Where the Web Went Wrong

http://gate.ac.uk/ http://nlp.shef.ac.uk/

Hamish CunninghamDept. Computer Science, University of Sheffield

Graz, May 2004

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Contents• The Web, presentation, and syndication• A Semantic Web for eCulture

– annoy half the audience– annoy the other half

• eCulture, metadata and human language– motivation– Information Extraction: quantified language

computing– MUMIS, GATE, ...

• Cultural memory is not a luxury

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Syndication and Mediation• The web promotes diversity, but also fragmentation• Original web: separate content and presentation (“this

is a header”, not “set in 20 point bold font”)• Now: many incompatible/inaccessible interfaces• Memory Institutions (museums, libraries, archives)

need to:– pool their impact: syndication in networked communities– support repurposable content

• Therefore data must be presentation independent• Candidate technologies:

DC, CIDOC, XML, RSS, RDF, OWL (“semantic web”)...

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Semantic Web (1)• Memory Institutions (museums, libraries, archives)

host massively diverse content• Fortunately, the differences are primarily at the level

of data structure and syntax. Significant conceptual overlaps exist between the descriptive schema used by memory institutions; elemental concepts such as objects, people, places, events, and the interrelationships between them are almost universal. Building semantic bridges between museums, libraries and archives: The CIDOC Conceptual Reference Model, T. Gill, April 2004

• Therefore we can add a semantic metadata layer to provide generalised inter-institution resource location

• Syndication and mediation for free!

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Semantic Web (2):good news and bad news

• The good news: SW focus of AI and metadata work• The bad news: AI always fails• How does the machine tell the difference between

“Mother Theresa is a saint” and “Tony Blair is a saint”?(Or, who tells Google which statement is important?)

• Other web users do, by linking (also cf. Amazon)• Two solutions to the AI problem:

– allow curators and users to build their own (simple specific models can succeed, but the cost may be too high)

– use recommender systems to make the user a curator’s assistant (researchers and students may barter for access)

• Any route to searchable content!

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IT context: the Knowledge Economy and Human Language

Gartner, December 2002: • taxonomic and hierachical knowledge mapping and indexing

will be prevalent in almost all information-rich applications • through 2012 more than 95% of human-to-computer

information input will involve textual language A contradiction: • to deal with the information deluge we need formal knowledge

in semantics-based systems • our archived history is in informal and ambiguous natural

language The challenge: to reconcile these two phenomena

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HumanLanguage

Formal Knowledge(ontologies andinstance bases)

(A)IE

CLIE

(M)NLG

ControlledLanguage

OIE

SemanticWeb; Semantic Grid;Semantic Web Services

KEYMNLG: Multilingual Natural Language GenerationOIE: Ontology-aware Information ExtractionAIE: Adaptive IECLIE: Controlled Language IE

HLT: Closing the Loop

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Information Extraction• Information Extraction (IE) pulls facts and

structured information from the content of large text collections.

• Contrast IE and Information Retrieval• NLP history: from NLU to IE • Progress driven by quantitative measures• MUC: Message Understanding

Conferences • ACE: Advanced Content Extraction

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“The shiny red rocket was fired on Tuesday. It is the brainchild of Dr. Big Head. Dr. Head is a staff scientist at We Build Rockets Inc.”

IE Example

• ST: rocket launch event with various participants

• NE: "rocket", "Tuesday", "Dr. Head“, "We Build Rockets"

• CO:"it" = rocket; "Dr. Head" = "Dr. Big Head"• TE: the rocket is "shiny red" and Head's

"brainchild". • TR: Dr. Head works for We Build Rockets Inc.

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Performance levels(Extensive quantitative evaluation since early

’90s; mainly on text, ASR; now also video OCR)• Vary according to text type, domain, scenario,

language • NE: up to 97% (tested in English, Spanish,

Japanese, Chinese, others) • CO: 60-70% resolution • TE: 80% • TR: 75-80% • ST: 60% (but: human level may be only 80%)

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Ontology-based IEXYZ was established on 03 November 1978 in London. It opened a plant in Bulgaria in …

Ontology & KB

Company

type

HQ

establOn

City Country

Location

partOf

type

type type

“03/11/1978”

XYZLondon

UK Bulgaria

HQpartOf

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EntityPerson

…

Job-title

president

chancellorminister

…

G.Brown

“Gordon Brown met George Bush during his two day visit.

Classes, instances & metadata

Classes+instances before

Bush

<metadata> <DOC-ID>http://… 1.html</DOC-ID> <Annotation> <s_offset> 0 </s_offset> <e_offset> 12 </e_offset> <string>Gordon Brown</string>

<class>…#Person</class> <inst>…#Person12345</inst>

</Annotation> <Annotation> <s_offset> 18 </s_offset> <e_offset> 32 </e_offset> <string>George Bush</string>

<class>…#Person</class> <inst>…#Person67890</inst>

</Annotation></metadata>

Classes+instances

after

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An example: the MUMIS project• Multimedia Indexing and Searching Environment • Composite index of a multimedia programme from

multiple sources in different languages• ASR, video processing, Information Extraction (Dutch,

English, German), merging, user interface• University of Twente/CTIT, University of Sheffield,

University of Nijmegen, DFKI, MPI, ESTEAM AB, VDA• An important experimental result: multiple sources for

same events can improve extraction quality– PrestoSpace applications in news and sports archiving

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Semantic Query

Not “goal Beckham”(includes e.g. missed goals, or “this was not a goal”)

Instead: “goal events with scorer David Beckham”

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The results: England win!

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GATE, a General Architecture for Text Engineering is...

• An architecture A macro-level organisational picture for LE software systems.

• A framework For programmers, GATE is an object-oriented class library that implements the architecture.

• A development environment For language engineers, a graphical development environment.

GATE comes with...• Free components, and wrappers for other peoples’ stuff• Tools for: evaluation; visualise/edit; persistence; IR; IE; dialogue;

ontologies; etc.• Free software (LGPL) at http://gate.ac.uk/download/• Used by thousands of people at hundreds of sites

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A bit of a nuisance (GATE users)GATE team projects. Past:• Conceptual indexing: MUMIS: automatic

semantic indices for sports video• MUSE, cross-genre entitiy finder• HSL, Health-and-safety IE• Old Bailey: collaboration with HRI on

17th century court reports• Multiflora: plant taxonomy text analysis

for biodiversity research e-science • ACE / TIDES: Arabic, Chinese NE• JHU summer w/s on semtagging• EMILLE: S. Asian languages corpus• hTechSight: chemical eng. K. portalPresent:• Advanced Knowledge Technologies:

€12m UK five site collaborative project• SEKT Semantic Knowledge Technology• PrestoSpace MM Preservation/Access• KnowledgeWeb Semantic WebFuture:• New eContent project LIRICS

Thousands of users at hundreds of sites. A representative sample: • the American National Corpus project • the Perseus Digital Library project, Tufts

University, US• Longman Pearson publishing, UK• Merck KgAa, Germany• Canon Europe, UK• Knight Ridder, US• BBN (leading HLT research lab), US• SMEs inc. Sirma AI Ltd., Bulgaria• Stanford, Imperial College, London, the

University of Manchester, UMIST, the University of Karlsruhe, Vassar College, the University of Southern California and a large number of other UK, US and EU Universities

• UK and EU projects inc. MyGrid, CLEF, dotkom, AMITIES, Cub Reporter, EMILLE, Poesia...

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GATE – infrastructure for semantic metadata extraction

• Combines learning and rule-based methods (new work on mixed-initiative learning)

• Allows combination of IE and IR • Enables use of large-scale linguistic resources

for IE, such as WordNet• Supports ontologies as part of IE applications -

Ontology-Based IE• Supports languages from Hindi to Chinese,

Italian to German

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PrestoSpace Semantics Architecture

EN

FormalText

FormalText

FormalTextFormal

TextFormal

TextFormal

TextFormalText

FormalText

FormalTextText

Sources

IE

IE

IE

IT

FormalText

FormalText

FormalTextFormalText

FormalText

FormalTextFormalText

FormalText

FormalText

Signal md, Transcr-iptions

ASR,etc.

Formal

Text

Formal

Text

Formal

Text

Formal

Text

Formal

Text

Formal

Text

Formal

Text

Formal

Text

Formal

Text

Formal

Text

Formal

Text

AVSignals

Merging Final Annotations

Formal

TextForma

lText

Formal

Text

Anno-tations

MultilingualConceptual

Q & A

...

Ontology-Based

Metadata

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Memory is not a luxury

•C21st: all the C20th mistakes but bigger & better?•If you don’t know where you’ve been, how can you know where you’re going?

•Archives: ammunition in the war on ignorance•Ammunition is useless if you can’t find it: new technology must make our history accessible to all, for all our futures

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Links

This talk:

http://gate.ac.uk/sale/talks/eculture-graz-may2004.ppt

Related projects: