Promoting the Semantic Web Crossing the Technology Chasm Date: April 28, 2006 Version 0. 1 Dan Mc. Creary President Dan Mc. Creary & Associates (952) 931 -9198 1
This Material is Protected Under Creative Commons 2. 5 BY: $ • Attribution. You must attribute the work in the manner specified by the author or licensor. • Noncommercial. You may not use this work for commercial purposes. • Share Alike. If you alter, transform, or build upon this work, you may distribute the resulting work only under a license identical to this one. 2
Overview • Where are Semantic Web standards today? – Review the standards stack – Semantic Web SWOT • Where do we want to be? – A mainstream standard (used by more that just innovator and early adopters) – Have high impact on the economics of data sharing • What is the Technology Standards Chasm? • The Linking Challenge • Strategies for Crossing the Chasm 3
Why This Presentation • After discussions with – Jim Heldler – “Linking is Power” – Ora Lassila - Nokia – Tony Shaw – Wilshire Conference – Eric Miller – W 3 C – Semantic Web Education and Outreach • What can we do to promote semantic web standards? 4
The Agent Vision The Semantic Web will bring structure to the meaningful content of Web pages, creating an environment where software agents roaming from page to page can readily carry out sophisticated tasks for users. Agent Agent The Semantic Web A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities By Tim Berners-Lee, James Hendler and Ora Lassila Scientific American 5
Semantic Web Standards Stack Trusted Semantic Web Source: Tim Berners-Lee www. w 3 c. org Proof Ontology (OWL) Encryption Rules/Query Signature Logic RDF Model & Syntax XML Query XML URI/IRI XML Schema Namespaces Unicode http: //www. w 3. org/Consortium/Offices/Presentations/Semantic. Web/34. html 6
Semantic Web Today • Search of Google for “ontology filetype: owl” • Returns about 14, 000 files from: – – . edu – lots of academic research projects. org – some standards bodies. gov – some government standards. com – very few commercial companies publish their metadata in. owl format • Extremely few inter-ontology links 7
Today: Sample SWOT Analysis for Semantic Weaknesses Web Strengths • W 3 C has an excellent reputation for creating useful standards (HTML, XML Schema etc) • Few alternative technologies with same breath and ambition • Widespread acceptance in academic institutions worldwide Future: Opportunities • IT departments spend billions each year on integration • Automated metadata discovery could become cost-effective • Automated integration requires ontologies • Business Intelligence/Analytics/Data Warehouse require precise semantics • Business Rule engines need precise semantics • SOA need precise semantics • Proof, logic and trust layers still in research and development stage • Few cost-effective tools for many areas • RDF perceived as too complex or conflicting with XML (RSS example) • Perception that web sites need to be published in both human and machine readable versions doubling costs • Few published case studies with documented ROI Threats • Many incompatible mini standards • Complexity • Vendor specific solutions • Complex XML structures (XLink, XPath) • Confusion with other standards (XMI, CWM, ISO-11179) • One big wikipedia takes over the entire world wide web and adds semantic features • Incompatible and constantly changing Folksonomies 8
Today the Semantic Web • Is being used by innovators and early adopters • Is not yet a “mainstream” technology • Has yet to pick up the momentum in the corporate world to be a viable long-term standard 9
Technology Standard Waves ? ? XHTML XML URI/HTML Technology standards come in “waves” and are built on other standards 10
Technology Adoption Cycles The Chasm Geoffrey Moore Innovators Early Majority Early Adopter s Late Majority Laggard s Technologies that fail to cross the chasm fail to reach critical mass. Source: “Crossing the Chasm” 11
Three Step Strategy 1. Identify where you customers are on the technology adoption cycle 2. Tailor your marketing strategy to needs the needs of that section of the marketplace 3. Build marketing materials that specifically target the needs of your customer 12
Innovators • First group to use a new technology • Pure technologists – sometime without clear business requirement • Aggressively pursue new ways of solving business problems • Want to know how things work – they will figure out how to apply a technology to their business problems • Tend to be very high maintenance, they need a lot of handholding • Are looked to from other buyers for recommendations • Less than 2% of buyers 13
Early Adopters • Usually the second group to use a new technology • Wait till the innovators have recommended a product • Don’t need full ROI analysis but… • Don’t want to be the first to use something but will be aggressive once • Use technology differentiation for competitive advantage in the marketplace (attract the “uber-geeks” to work in their IT departments) • Approximately 15% of buyers 14
Early Majority • Third group to use a new technology • Wait till the innovators and early adopters have recommended a product within their industry • Buy based on case studies of other users in similar industries • Like to see ROI analysis but don’t require it • Most profitable segment of the marketplace • Approximately 1/3 of buyers 15
Late Majority • Fourth group to use a new technology • Wait for industry standards to be available and being used by more than half of the peers in their industry • Wait till rock-solid ROI is available and clearly documented • They check references carefully and are very price conscious 16
Laggards • Last group to use a new technology • Strong dislike for new technology and change • Will only purchase a new technology when buried deep within a total solution • Sometimes least profitable to market to since the technology has been integrated and commoditized 17
The Chasm • The place where most standards fail (over 85%) • Primary Reasons: – A technology is too hard to use – To hard to explain the business benefits of a technology – Really does not address a significant enough business problem to justify the change 18
Expected Payout Change and Payout Approve Change Approver Position Withhold Approval Degree of change • People will make not make changes if they do not perceive there is a benefit to them individually (payout) • Individual will approve small changes if they see a small benefit • They will make large changes only if they see a large payouts for themselves • You must either convince approvers that the change is small or the payout is large Source: Managerial Economics and Organizational Architecture 3 rd Edition p. 556 19
The Chasm • The place where most standards fail (over 85%) • Primary Reasons: – The new technology is too complex to use – It is too hard to explain the business benefits of a technology to non-technical decision makers – It does not address a significant enough business problem to justify the change 20
Crossing The Chasm Early Majority Early Adopters • Standard cross the chasm by vertical industry • Early majority buyers want references from within their industry • But usually early adopters don’t want to share their success stories • Getting the first “reference accounts” in a specific vertical industry is the critical factor • Case studies must be carefully analyzed to ensure that the customers have the same motivation 21
Getting References • Use of “Case Study” Marketing • Sometime corporate identify can be obscured (a large Midwest bank), but this tends to mitigate the impact of a case study • Some purchasers what to know what specific peer companies are using a new technology • Many companies refuse to be considered for a case study since they perceive their technology strategy is part of their competitive advantage. 22
Key Elements of a Case Study 1. Organization Description – the reader looks for: “Is this organization similar to mine? ” 2. Business Challenge – the reader verifies: “Is this problem similar to my problem? ” 3. Solution – “Can we be expected to get similar results” 4. Results – “What types of quantifiable results did the users get? Could we get the same results? ” 23
Selling Incremental Change • Instead of a “big bang” or “forklift upgrade”, can you sell a smaller set of lowrisk changes? • Example: Microformats • How will web publishing tools need to change? • How will this benefit the Publisher 24
Ontologies are Islands of Understanding • An individual OWL file or internal metadata registry without links to other ontologies is a self-contained “island” of understanding • Concepts and properties are internally linked and consistent with each other but agents can not understand relationships of concepts to other ontologies • Fine for internal data warehouses and internal OLTP systems • Does not take advantage of the growing knowledge base of the machine understandable web 25
Inter-ontology Links are Bridges • RDF statements in separate ontologies can be expressed as URIs that are the identical • OWL supports same. As, equivalent. Class and equivalent. Property statements to create bridges between ontologies • Links allows agents to traverse ontologies and perform searches on disparate systems even if our local ontology does not have the data • “Linking is Power” applies to Google page ranks and agent interoperability 26
Bridge/Link Funding Agent Web page • What if there are two ontologies that have overlapping conceptual domains? • What if both source systems want to access each others data? • Who pays for the links? • Where are the links stored? • What about change control? 27
Who Pays for the Bridges? database Agent Web page • • What is the economic motivation for building a bridge? Who benefits from building a bridge? – – – • • • The agent seeking data? The data owners? The community as a whole? Where are inter-ontology links stored? Will there be the standards? Where are the bridges stored? 28
Hub and Spokes • Goal: create semantic linking to a few metadata standard, not many standards Mapping from one to many metadata registry to N other metadata registries: The O(N 2) problem Mapping to one metadata registry The O(N) problem (aka ESB-Enterprise Service Bus) 29
Large and Upper Ontologies • What is the role of large or upper ontologies in the process? • Can they be used as linking hubs? • What is the role of small ontologies such as Dublin Core? • How would users publish their semantic links to these central ontologies? • Can translation services be created from these standards? 30
The Tornado • When you are “inside the early majority” • Demand rises rapidly and outstrips supply of consultants and training • Lack of skilled workers and training • Who will provide these people/processes to convince decision makers that they can: • Can hire cost-effective contractors • Get their staff trained? 31
Branding/Site Certification • Should we promote some type of certification for resources (web sites)? • What would be the logo? What would it imply? Can an agent just look up the definitions of all the data elements on a page? Source: www. pmi. org Annual Report 32
Certification • Should we promote some type of certification for people? • What would the scope of these skills or web sites be? • How would we certify individuals? – Proctored exams? – Knowledge bases? • Example: The Project Management Institute has certified over 100, 000 individuals and has over $53 M in revenue in 2004 • What conflict of interest would arise? • Should we promote cost-effective on-line learning? Source: www. pmi. org Annual Report 33
Example: Moodle Open Source Learning Mgmt. System 34
Where are Big Dollars Being Spent? • Some analysts indicates that 50% of IT dollars go towards integration issues • Some analysts say that 75% of integration issues are due to poor semantics • What is the size of the market for “automated semantic integration”? 35
Metadata Discovery • Tools that “scan” data sources and create new ontologies or mappings to existing ontologies Relational Database Metadata Registry Data Source Mappings Corporate Ontology Examples: Silver Creek Systems 36
Vendors Partnerships • • Can we encourage hard-core ontology developers to publish their work in OWL format? Database vendors – What vendors are doing RDF support? – What vendors currently promote OWL publishing? – How can we recognize them? • Application development vendors – SOA – Can SOA vendors use the semantic web stack? – Can Web Service development tools export to OWL format? • XML Appliance/Integration/Security vendors – Can they automate integration using OWL standards • • • Metadata registry vendors Metadata discovery vendors Tool vendors Open Source partnerships Do vendors consider metadata publishing in OWL contrary to their metadata lockin strategy? 37
Promote Early Adopters • Commercial – Adobe, Cisco, HP, IBM, Nokia, Oracle, Sun, Vodaphone • Governments – US, EU, Japan • Industries – Health Care – Life sciences 38
Possible Strategies • Recognition – Linking is Power Award – given to organization that link ontologies together 39
References • Semantic Web Home Page: – http: //www. w 3. org/2001/sw/ • Semantic Web Education and Outreach Home Page – http: //www. w 3. org/2001/sw/EO/ • Semantic Technologies Conference – http: //www. semantic-conference. com/ • Linking is Power Award – http: //www. danmccreary. com/linking-is-power 40
Thank You! Please contact me for more information: • • Metadata Management Services Web Services Service Oriented Architectures XML Schema Design Business Intelligence and Data Warehouse Metadata Registries Semantic Web Dan Mc. Creary, President Dan Mc. Creary & Associates Metadata Strategy Development dan@danmccreary. com (952) 931 -9198 41
Скачать презентацию Promoting the Semantic Web Crossing the Technology Chasm
687b628725057efc95602579e9686935.ppt