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Language Technologies and the Semantic Web: An Essential - - PowerPoint PPT Presentation

Jul 14, 2023 144 likes •539 views

Language Technologies and the Semantic Web: An Essential Relationship. Enrico Motta Professor of Knowledge Technologies Knowledge Media Institute The Open University Content of the Talk Update on the Semantic Web Beyond the hype

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Language Technologies and the Semantic Web: An Essential Relationship.

Enrico Motta Professor of Knowledge Technologies Knowledge Media Institute The Open University

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Content of the Talk

  • Update on the Semantic Web

– Beyond the hype

  • What it is
  • Why it is interesting
  • What’s its status?
  • Semantic Web and AI
  • Semantic Web Applications

– Key features – Reasoning on the Semantic Web – Key role of Language Technologies

  • Conclusions
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The Semantic Web in 2 minutes…

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  • a

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  • p

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The foaf ontology

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The SW as ‘Web of Data’

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Current status of the semantic web

  • 10-20 million semantic web documents

– Expressed in RDF, OWL, DAML+ OIL

  • 7K-10K ontologies

– These cover a variety of domains - multimedia, computing, management, bio-medical sciences, geography, entertainment, upper level concepts, etc…

The above figures refer to resources w hich are publicly accessible on the w eb

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The Semantic Web today

  • To a significant extent the Semantic Web is already in place and is

characterized by a widespread production of formalized knowledge models (ontologies and metadata), from a variety of different groups and individuals

– “The Next Knowledge Medium - An information network with semi- automated services for the generation, distribution, and consumption of knowledge”

  • Stefik, 1986

– “Knowledge modelling to become a new form of literacy?”

  • Stutt and Motta, 1997
  • Still primarily a research enterprise, however interest is rapidly

increasing in both governmental and business organizations

  • “early adopters” phase
  • The result is slowly emerging as an unprecedented knowledge

resource, which can enable a new generation of intelligent applications on the web

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Semantic Web Applications

What can you do with the Semantic Web?

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  • A ‘corporate ontology’ is

used to provide a homogeneous view over heterogeneous data sources

  • Often tackle Enterprise

Information Integration scenarios

  • Hailed by Gartner as one
  • f the key emerging

strategic technology trends

– E.g., see personal information management in Garlik

“Corporate Semantic Webs”

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Exploiting large scale semantics Next Generation SW Applications Semantic Web

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Exploiting large scale semantics Next Generation SW Applications Semantic Web

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NGSW Applications in the context of AI research

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Knowledge-Based Systems Large Body

  • f Know ledge

I ntelligent Behaviour

“Today there has been a shift in

  • paradigm. The fundamental problem of

understanding intelligence is not the identification of a few powerful techniques, but rather the question of how to represent large amounts of knowledge in a fashion that permits their effective use” Goldstein and Papert, 1977

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The Knowledge Acquisition Bottleneck Large Body

  • f Know ledge

I ntelligent Behaviour KA Bottleneck Know ledge

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SW as Enabler of Intelligent Behaviour I ntelligent Behaviour

Both a platform for knowledge publishing and a large scale source of knowledge

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KBS vs SW Systems

Very Variable High Degree of trust Very Variable High Quality Heterogeneous Homogeneous

  • Repr. Schem a

Extra Huge Small/ Medium Size Distributed Centralized Provenance SW System s Classic KBS

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Key Paradigm Shift A side-effect of being able to integrate different types of reasoning to handle size and heterogeneous quality and representation A function of sophisticated, logical, task- centric problem solving I ntelligent Behaviour SW System s Classic KBS

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Next Generation SW Applications: Examples

Case Study 1: Automatic Alignment of Thesauri in the Agricultural/ Fishery Domain

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Method

Concept_A

(e.g., Supermarket)

Concept_B

(e.g., Building)

Scarlet Scarlet

≡ ≡

Sem antic W eb Sem antic Relation ( ) Deduce Access

⊆

  • SCARLET - matching

by Harvesting the SW

  • Automatically select

and combine multiple

  • nline ontologies to

derive a relation

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Two strategies

Supermarket Building

Superm arket Shop

⊆ ⊆

PublicBuilding

⊆ ⊆

Building

Scarlet Scarlet

Cholesterol OrganicChemical

Cholesterol Steroid

⊆ ⊆

Lipid

⊆ ⊆

OrganicChem ical

Scarlet Scarlet

Steroid

≡ ≡ ≡ ≡ ≡

Deriving relations from (A) one ontology and (B) across ontologies.

Sem antic W eb

( A) ( B)

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Matching:

  • AGROVOC
  • UN’s Food and

Agriculture Organisation (FAO) thesaurus

  • 28.174 descriptor terms
  • 10.028 non-descriptor

terms

  • NALT
  • US National Agricultural

Library Thesaurus

  • 41.577 descriptor terms
  • 24.525 non-descriptor

terms

Experiment

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226 Used Ontologies

http:/ / 1 3 9 .9 1 .1 8 3 .3 0 :9 0 9 0 / RDF/ VRP/ Exam ples/ tap.rdf http:/ / reliant.teknow ledge.com / DAML/ SUMO.dam l http:/ / reliant.teknow ledge.com / DAML/ Mid-level-ontology.dam l http:/ / reliant.teknow ledge.com / DAML/ Econom y.dam l http:/ / gate.ac.uk/ projects/ htechsight/ Technologies.dam l

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Evaluation 1 - Precision

  • Manual assessment of 1000 mappings (15% )
  • Evaluators:

– Researchers in the area of the Semantic Web – 6 people split in two groups

  • Results:

– Comparable to best results for background knowledge based matchers.

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Evaluation 2 – Error Analysis

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Other Case Studies…

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Giving meaning to tags

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Example

Cluster_ 1: {college commerce corporate course education high

instructing learn learning lms school student}

education training1,4 qualification corporate1 institution university2,3 college2 postSecondary School2 school2 student3 studiesAt course3

  • ffersCourse

takesCourse activities4 learning4 teaching4

1http://gate.ac.uk/projects/htechsight/Employment.daml. 2http://reliant.teknowledge.com/DAML/Mid-level-ontology.daml. 3http://www.mondeca.com/owl/moses/ita.owl. 4http://www.cs.utexas.edu/users/mfkb/RKF/tree/CLib-core-office.owl.

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Conclusions

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Typical misconceptions…

  • “The SW is a long-term vision…

”

– Ehm… actually… it already exists…

  • “The SW will never work because nobody is going to annotate

their web pages”

– The SW is not about annotating w eb pages, the SW is a web

  • f data, most of which are generated from DBs, or from web

mining software, or from applications which produce SW data as a side effect of supporting users’ tasks

  • “The idea of a universal ontology has failed before and will fail
  • again. Hence the SW is doomed”

– The SW is not about a single universal ontology. Already there are around 10K ontologies and the number is growing… – SW applications may use 1, 2, 3, or even hundreds of ontologies.

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SW and Language Technologies

  • All the applications mentioned here combine

language, web, statistical and semantic technologies

  • Heterogeneity and sloppy modelling implies that

language and statistical technologies are almost always needed when building NGSW apps

  • In contrast with traditional KBS, intelligent

behaviour is more a side-effect of intg. multiple techniques to handle scale and heterogeneity, rather than a function of powerful deductive reasoning

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