This document outlines 7 arguments for semantic technologies. It discusses how semantic technologies can improve findability by unifying different types of data and search methods. It also allows for relationships between data to provide context. Semantic technologies enable more capabilities for information architecture by allowing data to be more interoperable, annotated, and computable. They also allow knowledge to be represented and organized as a knowledge graph. Semantic technologies shift more control to business users by separating content from applications and allowing users to model information. They also enable more adaptive and robust data structures and reduce costs compared to traditional systems.

1. Seven Arguments for
Semantic Technologies
Michael K. Bergman
February 24, 2013

2. Summary Points
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3. Knowledge-based Findability
 A “unifier” of search:
 structured + semi-structured + unstructured data
 find + discover + navigate
 SQL + SPARQL + full-text  “dial-in” search results
 Global faceting:
 types of things
 relationships of thing
 Relationships bring context
 Explodes keyword boundaries:
 concept enrichment
 meaning and many ways to describe things
 multi-linguality
 inference
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4. Ability to Do More – Classic IA
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5. Going Beyond Content and Context
 Convertibility  single data representation model 
the “universal solvent”
 Data interoperability
 Semantic annotation and tagging:
 concepts – OBIE
 entities – NER
 semantic injection
 Understood equally by machines + humans
 Computability
 Analysis
 Superior performance
 Semantic publishing
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6. The Knowledge Graph
 Built from simple (“triple”) assertions:
 easily understood
 easily corrected
 concepts and many ways to speak of them
 can capture rich relationships
 rich ways to organize
 Based on logic:
 may be reasoned over
 testable for coherence
 Supports unique capabilities:
 graph traversal
 graph analysis
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7. Control Shifts to the Business
 Major driver is the ontology, structure of information
 Content is separated:
 from presentation
 from applications
 SMEs and knowledge workers now:
 model the business, workflows
 extend structure
 expand concepts, meaning
 author content, manage datasets
 IT now shifts to:
 developing generic applications (“ODapps”)
 stack deployment and maintenance
 security, content versioning and backups
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8. Adaptive, Robust Structures
 The right data representation model:
 RDF v RDBMs
 open world v closed world assumption
 representation = knowledge + access
 Integrating heterogeneous information:
 syntactic
 structure (“granularity mismatch”)
 semantic
 More effective HCI (human-computer interface):
 separation of content and presentation
 templates
 ontology-driven (generic) applications
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9. Much Reduced Costs
 Leverage existing information assets
 Single representation of schema
 Use of generic, ontology-driven tools
 Extendable without re-architecting
 Reduced errors
 Higher quality information in context
 Lower deployment and set-up costs
+
 Lower maintenance and extension costs
 average 1 – 2 orders of magnitude less
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10. Domain Rationale (biomedical example)
 Veritable explosion in data (“Big Data”)
 Veritable explosion in new concepts, new knowledge
 Rich, layered, complex relationships:
 many disciplines and sub-disciplines
 many informational perspectives
 many players
 much varied terminology
 fit with electronic health records (EHR)
 Strong, existing support for semantic technologies:
 more than 250 extant ontologies, ontology groups
 the OBO Foundry
 the National Center for Biomedical Ontology
 major emphasis of US National Institutes of Health and its
National Library of Medicine
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