Semantics, Syndication and Social Networks: Mechanisms for Future Structured Information Spaces
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Semantics, Syndication and Social Networks: Mechanisms for Future Structured Information Spaces Hamish Cunningham (University of Sheffield) Werner Haas (Johaneum Research) Ant Miller (BBC) Libby Miller (University of Bristol) Ralph Traphoener (Empolis / Bertelsmann)

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Semantics, Syndication and Social Networks: Mechanisms for Future Structured Information Spaces

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Semantics syndication and social networks mechanisms for future structured information spaces

  • Semantics, Syndication and Social Networks: Mechanisms for Future Structured Information Spaces

    • Hamish Cunningham (University of Sheffield)

    • Werner Haas (Johaneum Research)

    • Ant Miller (BBC)

    • Libby Miller (University of Bristol)

    • Ralph Traphoener (Empolis / Bertelsmann)

    • Paul Warren (British Telecom)


Semantics syndication and social networks mechanisms for future structured information spaces

What’s the difference between Mother Theresa and Tony Bliar?

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

Hamish Cunningham

Dept. Computer Science, University of Sheffield


Why semantic metadata

Different types of metadata allow different types of search (but also incur different costs and have different limits)

full text: "find me Nevsky in Bulgaria"

taxonomy / thesaurus / semantic annotation / ontology: "find me churches in Eastern Europe"

E.g. BBC's INFAX taxonomic system: 66% of searches would fail if only full text

The web promotes diversity but also fragmentation; there's too much of it; less and less impact for curated data

In face of this cultural memory institutions need

Syndication and mediation (to pool outlets and multiply impact); this means presentation-independent, multipurpose content

Users as assistants (to cut the cost of metadata); this can mean shared conceptualisations of content

How do we get there?

Why semantic metadata?

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The semantic web and why you can t have it yet

The semantic web is about a semantic layer for interoperability, machine-readability, inference – ideal for semantic libraries?

Problems:

Construction and maintenance of shared taxonomies, terminologies & ontologies is expensive

Annotation of content relative to them is v. expensive

How does a machine tell the difference between "Mother Theresa is a Saint" and "Tony Blair is a Saint"? (Beyond the shallow and the general we get into typical AI problems, the contextual and shifting nature of meaning, etc.)

The semantic web and why you can't have it (yet)

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Four promising directions

Use recommender systems to make the users into curators’ assistants (who tells Google which page is important? other web users do, by linking; also Amazon)

Allow curators and users to DIY simple specific ontologies and KBs (targetted adjuncts to general models like CIDOC)

Use Information Extraction (IE) to populate semantic models

Ride the next wave of social software and on-line communities (Wikis, Bloggs, OSN, file sharing / P2P, RSS/ATOM)

Four promising directions

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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

IT context: the Knowledge Economy and Human Language

6


Semantics syndication and social networks mechanisms for future structured information spaces

HLT: Closing the Loop

KEY

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

(M)NLG

Semantic

Web;

Semantic

Grid;Semantic

Web

Services

Formal Knowledge(ontologies andinstance bases)

HumanLanguage

OIE

(A)IE

ControlledLanguage

CLIE

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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

General Architecture for Text Engineering (GATE): http://gate.ac.uk/

Information Extraction

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Ie example

“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.”

ST: rocket launch event with various participants

IE Example

  • 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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Ontology based ie

Bulgaria

London

XYZ

UK

Ontology-based IE

XYZ was establishedon 03 November 1978 in London. It opened a plant in Bulgaria in …

Ontology & KB

Location

Company

HQ

partOf

City

Country

type

type

HQ

type

type

establOn

partOf

“03/11/1978”

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Semantics syndication and social networks mechanisms for future structured information spaces

A Necessary Trade-Off

Domain specificity vs. task complexity:

general

acceptableaccuracy

specificity

domainspecific

complexity

complex

simple

bag-of-words

events

entities

relations

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Open information defended communities

Trend 1: seconds out, round 5: file sharing is about to go social

Trend 2: the living room is about to be computerised

What will happen when all your living room devices fold into a single PC?

Bill Gates hopes you'll be running Windoze, but Consumer Electronics firms bet on Linux & stable hardware (no viruses, no crashes, cheap, ...)

What if these two trends combine? Ubiquitous on-line communities centred on shared content, with a model of trust

What if memory institutions provide means of organising, explaining, interlinking the cross-over between modern popular culture and the curated memory?

Important because DRM is the beginning of the end of civilisation as we know it (controls how you consume media you buy; has the potential to be linked with censorship and with invasive behaviour logging)

you can't make digital objects behave like physical objects - unless you totally control the hardware and the operating system

if someone has control, then we may end up finding that someone has given the contract for preserving our culture to Haliburton

Open information, defended communities

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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?

Libraries, museums, archives: ammunition in the war on ignorance (more dangerous than “terror”?)

Ammunition is useless if you can’t find it: new technology must make our history accessible to all, for all our futures

Memory is not a luxury

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Summary

Cultural memory can benefit from semantic metadata, presentation-independence and repurposing

Semantic web technology:

no: it won’t make machines intelligent

perhaps: simple specific models can work

Four ways to cross the AI bridge: DIY models; recommenders; IE; OSN + P2P

This talk: http://gate.ac.uk/talks/ecdl-sept-2004.ppt

More: http://gate.ac.uk/●Related projects:

Summary

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