Semantic Spaces

Professor Nigel Shadbolt

Director of AKT

School of Electronics and Computer Science

University of Southampton

Structure of the Talk

• Introduction• Semantic Spaces: The vision and the reality• Ingredients for Semantic Spaces

– Ontologies– Heterogeneous Information Sources– Navigation and visualisation of the space– Knowledge Processing Services– Socio-technical Challenges

• Interleave with two primary examples from AKT – CS ATKive Space

http://triplestore.aktors.org/demo/AKTiveSpace/– MIAKT

www.aktors.org/miakt

Advanced Knowledge Technologies IRC

AKT started Sept 00, 6 years, £8.8 Meg, EPSRC

www.aktors.org

Around 65 investigators and research staff

Dramatis Personae

Departments PIs CIs

Computing Sciences, Aberdeen

Derek Sleeman Peter Gray

Alun Preece

Informatics, Edinburgh Austin Tate

Dave Robertson

KMI, OU Enrico Motta Simon Buckingham-Shum

John Domingue

Computer Science Yorick Wilks Fabio Ciravegnia

Hamish Cunningham

ECS, Southampton Nigel Shadbolt

Wendy Hall

Leslie Carr

Dave De Roure

Hugh Glaser

Kieron O’Hara

Semantic Spaces: The Vision

Structured Spaces

• Linkage of heterogeneous information– web content

– databases

– meta-data repository

– multimedia

• Via ontologies as information mediation structures

• Using Semantic Web languages

Oncogene(MYC): Found_In_Organism(Human). Gene_Has_Function(Transcriptional_Regulation). Gene_Has_Function(Gene_Transcription). In_Chromosomal_Location(8q24). Gene_Associated_With_Disease(Burkitts_Lymphoma).

NCI Cancer Ontology (OWL)

<meta> <classifications> <classification type="MYC” subtype="old_arx_id">bcr-2-1-059</classification> </classifications></meta>

BioMedCentral Metadata (XML)

Web data set (XHTML)

Vocabulary (RDFS)

Services on the Space

Hendler 03- Science

So what have we got?

• A very particular KRL for the web• Very low take up of structured meta-data beyond

XML• What RDF exists is largely FOAF• A variety of demonstrators on the small to

medium scale • Few deployed examples• A lot of Good Old Fashioned Artificial

Intelligence (GOFAI) proposals in the wings• But this could be our big chance….

Semantic Spaces: Ontologies

Perspectives on ontologies

Source, Concepts for Automating Systems Integration, E. Barkmeyer, A. Feeney, P. Denno, D. Flater, D. Libes, M. Steves, E. Wallace. NISTIR 6928, NIST Feb., 2003

• The semantic view: An ontology is the context needed to understand a specification, model, or other communication in the way that was intended.

• The specification / reference view: "An ontology is an explicit specification of a conceptualization." and "Commitment to a common ontology is a guarantee of consistency [in terminology]." Simple taxonomies and thesauri are included in this definition as degenerate cases.

• The modeling view: An ontology is a metamodel. • The automation view: An ontology is, or is captured in,

a knowledge base designed to support automatic reasoning.

Ontologies offer….

• Communication– Normative models– Networks of relationships– Consistent and

unambiguous– Integrate multiple

perspectives

• Inter-operability and Integration: Sharing & Reuse– Inter-lingua– Specifications– Reliability

• Control– Controlled vocabularies – Accurate data collection or

retrieval– Classification– Finding, sharing,

discovering, navigation, indexing

Medicine: The UMLS®

• Extensive Medical Nomenclature Project

• Integrative– SnoMed

• Translation work into OWL

• Being widely adoped• High level

Governmental support

OrganismAttribute

AnatomicalStructure

EmbryonicStructure

AnatomicalAbnormality

CongenitalAbnormality

AcquiredAbnormality

Fully FormedAnatomical

Structure

Finding

Laboratory orTest Result

Sign orSymptom

BodySubstance

Body System

part of

part of

part of part of

part of

Body Part, Organ orOrgan Component

Tissue Cell CellComponent

Gene orGenome

Injury orPoisoning

property of

evaluation of

Body Spaceor Junction

conceptualpart of

Body Locationor Region

conceptualpart of

produces,contains

disrupts

disrupts

process of

conceptualpart of

evaluation of

isa linksnon-isa relations

conceptualpart of

BiologicFunction

PhysiologicFunction

Organ orTissue

Function

CellFunction

MolecularFunction

OrganismFunction

GeneticFunction

MentalProcess

PathologicFunction

Cell orMolecular

Dysfunction

Experimentalmodel

of Disease

Disease orSyndrome

Mental orBehavioral

Dysfunction

NeoplasticProcess

location of

adjacent to

location of

co-occurs with

Organism

Alga

Fungus Virus Rickettsiaor

Chlamydia

Bacterium Animal

Invertebrate Vertebrate

Amphibian Bird Fish

PlantArchaeon

ReptileMammal

Human

OrganismAttribute

AnatomicalStructure

EmbryonicStructure

AnatomicalAbnormality

CongenitalAbnormality

AcquiredAbnormality

Fully FormedAnatomical

Structure

Finding

Laboratory orTest Result

Sign orSymptom

Laboratory orTest Result

Sign orSymptom

BodySubstance

Body System

part of

part of

part of part of

part of

Body Part, Organ orOrgan Component

Tissue Cell CellComponent

Gene orGenome

Injury orPoisoningInjury orPoisoning

property of

evaluation of

Body Spaceor Junction

conceptualpart of

Body Locationor Region

conceptualpart of

produces,contains

disrupts

disrupts

process of

conceptualpart of

evaluation of

isa linksnon-isa relationsisa linksnon-isa relations

conceptualpart of

BiologicFunction

PhysiologicFunction

Organ orTissue

Function

CellFunction

MolecularFunction

OrganismFunction

GeneticFunction

MentalProcess

PathologicFunction

Cell orMolecular

Dysfunction

Experimentalmodel

of Disease

Disease orSyndrome

Mental orBehavioral

Dysfunction

NeoplasticProcess

Mental orBehavioral

Dysfunction

NeoplasticProcess

location of

adjacent to

location of

co-occurs with

Organism

Alga

Fungus Virus Rickettsiaor

Chlamydia

Bacterium Animal

Invertebrate Vertebrate

Amphibian Bird Fish

PlantArchaeon

ReptileMammal

Human

Organism

Alga

Fungus Virus Rickettsiaor

Chlamydia

Bacterium Animal

Invertebrate Vertebrate

Amphibian Bird Fish

PlantArchaeon

ReptileMammal

Human

Mammal

Human

Genetics: Gene Ontology

• One of the earliest examples of the benefits of ontologies

• Integration and interoperability were big wins

• Specific tool support• Considerable resources

invested and continuing in maintenance

• Translation into DLs• Spawned more generic

biological ontology efforts

Manufacturing: Aerospace

• Considerable work on ontologies for products and components

• Used in all stages of the life cycle, from design to in service maintenance

• Need for multiple perspectives e.g– Whole engine– Heat transfer– Cost model– Manufacturing– Assembling/Maintenance

Military: Coalition Operations

• Some of the original motivation behind DAML work

• Lots of activity to build ontologies in a range of contexts

• Particularly important in coalition operations

• Central requirement for the concept of Network Enabled Capability

Computer Science: The AKT Ontology

• Designed as a learning case for AKT

• Adopted for our own Semantic Web experiments including CS AKTive

• Uses a number of Upper Ontology Fragments

• Reusable in many University and Research Contexts

MIAKT: Multi-disciplinary Assessment

• Multiple stakeholders• Multiple viewpoints and

vocabularies– Breast imaging – X-ray,

ultrasound, MRI– Clinical examination– Microscopy – cells and

tissues (also, hormone receptors)

• Local dialects in use• Variation between

countries due to factors such as insurance claims!

Ontologies: Observations

• In any domain– Usually highly implicit– Poorly documented– Likely to be ambiguous, vague, inconsistent

• When modelling– Interaction Problem: tasks influence ontologies– Integration Problem: integrating multiple ontologies– Modularity Problem: how to modularise and what grain size?

• Maintenance– Ongoing maintenance overhead– Ontologies evolve and change– Design rationale is important

• Upside– They do facilitate interoperability– They do enhance reuse– They are becoming part of the infrastructure

The Crucial Role Standards Play

HTML XML + Name Space + XML Schema

Topic Maps

SMIL

RDF(S)XOL

OWL

RDF

Unicode URI

Semantic Spaces: Heterogeneous Information

Sources

What might Heterogeneous Information Sources mean?

• Provenance– Could be legacy– Not necessarily under direct control– Variable validity

• Form– More or less structured– Different syntactic and semantic formats– Multimedia– Distributed in space or time

• Function– Collected for different reasons

MIAKT DEMO

• Clinical examination– Notes

• Imaging– X-ray, – Ultrasound– MRI

• Microscopy– Histopathology

• Treatment– Protocol Records– Re-assessment

• Medical Records– Case sets– Individual patient records

• Published background– Epidemiology– Medical Abstracts

AKTive Spaces• Content harvested and published from

multiple Heterogeneous Sources Higher Education directories

• 2001 RAE submissions• UK EPSRC project database (all

grants awarded by EPSRC in the past decade)

• Detailed data on personnel, projects and publications harvested for:

– all AKT partners– all 5 or 5* CS departments in the UK– Automatic NL mining: Armadillo

• Additional resources– All the world's countries (from

ISO3166-1)– All UK administrative areas (from

ISO3166-2)– All UK settlements listed in the UN

LOCODE service– All the world's airports (from the IATA)– (and they're all integrated via the AKT

reference ontology)

Semantic Spaces: Navigation and Visualisation

Aspects to navigating or visualising a semantic space

• Semantic Interfaces– Ontology as a navigable structure– Semantic encoding visualisations

• Scope– Local to global– Domain specific or generic

• Function– Reader to author– Individual to collaborative

• Navigation and visualisation via graphical characterisation of ontology

• Ontological relations are also the essential relations that are used to navigate the information space

• Natural Language Generation is used to provide a summary of content held in the image

W3photo and AKTive photo

• An AKTive Space for photo annotation

• The annotation is direct from the ontology

• The navigation is also based on the ontology

• Re-ordering columns (classes) exposes different parts of the information space

CS AKTive Space

• An AKTive Space for CS research

• The navigation is also based on the AKT ontology

• Re-ordering columns (classes) exposes different parts of the information space

• Complex RDQL dispatched behind the direct manipulation interface

• Strong geographical overaly

Semantic Spaces: Knowledge Processing Services

What constitutes a semantic service?

• Have semantic characterisation– What is the goal or task achieving effect?– What are its “knowledge level” preconditions

or inputs

• Compositionality– Grain size– Internal and external aspects

• Discoverable or locatable– Accessible– Maintained

MIAKT: Overall Framework and Current Services

CS AKTive Space Services

• Triple Store (3store) and associated browser navigation• RDQL interface to 3store• Harvesting and scraping• M-space visualisation• Community of Practice• Armadillo – publication harvesting

The CS AKTive Space:Semantic Web Challenge Winner

2003• 24/7 update of content• Content continually harvested and acquired against

community agreed ontology• Easy access to information gestalts - who, what, where• Hot spots

– Institutions

– Individuals

– Topics

• Impact of research– citation services etc

– funding levels

– Changes and deltas

• Dynamic Communities of Practice…

CS AKTive Space

DEMO

Semantic Spaces: Socio-Technical Context

Semantic Spaces: A Challenge?

“Is this rocket science? Well, not really …We are not inventing relational models for data, or query systems or rule-based systems. We are just webizing them. We are just allowing them to work together in a decentralized system - without a human having to custom handcraft every connection.”

Tim Berners-Lee, Business Case for the Semantic Web,http://www.w3.org/DesignIssues/Business

Technical Challenges in Semantic Space

• Annotation• Content capture/harvesting• Ontology mapping and alignment• Referential Integrity• Reasoning Services including incorporation of

statistical and probabilistic methods • Semantic service composition• Provenance and Trust• Multimedia content• Semantic HCI

Social Challenges of Semantic Spaces

• Social Issues– How do you get communities to participate?– Mandate and require– The need to share information e.g. e-Science– Become social and viral e.g. early days of

web and FOAF

• Regulatory– Fidelity of content is on the high side but even

so…provenance and quality services– Data Protection and information assurance