pragmatic approaches to the semantic web

Michael K. Bergman

Pragmatic Approaches to the Semantic Webor, Why Aren’t We in Hyperland Yet?

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Outline

Intro to SD and Me

Summary of Main Thesis

A Wee Bit of History

What is Not Working?

Problems with Linked Data

What is Working?

Some Pragmatic Lessons

SD’s Pragmatic Approach

Conclusion and Q & A

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Structured Dynamics

Founded 2008; predecessor Zitgist LLC; two principals

Privately held, revenue funded

Boutique semantic technology shop

Services and consulting: Semantic enterprise adoption Ontology development and mapping Tech transfer and training

Development and software: Open source OSF stack

Data conversion and migration

Client-specific development

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Current Products and OSF Stack

the pivotal product; Web services middleware that provides distributed data access and federation

Drupal-based structured data linkage to structWSF

spreadsheet, JSON and XML authoring and conversion framework

reference set of linking subjects and basis for domain vocabularies

an ontology- and entity-driven information extraction and tagging system

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SD Locations

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Michael Bergman

Summary of Main Thesis

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Main Arguments

Not against linked data Proponent and explicator since 2006

But, linked data burdensome, not pivotal to interoperability

Interoperability requires: Structured data (from any source) Canonical data model (RDF) (Relatively simple) ontologies for world views, schema Curation

A Wee Bit of History

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Key Historical Milestones

1945: Memex

1963: Hypertext

1990: Hyperland

2001: Semantic Web Lack of uptake

2006: Linked Data

2010: Revisionist Linked Data

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Hyperland

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Linked Data

“Linked Data is a set of best practices for publishing

and deploying instance and class data using the RDF

data model, naming the data objects using uniform

resource identifiers (URIs), thereby exposing the data

for access via the HTTP protocol, while emphasizing

data interconnections, interrelationships and context

useful to both humans and machine agents.”

What is Not Working?

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Some Disappointments to Date

Full semantic Web vision

Widescale adoption of the semantic Web, linked data

Lack of intelligent agents

Many aspects of the practice of linked data

Problems with Linked Data

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Problems with Linked Data

Burdensome on publishers

Naïve linkages: Overuse of sameAs Lack of accurate alignments

(Often) poor data quality

Wrong focus

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Some Conditions for Interoperability

<Interoperability> <needsMapping> <Predicates>

<Interoperability> <needsReference> <Nouns>

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Many Mappings Should be Approximate skos:broadMatch skos:related ore:similarTo umbel:isAbout vmf:isInVocabulary skos:closeMatch lvont:nearlySameAs umbel:isLike umbel:hasCharacteristic lvont:somewhatSameAs rdfs:seeAlso ore:describes map:narrowerThan skos:narrower map:broaderThan skos:broader dc:subject link:uri foaf:isPrimaryTopicOf

What is Working?

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Successes

Siri

Bing (Powerset)

Google + schema.org

(Some) linked data

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Siri

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Bing (Powerset)

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Google

Statistical NLP

Structured results

Initial schema (Metaweb)

schema.org (with Yahoo, Bing and Yandex)

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Some Linked Data

Some selected knowledge bases: DBpedia GeoNames Freebase (Google)

Biomedical community

LOD-LAM community

Some Pragmatic Lessons

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Some Lessons Learned

Structure is good in any form

Keep semantic technology in the background

Open Web (FYN) likely to be disappointing

Ontologies essential for alignments

NLP an essential contributor to structure

Metadata an essential contributor to characterization, use

Linked data is a burden to publishers, places semantic emphasis on wrong part of chain

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Seven Pillars

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Preserving Existing Assets

Relational databases (RDBMs)

Distributed structured assets spreadsheets lightweight datastores

Web pages and Web sites

Existing documents and text

Web databases and APIs

Other databases (RDF, OO, etc.)

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irON Dataset Exchange Framework

Simple authoring and dataset creation

irON includes an abstract notation and vocabulary for instance records

Notations for: Instance records

Schema

Datasets and metadata

Linkages to other schema

Serializations available for: XML (irXML)

JSON (irJSON)

CSV/spreadsheets (commON)

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Three irON SerializationsirXML irJSON

commON

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Spreadsheet Correspondence to Triples

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More-or-less Interchangeable Formats

SD’s Pragmatic Approach

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A Layered Approach

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OSF Stack

Conclusion

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Summary

If you can, do linked data; it is a GOOD THING

In any event, expose your data: Structured (use NLP for unstructured) Metadata Definitions Relations (simple) “Semsets” (synonyms, acronyms, spelling variants)

Build vocabulary and ontology consortia

Build trust and curation communities

Semantics essential at the interoperability level, not necessarily publication or data transfer

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Take Aways

James Hendler:

“A little bit of semantics goes a long way”

Leverage linked data, but broaden focus

Consider adopting the semantic enterprise as the broader focus

Further Information

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More Info and Links

Open Semantic Framework (OSF) stack: http://openstructs.org

TechWiki (400 detailed OSF how-to articles): http://techwiki.openstructs.org

Key ontologies: UMBEL: http://umbel.org

BIBO: http://bibliontology.org

Blogs: Mike Bergman: http://mkbergman.com

Fred Giasson: http://fgiasson.com/blog

Structured Dynamics: http://structureddynamics.com

http://citizen-dan.org (community indicator systems)