developing semantic web sites: results and lessons learnt enrico motta, yuangui lei, martin dzbor,...
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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)
Story Database
NewsBoy
NewsHound
Modelling Language (OCML)
Planet KB
KA Tool
QueryInterface
Planet Ontology
Web BrowserWebOnto
Architecture of Planet-Onto
Story Database
NewsBoy
NewsHound
Modelling Language (OCML)
Planet KB
KA Tool
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
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