industry adoption of semantic web technologykeg.cs.tsinghua.edu.cn/aswc2006/resources/sw industry...

IBM China Research Laboratory

© Copyright IBM Corporation 2006

Industry Adoption of Semantic Web Technology

Dr. Yue [email protected]

IBM Research


Outline

� Business Drivers

� Industries as early adopters

� A Software Roadmap

� Conclusion

IBM Research


Data Semantics returns

“…The three most important problems in Databases used to be Performance, Performance and Performance; in the future, the three most important problems will be Semantics, Semantics and Semantics…”

(paraphrase) Stefano Ceri, June 11, 1998

� What it is

� The study of how to establish and maintain the correspondence between a data source, hereafter a model, and its intended subject matter.

� Let’s take a historical view

� 1970~80 Entity Relationship Model

� 1980~ Description Logics and other KR

� 2000 ~ Semantic Web

� Now

� Enterprise Semantic Web is chosen as one of the Gartner’s highlighted Emerging Technologies with high business impact in 2006

IBM Research


Data Semantics: Why Now

� Progress in storage and system

� Human resource cost

� Large number of databases/applications silos deployed in enterprise

� Business becoming more dynamic,

IBM Research


Critical Attributes of Information On DemandMoving From a Project-Based to a Flexible Architecture

Open Standards

Flexible & Resilient Infrastructure

Heterogeneous Applications & InformationHeterogeneous Applications & Information

� Deliver Information – In Context– In Line– Effectively Governed

� Integrate Information– Structured & Unstructured– Timely– Accurate– Manage Complexity

IBM Research


Semantic Web

� Today’s Web is mainly for human consumption

� Semantic Web make it towards data and programs

� A Web of (Hyper)Text (HTML)-> A Web of Data (XML)

-> A Web of Meaningful Data (RDF,OWL…)

� Smarter Data (vs. Smarter Machines)

� Make content easier for machines to find, access and process � Express data and meaning in standard machine-readable format � Support decentralized definition and management

� ... this is what the Semantic Web is about

IBM Research


Industries as Early Adopters

� Diagnosis of needs for Semantic Technology and Semantic Web

� Complexity of Data (structures, sources, volume)

� Volatility of data

� Willingness to Experiement on the Bleeding Edge

� Most likely

� Healthcare and Life Sciences

� Financial Market

� Interactive media companies

� Aerospace and defense

� Maybe

� Insurance

� Chemical and Petroleum

� Travel and Transportation

� Electronics

� Reference

� A. Jackson, L. Weitzman, S. Martin. A Beginner’s Guide to the Semantic Web. IBM Report, 2004.

IBM Research


Healthcare and Life Science

� UMLS – Unified Medical Language System

� UMLS Semantic Network

� HL7 - Healthcare Level Seven

� HL7 CTS – Common Terminology Service

� Mayo Clinic’s LexGrid

� Reference

� Vipul Kashyap,The UMLS Semantic Network and the Semantic Web.

� G. Eysenbach. The Semantic Web and healthcare consumers: a new challenge and opportunity on the horizon? Int. J. Healthcare Technology and Management 5, 2003

� LexGrid http://informatics.mayo.edu/LexGrid/

IBM Research


Healthcare and Life Science (Continue)

� ‘OMIC’ (Genomics, transcriptomics, proteomics etc.) standards

� Gene Ontology

� Life Science Identifier

� MIT’s Haystack

� BioDASH

� Simile

� Reference

� X. Wang, R. Gorlitsky & J.S. Almeida. From XML to RDF: how semantic web technologies will change the design of 'omic' standards. Nature Biotechnology 23, 2005.

� Semantic Web in Health-care and Life Sciences - Applications and Demonstrations, http://www.w3.org/2005/04/swls/

IBM Research


Financial Market

� XBRL - Extensible Business Reporting Language

� XBRL Taxonomy

� XBRL Linkbase

� Portia’s XBRL Tool& Service

� Reference

� Gartner Highlights Key Emerging Technologies in 2005 Hype Cycle

� http://www.portia.dk/websites/XBRLToolsServices.htm

IBM Research


Interactive Media

� Dublin Core

� RSS – Really Simple Syndication

� Reference

� Dublin Core Metadata Initiative. http://dublincore.org

� RSS. http://en.wikipedia.org/wiki/RSS_(file_format)

IBM Research


A Software Roadmap

� Place the foundation

� Representation Language and Standards

� Interpretation Agent (reasoner, knowledge base)

� Enable ontology management

� Metadata Management

� Master Data Management

� Web Service Registry

� Support Semantic Web

IBM Research


Semantic Web Activity

� The Semantic Web is not going to replace current Web (HTTP, XML, URL), but to build on top of it and get the full potential of the Web

� Semantic Web Activity

� 1997, the W3C defined the first Resource Description Framework (RDF)

� 1999, RDF became a W3C recommendation

� 2004, RDF Schema (RDFS) and Web Ontology Language (OWL) became a W3C recommendation

� 2005, Work has begun on the Rule Interchange Format

Tim Berners-Lee, AAAI 2006 Conference keynote

IBM Research


Challenges in Current Semantic Web

� OWL Full (DL, Lite) is neither necessary or adequate in many domains. How to subset and extend them in specific applications?

� If subset and/or extend OWL, how to still ensure the interoperability between the dialects?

� Most of existing data source is in other kind of data models which use different assumptions, like close world assumption in database. Semantic Web must be bootstraped from existing data sources, then how to overcome the incompatible assumptions?

� Scalable and high performance knowledge base – support millions and billions of triples

� Distributed reasoning algorithm

IBM Research


Integrated Ontology Development Toolkit

IODT is both a core technology for managing RDF, RDFS and OWL ontology data and an API and toolkit for developing and using the technology.

EODMSource Code at http://www.eclipse.org/emft/projects/eodm/

IODTBinary code at http://www.alphaworks.ibm.com/tech/semanticstkSource Code at https://cs.opensource.ibm.com/projects/iodt

Ranks 19 in download counts of all 556 alphaWorks technologies in June 2006

IBM Research


Integrated Ontology Development Toolkit

OntologyOntology

ECore Model(EMF Perspective)

Object-Oriented ODM Model(Model Driven Perspective)

Triples and Logic Model

(Semantic Perspective)

EODM ModelMinerva

IBM Research


A Software Roadmap









IBM Research


Metadata: Help understand/exploit information assets

Information Assets

End Users

We’re losing revenue because similar products are hard to find in our online catalog.

BusinessAnalyst

We need a system to automatically reconcile product inventory from our subsidiaries.

Software Architect

We can build a system to integrate product information across subsidiaries.

Developer

This EJB will represent product information in a vendor-independent way.

Data Architect

This data model will integrate our product inventory with vendor information.

Data AdminWe can deploy this data model using federation to integrate existing inventory databases.

IT Admin

We’ll need to a put in high speed WAN between our subsidiary data centers.

Application metadata

Operationalmetadata

Business metadata

Source: IBM Metadata TT Study, 2005

IBM Research


Metadata Challenges

� Current limitations

� Lack of interoperability between metadata

� Each data source has its own metadata and/or metadata repository

� Limit of business understanding and collaboration between business and IT

� Models are IT-centric, not business-centric

� Insufficient semantics for dynamic SOA environments

� Informational, but not computational, limiting automation

� What is metadata: Metadata defines the content, context, and structure of information

� IT level

� Table names, column names and data types

� key constraints between tables

� Text annotations/descriptions of what the tables and columns represent

� Business level

� Business concepts

� Business objects

� Business rules

IBM Research


Semantic Metadata Management

� Using ontology to model business concept and ontology instances for business objects

� Capture the industry-wide or enterprise-wide business concept into ontologies

� Populate and manage metadata about business concept, physical data and the inter-relationship between them

� Construct “virtual” business object dynamically from the metadata

� Enables greater IT flexibility to more responsively support business operations

� Give a view of data from business level instead of IT

� Enable dynamic definition of new business concepts

� Apply rule and policy to business objects, instead of IT-level objects

IBM Research


Master Data

� Definition

� “Master data is data that is shared across systems (such as lists or hierarchies of customers, suppliers, accounts, or organizational units) and is used to classify and define transactional data.” [IDC]

� Examples

� Sell Product A to Customer X on 1/1/06 for $100.

� With Master Data, we should be able to answer to such questions

� What is a “customer” ?

� Define the concept

� How to add a new customer ?

� Defines the workflow

� How to know that 2 customers refers to the same identity ?

� Defines business rules

� Value of Master Data Management

� Use a MDM Hub as the “master” to keep the multiple system already deployed as the “slave”consistent

� Apply workflows and rules consistently across applications

Hierarchies;Rules;

Workflow

MDM Hub

Master datadata Metadata

Data warehouse

Query and Reporting

Master dataTrans data Metadata

CRM

Analysis


Planning


Business Performance Management


Procurement

Transactions


Finance


Marketing

IBM Research


Master Data Challenges

� Three Essential Technologies for MDM

� “An enterprise-wide data model" � which provides a logical model for

aggregating and reconciling the various data sources that comprise a master record.

� “Federated capabilities" � to connect independent data stores

with a thin structure while leaving most of the data in their source locations.

� "Identity management" � to not only securely identify customers

but also centrally manage privacy policies.

� Source: CDI INSTITUTE MARKETPULSE SURVEY 2004

� Next Generation Master Data Management

� Flexible model� Easy to change� Expressive – Semantically Complete� Effective to manage metadata and data� Scalable and of high performance

Data Model

Storage Model

IBM Research


Semantic Master Data Management

� Use ontology and ontology instances to model master data

� Develop scalable ontology repository and search engine

� Add-on Value of ontology model for master data

� Support dynamic categories of customers, products

� Support rich relationship types and reasoning over relationship

� Query over relationship by leveraging semantic query (e.g. SPARQL)

� Categorize objects on the fly for master data exchanges

IBM Research


Web Service Discovery

� Need a standard interoperable platform that enables companies and applications to quickly, easily, and dynamically find and use Web services over the Internet

IBM Research


Semantic Web Services

� Semantics can improve software reuse and discovery, significantly facilitate composition of Web services and enable integrating legacy applications

� semantic models provide agreement on the meaning and intended use of terms

� Reasoning techniques can be used to find the semantic similarity between the service description and the request.

� Example: WSDL-S

� Associate semantic annotations with Web services described using WSDL

� Other Representations

� WSMO, OWL-S, SWSA/SWSL.

R. Akkiraju, J. Farrell, J.Miller, M. Nagarajan, M. Schmidt, A. Sheth, K. Verma,"Web Service Semantics - WSDL-S, " A joint UGA-IBM Technical Note, version 1.0,April 18, 2005

WSDL-S Informaiton Model

IBM Research


A Software Roadmap









IBM Research


Information Delivery and Integration on Semantic Web

RDF+OWL+SPARQL+HTTP

Mapping

SQL Schema

Adapter

Existing Database

Ontologies of Objects

RDF form of Data

SPARQL Service

Mapping

SQL Schema

Adapter

Ontologies of Objects

RDF form of Data

SPARQL Service

XQueryService

Existing XML

Index of Objects

Object Crawler

Search Interface

Support many kinds of data integration, so that we can 1) postpone the labor-intensive aspects of data integration until they are absolutely needed and choose the right approach of integration; 2) reuse previous data integration efforts

Aggregation

Federation

Search

ETL

Iterative

IBM Research


Conclude: Semantic Web, the Next Generation Web

Digitalized Structured Linked Integrated Intelligent

UnstructuredInformation

StructuredInformation

Electronic File

Database

Web 1.0

Web 2.0 (SOA …)

Web 3.0Semantic Web

Information On Demand

IBM Research

Semantic Technologies | Integrated Ontology Development Toolkit | Confidential © 2005 IBM Corporation

Thank You!

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