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

1. Semantic Technology:
The State of the Art and Research Directions
Sung-Kook Han
2010.12.03

2. Outline
Review of Semantic Technology
Hot Issues:
 Linked Data
 Context-aware
Future 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 Department
Telecommunication
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 Interoperability
Machine-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 reasoning
Classic Web Web of Services
Internet of Services
Web of Documents
HTML as document format
HTTP URLs as globally unique IDs
Hyperlinks 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 Applications
Semantic 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 Today
No 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, 2003
The first step is putting data on the Web in a form that machines can
naturally understand, or converting it to that form. This creates what I call a
Semantic Web - a web of data that can be processed directly or indirectly by
machines. Therefore, while the Semantic Web, or Web of Data, is the goal or
the end result of this process, Linked Data provides the means to reach that
goal. -- 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…..92df7
URI: global primary key fb:creator
skos:subject = http://www.w3.org/2004/02/skos/core#subject fb:street_address
dbp-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 Step
Linking, 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 Cloud
Search 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
IaaS
Linked Open Data
PaaS
Domain Ontolgies
SaaS
CKAN SIndice WSMO
voiD USDL
Context
Multi-tenant, ubiquitous rich experience devices

41. Context-aware Computing
Gartner's 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 services
Location 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 Directions
Real interaction: These technologies move the site and style of interaction
beyond the desktop and into the larger real world where we live and act.
Real-world services: The desktop is a well-understood, well-controlled
environment. Context
Context-aware computing is for the real world services.

51. Summary: Context-aware
Catalyst and enabler to make semantic technology real…
Gun for killer apps of semantic technology…
Real human-computer interaction
Unlimited 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 Relations
2010-11-27 Sung-Kook Han 53

54. Open Semantic Data Services
Industries R&D Users Education
Government Healthcare
Culture Rich Experience
Delivery Ubiquitous
Layer
Service 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’mt
Resource 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 Technology
Your World, Your Way
skhan@wku.ac.kr