The document outlines the rationale for using semantic technologies, emphasizing their role in knowledge representation rather than traditional data transactions. It highlights the open world paradigm, the integration of various information types, and the advantages of graph-based structures for understanding complex knowledge domains. Additionally, it discusses the limitations of current IT systems and the benefits of leveraging existing assets to democratize knowledge management.

1. The Rationale for
Semantic Technologies
Michael K. Bergman
July 2012

2. Outline
§ Nature of the World
§ Knowledge Representation, Not Transactions
§ The New Open World Paradigm
§ Integrating All Forms of Information
§ Connections Create Graphs
§ Network Analysis is the New Algebra
§ Information and Interaction is Distributed
§ The Web is the Perfect Medium
§ Leveraging – Not Replacing – Existing IT Assets
§ Democratizing the Knowledge Function
§ Seven Pillars of the Semantic Enterprise
§ Summary of Semantic Technology Benefits
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3. Some Caveats
 Semantic technologies are NOT:
 Cloud computing
 Big data
 Necessarily open data
 “One ring to rule them all”
 A replacement for current IT systems
 These ideas are mostly orthogonal to semantics
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4. Nature of the World
 Messy
 Complicated
 Interconnected
 Changing
 Interdependent
 Uncertain
 Diverse
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5. Nature of Knowledge
 Knowledge is never complete
 Knowledge is found in structured, semi-structured
and unstructured forms
 Knowledge can be found anywhere
 Knowledge structure evolves with the incorporation of
more information
 Knowledge is contextual
 Knowledge should be coherent
 Knowledge is about its users defining its structure
and use
Knowledge ≡ Nature of the World
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6. Knowledge Representation, Not Transactions
 KR functions:
 Search
 Business intelligence
 Competitive intelligence
 Planning
 Data federation
 Data warehousing
 Knowledge management
 Enterprise information integration
 Master data management
 Traditional IT has been transaction-oriented
 e.g., “Seats on a plane”
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7. Current Approaches Have Failed
 Relational databases:
 Structured data only
 Inflexible, fragile
 Constant re-architecture
 Business intelligence:
 Slow, inflexible
 Structured data only
 IT-constrained, not user-driven
 Extract, Transfer, Load (ETL):
 Structured data only
 Inflexible, fragile
 High $$$, incomplete, not adaptable
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8. A 30-yr Quest to Integrate Content
 Content and data federation has been insolvable for
30 years since IT systems first adopted:
 Structured + semi-structured + unstructured content
 Data “silos” and unconnected systems
 Incompatible protocols and hardware
 85% of content not in databases
 Semantic heterogeneities
 No universal data model
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9. The New Open World Paradigm
 Opposite logic of closed-world transactions
 The open world assumption (OWA) means:
 Lack of a given assertion does not imply whether it is true or
false: it simply is not known
 A lack of knowledge does not imply falsity
 Everything is permitted until it is prohibited
 Schema can be incremental without re-architecting prior
schema (“extensible”)
 Information at various levels of incompleteness can be
combined
 The right logic for KR problems
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10. Integrating All Forms of Information
 Uses a “canonical” data model (RDF)
 RDF is a universal solvent for all information:
 Unstructured data – text, images
 Semi-structured data – markup, metadata
 Structured data – databases, tables
 “Soft” (social, opinion) + “hard” (facts) information
 RDF can represent simple assertions (“Jane runs fast”)
to complex vocabularies and languages
 Generic tools can be driven by the RDF data model
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11. Integrated Data and Tools using RDF
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12. Connections Create Graphs
 Things and concepts create nodes
 Relationships between things create connections
(“edges”)
 Adding things leads to more connections
 More connections leads to more structure
 Coherent structure leads to more knowledge and
understanding
 The natural structure of
knowledge domains is a
graph
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13. Graphs Grow Naturally with Knowledge
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14. Benefits of Graphs (ontologies)
 Coherent navigation
 Flexible entry points
 Inferencing
 Reasoning
 Connections to related information
 Ability to represent any form of information
 Concept matching  integrate external content
 A framework for disambiguation
 A common vocabulary to drive content “tagging”
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15. Network Analysis is the New Algebra
 Network analysis provides new tools for gauging:
 Influence
 Relatedness
 Proximity
 Centrality
 Inference
 Shortest paths
 Diffusion
 Graphs can represent any structure
 Many structures can only be represented by graphs
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16. Information and Interaction is Distributed
 Knowledge is everywhere
 People and stakeholders are everywhere
 External information needs to be integrated with
internal information
 A uniform access protocol/framework is desirable to:
 Preserve existing information assets
 Reflect the diversity of data formats
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17. The Web is the Perfect Medium
 All information may be accessed via the Web
 All information may be given Web identifiers (URIs)
 All Web tools are available for use and integration
 All Web information may be integrated
 Web-oriented architectures (WOA) have proven:
 Scalability
 Robustness
 Substitutability
 Most Web technologies are open source
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18. A Distributed Web-oriented Architecture
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19. Leveraging – Not Replacing – Existing IT Assets
 Existing IT assets represent:
 Massive sunk costs
 Legacy knowledge and expertise
 Stakeholder consensus
 Yet, still stovepiped
 Semantic technologies are an interoperability layer
over existing IT assets
 Preserve prior investments while enabling
interoperability
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20. Democratizing the Knowledge Function
 Move from bespoke software to knowledge graphs
 Knowledge graphs can be constructed and modified
by:
 Subject matter experts
 Employees
 Partners
 Stakeholders
 General public
 Graph-driven applications can be made generic by
function, visualization
 Graph-driven applications democratize KR
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21. Seven Pillars of the Semantic Enterprise
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22. Summary of Semantic Technology Benefits
 Can deploy incrementally
 lower risks
 lower costs
 Excellent integration approach
 No need to re-do schema because of changed
circumstances
 Leverages existing information assets
 Well-suited for knowledge applications
 Can accommodate multiple viewpoints, stakeholders
 Leadership visibility to the Forum
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