The UK Ontology Networks Workshop took place over one day in the Informatics Forum.
There was a mix of people there; some talks were way over my head and very technical, and some talks were by people who confessed they had had to look up "ontology" that morning. And things in between.
Lazy writeup, but following are notes as I scribbled them:
John Callahan
US navy research.
Focused information integration.
Human intervention to keep predictive part on track. Tweaking.
Alan Bundy
Interaction of representation and reasoning.
Changing world so agents must evolve. How to automate? What would trigger a need for change:
Inconsistency
Incompleteness
Inefficiency
how to diagnose which?
Interested in language and perception change.
Unsorted first order logic algorithm called Reformation. Based on standard unification algorithm.
Allows blocking and unblocking unification.
Phil Barker
Schema.org
Cetis (JISC funded)
learning resource metadata initiative.
Big names behind schema.org.
= ontology + syntax
Big and growing ontology.
Dumbed down for people.
LRMI adds to it. W3C go through it. It's creeping, how much do the big names actually care about stuff that's added?
don't know how Google uses it.
People should consider using it for more sophisticated search and disambiguation.
Gill Hamilton
Doing more with library metadata. Learnt from OKFN. Had to convince people in charge.
Dublin core, didn't like; not specific enough. Instead RDF > OWL. "We know best how to structure our data"
Hardest was convincing marketing people that there was no commercial value. Metadata is advert to actual resource.
Enrico Motta
Traditionally top down approach. So now so many people interacting with semantic structures, so should involve users.
Recognise there isn't a unique or best way of doing things.
Initial study included modeling task with binary relations.
Patterns that are more or less intuitive. 4D least, 3D+1 most.
N-ary most widely used by experts.
Relationship between reasoning power and intuitiveness of writing? More creativity needed for simpler ones. (Not really sure what he's saying)
Email him for copy of study.
Chris Mellish
Ontology authoring is hard. Better ways to do it.
Controlled language input (mature tech); responsive reasoning (also mature, information as you're editing); understanding the process (beginning to understand more).
Hypotheses:
users don't know what they're doing. What if questions. Many answers, what is relevant? Depends on context.
Authoring as dialogue.
Todo list.
Useable in the same ways as protégé.
Peter Winstanley
UN classification schemes.
Various vocabularies.
Allow development of cross mapping between government administrations.
Mostly internal currently. Moves to bring externalizing data into the 21st century.
Peter Murray-Rust
Fight for your Ontologies.
Ontologies in physical sciences. Chemists don't want ontologies. They'll sue you.
Crystallography uses 'dictionary'. Written in CIF. 20 years to build CIF.
Compare physical sciences to government.
Every program author writes dictionaries that work for them. When different parties agree, promote to communal dictionary. Provide conventions to help disagreements.
Show a company can do it as opposed to a rabbiting academic ..
Jeff Pan
Tractable ontological stream reasoning.
Need to be more efficient, scaleable, as things change. Inputs from web.
Dealing with complexities: approximate owl2.
Dealing with frequent updates: to-add stream and to-do delete stream. Truth maintenance. Evaluation criteria.
Trowl.EU can use with protégé, also supports jena.
Edoardo Pignotti
Semantic web tech to support Interdisciplinary research.
ourSpaces VRE
Provenance crucial.
OPM prov ontology.
Deployed since 2009, 180 users. Comprehensive ontologies but people unwilling to provide metadata.
paper! Edwards et al. ourSpaces.
Tom Grahame (BBC) @tfgrahame
Content arrangement on BBC sport by tagging, automatic to free up editors to write.
LD API so systems don't need to know about each other.
Growing from simple rdfxml to more complex ontology.
Can ask much more general and much more detailed questions about sport.
Mapping incoming data is outsourced.
Lots of errors, sometimes system alerts, sometimes manual.
Working on opening the data. Maybe a dump, but licensing issues.
Ewan Klein
Mining old texts for commodities, adding place and time and putting in structured database.
Transcriptions of customs import records.
Skos for synonyms.
Dbp concepts.
Why? Want to query.
Visualisations.
Tools? Python script.
Janice Watson
Harnessing clinical terminologies and classifications for healthcare improvements.
Bob Barr
Geographical addressing.
Addressing and address geocoding is important and broad. Not always postal, but this not addressed (punlol) in ontologies.
Different contexts change meaning of address (for delivering, you only care about postbox; property sale whole building).
Loads of things to address. Loads of reasons why.
Work held up as national address file is owned by royal mail and might be sold!
Fiona McNeill
Run time extraction of data. Failure driven. Looking at extraction of specific information.
Emergency response. Lots of data, timely sharing of data required.
From domestic level to humanitarian disasters.
How can it be automated?
Multilayered incompatibility.
Format
Terminology
Structure
...
Richard Gunn
Towards an intelligent information industry.
Elena Simperl (Soton, sociam)
Crowdsourcing ontology engineering.
CSrc: Brabham 2008.
Distribute task into smaller atomic units.
Humans validating results that are automatically detected as not accurate.
What are the costs? What resources?
Games with a purpose. Like quizzes.
Micropayments or vouchers.
MTurk. CrowdFlower.
Paper about useage of microtask crowdsourcing. ISWC 2012.
Claudia Paglieri
Ontologies in ehealth.
Enrico Motta - Rexplore
Klink algorithm mines relations between research topics.
Use this! Nope, it's not public. Uees MS Academic research.
Peter Murray-Rust
Content mining expands regular text mining.
Focus on academic stuff.
Chemical Tagger. Takes chemistry jargon and annotated it, knows actions, conditions, molecules etc.. NLP. Uses ontologies and contributes to ontologies.
In chemistry, no need to put everything in rdf because there are already lots of formalisms.
Proper cool PDF to sensible format conversion. Amy the kangaroo. Looking for collaborators.
Yuan Ren
Ontology authoring in whatif project.
Reasoning with protégé and trowl .
Tractable reasoning. Trowl v fast.
Notes from conversations / breakout discussions:
BBC use owlm triplestore .
Store all their datasets in svn. But they have reads and writes to the live triplestore all the time.
Lots of people saying minimise owl use because of unpredictable output.
Versioning ontologies (available in owl2) in case third parties change stuff you use. You're dependent on their software engineering practices. Only good if they're ahead of the game.
IRIs, Arabic characters in ontologies!
Semantic heavy, maybe make a decision to abstract away to ids and make heavier use of labels.
Difference between importing and using someone else's.
There's no (practically useful) software that lets you reason over stuff you haven't imported? (over HTTP?)
Build ontology from reality (data), don't start with no data.
Lode.
Problems with dbpedia URIs changing or disappearing.
Hard to visualize massive graphs. Relational, tabular much easier to understand.
There was a mix of people there; some talks were way over my head and very technical, and some talks were by people who confessed they had had to look up "ontology" that morning. And things in between.
Lazy writeup, but following are notes as I scribbled them:
John Callahan
US navy research.
Focused information integration.
Human intervention to keep predictive part on track. Tweaking.
Alan Bundy
Interaction of representation and reasoning.
Changing world so agents must evolve. How to automate? What would trigger a need for change:
Inconsistency
Incompleteness
Inefficiency
how to diagnose which?
Interested in language and perception change.
Unsorted first order logic algorithm called Reformation. Based on standard unification algorithm.
Allows blocking and unblocking unification.
Phil Barker
Schema.org
Cetis (JISC funded)
learning resource metadata initiative.
Big names behind schema.org.
= ontology + syntax
Big and growing ontology.
Dumbed down for people.
LRMI adds to it. W3C go through it. It's creeping, how much do the big names actually care about stuff that's added?
don't know how Google uses it.
People should consider using it for more sophisticated search and disambiguation.
Gill Hamilton
Doing more with library metadata. Learnt from OKFN. Had to convince people in charge.
Dublin core, didn't like; not specific enough. Instead RDF > OWL. "We know best how to structure our data"
Hardest was convincing marketing people that there was no commercial value. Metadata is advert to actual resource.
Enrico Motta
Traditionally top down approach. So now so many people interacting with semantic structures, so should involve users.
Recognise there isn't a unique or best way of doing things.
Initial study included modeling task with binary relations.
Patterns that are more or less intuitive. 4D least, 3D+1 most.
N-ary most widely used by experts.
Relationship between reasoning power and intuitiveness of writing? More creativity needed for simpler ones. (Not really sure what he's saying)
Email him for copy of study.
Chris Mellish
Ontology authoring is hard. Better ways to do it.
Controlled language input (mature tech); responsive reasoning (also mature, information as you're editing); understanding the process (beginning to understand more).
Hypotheses:
users don't know what they're doing. What if questions. Many answers, what is relevant? Depends on context.
Authoring as dialogue.
Todo list.
Useable in the same ways as protégé.
Peter Winstanley
UN classification schemes.
Various vocabularies.
Allow development of cross mapping between government administrations.
Mostly internal currently. Moves to bring externalizing data into the 21st century.
Peter Murray-Rust
Fight for your Ontologies.
Ontologies in physical sciences. Chemists don't want ontologies. They'll sue you.
Crystallography uses 'dictionary'. Written in CIF. 20 years to build CIF.
Compare physical sciences to government.
Every program author writes dictionaries that work for them. When different parties agree, promote to communal dictionary. Provide conventions to help disagreements.
Show a company can do it as opposed to a rabbiting academic ..
Jeff Pan
Tractable ontological stream reasoning.
Need to be more efficient, scaleable, as things change. Inputs from web.
Dealing with complexities: approximate owl2.
Dealing with frequent updates: to-add stream and to-do delete stream. Truth maintenance. Evaluation criteria.
Trowl.EU can use with protégé, also supports jena.
Edoardo Pignotti
Semantic web tech to support Interdisciplinary research.
ourSpaces VRE
Provenance crucial.
OPM prov ontology.
Deployed since 2009, 180 users. Comprehensive ontologies but people unwilling to provide metadata.
paper! Edwards et al. ourSpaces.
Tom Grahame (BBC) @tfgrahame
Content arrangement on BBC sport by tagging, automatic to free up editors to write.
LD API so systems don't need to know about each other.
Growing from simple rdfxml to more complex ontology.
Can ask much more general and much more detailed questions about sport.
Mapping incoming data is outsourced.
Lots of errors, sometimes system alerts, sometimes manual.
Working on opening the data. Maybe a dump, but licensing issues.
Ewan Klein
Mining old texts for commodities, adding place and time and putting in structured database.
Transcriptions of customs import records.
Skos for synonyms.
Dbp concepts.
Why? Want to query.
Visualisations.
Tools? Python script.
Janice Watson
Harnessing clinical terminologies and classifications for healthcare improvements.
Bob Barr
Geographical addressing.
Addressing and address geocoding is important and broad. Not always postal, but this not addressed (punlol) in ontologies.
Different contexts change meaning of address (for delivering, you only care about postbox; property sale whole building).
Loads of things to address. Loads of reasons why.
Work held up as national address file is owned by royal mail and might be sold!
Fiona McNeill
Run time extraction of data. Failure driven. Looking at extraction of specific information.
Emergency response. Lots of data, timely sharing of data required.
From domestic level to humanitarian disasters.
How can it be automated?
Multilayered incompatibility.
Format
Terminology
Structure
...
Richard Gunn
Towards an intelligent information industry.
Elena Simperl (Soton, sociam)
Crowdsourcing ontology engineering.
CSrc: Brabham 2008.
Distribute task into smaller atomic units.
Humans validating results that are automatically detected as not accurate.
What are the costs? What resources?
Games with a purpose. Like quizzes.
Micropayments or vouchers.
MTurk. CrowdFlower.
Paper about useage of microtask crowdsourcing. ISWC 2012.
Claudia Paglieri
Ontologies in ehealth.
Enrico Motta - Rexplore
Klink algorithm mines relations between research topics.
Use this! Nope, it's not public. Uees MS Academic research.
Peter Murray-Rust
Content mining expands regular text mining.
Focus on academic stuff.
Chemical Tagger. Takes chemistry jargon and annotated it, knows actions, conditions, molecules etc.. NLP. Uses ontologies and contributes to ontologies.
In chemistry, no need to put everything in rdf because there are already lots of formalisms.
Proper cool PDF to sensible format conversion. Amy the kangaroo. Looking for collaborators.
Yuan Ren
Ontology authoring in whatif project.
Reasoning with protégé and trowl .
Tractable reasoning. Trowl v fast.
Notes from conversations / breakout discussions:
BBC use owlm triplestore .
Store all their datasets in svn. But they have reads and writes to the live triplestore all the time.
Lots of people saying minimise owl use because of unpredictable output.
Versioning ontologies (available in owl2) in case third parties change stuff you use. You're dependent on their software engineering practices. Only good if they're ahead of the game.
IRIs, Arabic characters in ontologies!
Semantic heavy, maybe make a decision to abstract away to ids and make heavier use of labels.
Difference between importing and using someone else's.
There's no (practically useful) software that lets you reason over stuff you haven't imported? (over HTTP?)
Build ontology from reality (data), don't start with no data.
Lode.
Problems with dbpedia URIs changing or disappearing.
Hard to visualize massive graphs. Relational, tabular much easier to understand.
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