Developing Semantic Web Sites: Results and Lessons Learnt

Enrico Motta, Yuangui Lei, Martin Dzbor, Vanessa Lopez, John Domingue,

Jianhan Zhu, Liliana Cabral, Alex Goncalves, Victoria Uren

Motivation for KMi Sem Web

Key Objective To generate a live, declarative representation of

what happens in KMi, which can support smart queries and the specification of intelligent services producing smart inferences on the basis of this data

Initial version was ready in 1998 PlanetOnto System (95-98)

Relates-event

PeopleProjectOrganizationTechnology

Event

Story

Story Database

NewsBoy

NewsHound

Modelling Language (OCML)

Planet KB

KA Tool

Email

QueryInterface

Planet Ontology

Web BrowserWebOnto

Architecture of Planet-Onto

Story Database

NewsBoy

NewsHound

Modelling Language (OCML)

Planet KB

KA Tool

Email

QueryInterface

Planet Ontology

Web BrowserWebOnto

Architecture of Planet-Onto

Key Criteria for Sem Web Site

Emphasis on Automatic KA Fully automated generation of information

No knowledge capture bottleneck Manual annotation is welcome but should not be a core

part of the process Manual annotation should not require sophisticated KR

skills Ideally manual annotation should take place through

side effects generating from normal work activities

Architecture Keep the semantic layer separated (and to some

extent independent) from the actual web site

Interoperability Semantic Web Site ought to be open

Semantic representation publicly available to any reasoning engine who wants to use the information

DBs

DBs

Mapping Specs

KMi Semantic Web Site

Docs

XML mark-up

Mapping Engine

Domain ontology

RawKB

Data Verification Engine

KB

Source Data Integration Layer Verification Layer Target Data

Information Extraction Engine

(Espotter)

Ontological Structure

KMi Semantic Web

KMi Ontology

AKT Portal Ontology

AKT Support Ontology

AKT Reference Ontology

Publications

Projects

Research Areas

People

Organizations

Technologies

News

Key Categories

Data verification

Finding and eliminating duplicate data

Recognizing ambiguous data, e.g. finding correct person instances for names like John, Victoria

Using a lexicon component to record the mappings between strings and instance names found in the previous processes

Using contextual information to decide

Number People Organizations Projects Research Area

Total

Manual data 93 75 25 23 216

ESpotter finds 77 58 17 13 165

ESpotter Recall-rate 0.827 0.773 0.68 0.565 0.763

Initial Evaluation

People Organizations

Projects Research Area Total

Total (discovered) 84 97 19 15 215

Wrong 4 18 1 2 25

Precision rate 0.9523 0.71 0.947 0.86 0.883

Recall

Precision

KMi Semantic Web

So What?

At a basic level, the architecture works Automatic generation is key

Services still limited Developing interesting services requires non

trivial effort

Brittleness is a problem You rapidly reach the boundaries of the

knowledge held in KMi resources and performance decreases

Badly needs integration with other similar resources

No API. Data available only as sources

What should happen next

Integration with other similar activities Hence this workshop….

Ability to bring in knowledge expressed in other ontologies

Need for standardised APIs/knowledge servers

Develop mechanisms for semantic annotation by side-effect

Improve text mining technology to improve both the quantity and the quality of the knowledge

Develop more value-adding services

Services defined for a particular

Class in a particular Ontology are

available to any system who

asks for them

Intg. with Sem Web Services