The Rationale for Semantic Technologies

The Rationale for
Semantic Technologies

Michael K. Bergman

July 2012

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

2

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

3

Nature of the World
 Messy
 Complicated
 Interconnected
 Changing
 Interdependent
 Uncertain
 Diverse

4

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

5

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”

6

Current Approaches Have Failed
 Relational databases:
 Structured data only
 Inflexible, fragile
 Constant re-architecture
 Business intelligence:
 Slow, inflexible
 IT-constrained, not user-driven
 Extract, Transfer, Load (ETL):
 Inflexible, fragile
 High $$$, incomplete, not adaptable

7

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

8

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

9

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

10

Integrated Data and Tools using RDF

11

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

12

Graphs Grow Naturally with Knowledge

13

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”

14

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

15

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

16

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

17

A Distributed Web-oriented Architecture

18

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

19

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

20

Seven Pillars of the Semantic Enterprise

21

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

22

The Rationale for Semantic Technologies

The Rationale for Semantic Technologies

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