Control
  • 48. Context-aware Applications  A cell phone will always vibrate and never beep in a concert, if the system can know the location of the cell phone and the concert schedule.  A coffee machine that senses the user can make coffee according to preferences.  A t-shirt automatically adjusts the ambient temperature of the room by sensing body temperature.  An airline check-in count automatically issues the board pass according to the passenger context.  When you visit Berlin at first time, your smart phone connects Facebook users who have already been there to ask the best way to West Bahnhoff.Context aware advertisements Tourist Guides & Navigation Systems Argument Reality Office Awareness Systems Smart workspace A Context-Aware Recommender System Conference Assistant Telematics servicesLocation Aware Information Delivery Emergency services Workflow management Package tracking services
  • 49. Issues: Context-aware Specific Context Definition to General Context Definition Non-Flexible Context Models to Flexible and Extensible Context Model Domain-specific Applications to General Frameworks Provide Rich User Experience through diverse mobile devices Service-oriented system based on Context Ontology
  • 50. Research DirectionsReal interaction: These technologies move the site and style of interactionbeyond the desktop and into the larger real world where we live and act.Real-world services: The desktop is a well-understood, well-controlledenvironment. Context Context-aware computing is for the real world services.
  • 51. Summary: Context-awareCatalyst and enabler to make semantic technology real…Gun for killer apps of semantic technology…Real human-computer interactionUnlimited opportunities ahead…
  • 52. Wrap up and Conclusions
  • 53. Semantic Technology Scalability Personalization Context-aware Semantic Usability Interoperability Aggregation Intelligence Semantic Technology Ontologies usually are application domain-dependent. Healthcare Education Telecom Life Science Automotive Banking Business Culture Library Aero-Space Energy Manufacturing Publishing Food Laws Human Relations2010-11-27 Sung-Kook Han 53
  • 54. Open Semantic Data Services Industries R&D Users Education Government Healthcare Culture Rich ExperienceDelivery Ubiquitous LayerService Innovation Layer Creativity Service Cloud Open Semantic Data Service Framework Knowledge Knowledge Semantic Web-Scale Knowledge Construction Registry Search Reasoning Management Interoperability Core Reuse Layer Semantic Service Service Service Service Service Service Access Delivery Partner Repository Mashup Discovery Control Man’mt Man’mtResource Public DB Public Resources Openness Layer Sharing Global Open Knowledge base
  • 55. Open Semantic Data Services
  • 56. Research Strategy Leave the Top-Down path. No Grand semantic theory, No Grand upper ontology Do not be overconfident about Semantic Technology. Do not oversell the Semantic Technology. Demonstrate Performance. Early release is the key. Show the power of Semantic Technology even though it is small Do not oversell the Semantic Technology. A little semantics goes a long way. Beware the usefulness of shallow ontology shown in LOD. Focus on the domain ontology. Be convinced of the benefit of Semantic Technology. Remember the community. Open and Share your ontologies, tools and platforms. Make it standard. Wikipedia is all about semantics.2010-11-27 Sung-Kook Han 56
  • 57. R&D Agenda Foundation Core Technologies Applications Context:  Semantic Repository  Semantic services • Context modeling • Automatic LOD population • Semantic search/discovery • Knowledge-in-context • Linking relational DB to LOD • Semantic social network/semantic • Context ontology (D2RQ, D2R,) graph/semantics for Internet of things • Emotion ontology • Scalable LOD store and • Context-aware service (location-based repository service, emotion-based service, Ontology • Semantic index (Sindice, SIRE) personalized service) • Ontology mapping/matching  Large-scale reasoning  Rich user experience • Large-scale reasoner (Larkc, • Personalized knowledge Knowledge SILK) manager/Semantic browser (Siri, • Knowledge extraction • Spatial/temporal reasoning Nepomuk) • Knowledge mining • Parallel implementation of • Semantic augmented reality reasoner (semantics+mobile+service)  Query processing  Embedded semantics • SPARQL engine • Green It using semantic sensor • SPARQL/SQL integrator network • Context-aware robot  Semantic Services • Semantic service  Domain applications • Semantic service platform (Talis) • Semantic business process • Semantic service mashup management • Semantic e-commerce • Semantic e-government • Semantic e-learning
  • 58. Conclusions Semantic Technologies need to go where the data is ! Long Live Semantic Technology ! Early adaptation of Semantic Technology is the king ! Link, Integrate, Embed Semantic Technology! Ontology is the common shared conceptualization. Ontology is the common vocabulary to communicate. We are live in the networked planet. Connection, Cooperation and Collaboration !2010-11-27 Sung-Kook Han 58
  • 59. Semantic TechnologyYour World, Your Way skhan@wku.ac.kr