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Ontolog Invited Speaker Presentation - Professor Ian Horrocks - Thu 2010.07.22     (1)

  • Invited Speaker: Professor IanHorrocks (University of Oxford)     (1B)
  • Presentation Title: "Scalable Ontology-Based Information Systems"     (1C)

  • Shared-screen support (VNC session) will be started 5 minutes before the call at: http://vnc2.cim3.net:5800/     (1E5)
    • view-only password: "ontolog"     (1E5A)
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    • people behind corporate firewalls may have difficulty accessing this. If that is the case, please download the slides above and running them locally. The speaker(s) will prompt you to advance the slides during the talk.     (1E5C)
  • Discussions and Q & A:     (1E6)
    • (Unless the conference host has already muted everyone) Please mute your phone, by pressing "*2" on your phone keypad, when a presentation is in progress. To un-mute, press "*3"     (1E6A)
    • You can type in your questions or comments through the browser based chat session by:     (1E6B)
      • instructions: once you got access to the page, click on the "settings" button, and identify yourself (by modifying the Name field). You can indicate that you want to ask a question verbally by clicking on the "hand" button, and wait for the moderator to call on you; or, type and send your question into the chat window at the bottom of the screen.     (1E6C1)
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    • thanks to the soaphub.org folks, one can now use a jabber/xmpp client (e.g. gtalk) to join this chatroom. Just add the room as a buddy - (in our case here) ontolog_20100722@soaphub.org ... Handy for mobile devices!     (1E6E)
  • Please note that this session will be recorded, and the audio archive is expected to be made available as open content to our community membership and the public at-large under our prevailing open IPR policy.     (1E10)

Attendees     (1G)

Agenda & Proceedings     (1H)

  • Session Format and Agenda:     (1H1)
    • this will be virtual session over a phone conference setting, augmented by in-session chat and shared computer screen support     (1H1A)
    1. The session will start with a brief self-introduction of the attendees (~15 min.) [We will be skipping this if there are more than 20 participants.]     (1H2A)
    2. Introduction of the invited speakers - session chair: Leo Obrst     (1H2B)
    3. Presentation by our invited speakers - Ian Horrocks (~60 min.)     (1H2C)
    4. Q&A and Open discussion (~15 min.) [Kindly identify yourself before speaking.]     (1H2D)
  • Presentation Title: "Scalable Ontology-Based Information Systems"     (1H3)
IanHorricks_20100722a.jpg [ Professor Ian Horrocks ]     (1H4)
Ontologies and ontology based systems are becoming increasingly     (1H6)

important in meeting the demand for more powerful and flexible information systems. Requirements for such systems include the need to deal with incomplete and semi-structured information, to integrate information from heterogeneous sources, to employ richer and more flexible schemas, and for query answers to reflect both knowledge and data. Provision of such enhanced capabilities must, however, be in addition to, and not instead of, the well-established features of existing database systems, in particular their robust scalability. Achieving this is, of course, extremely challenging. In this talk I will present some recent research efforts that tackle this problem, including investigations of tractable fragments, new algorithmic techniques, new optimisations and the exploitation of relational database technology.     (1H7)

Ian Horrocks is a Professor in the Oxford University Computing Laboratory where he jointly (with Georg Gottlob) leads the Information Systems Group. His research interests include knowledge representation, ontologies and ontology languages, modal and description logics, automated reasoning, implementation and optimisation of reasoning systems, and applications in areas such as e-Science and the Semantic Web. He was centrally involved in the development of the OIL, DAML+OIL and OWL ontology languages, and was co-chair of the W3C Working Group that recently developed OWL 2. He also developed algorithms and implementation techniques that are employed in many reasoning systems, and implemented the well known FaCT system in which many of these algorithms and implementations techniques were first deployed. He has published more than 150 articles in conferences, journals and books (see http://www.comlab.ox.ac.uk/people/ian.horrocks/Publications/complete.html), many of which are highly cited, and has given more than 40 keynote and invited talks. He is a BCS Fellow, an ECCAI Fellow, an EPSRC Senior Research Fellow and a past winner of the BCS Roger Needham award.     (1H9)

Transcript of the online chat during the session     (1H10)

see raw transcript here.     (1H10A)

(for better clarity, the version below is a re-organized and lightly edited chat-transcript.)     (1H10B)

Participants are welcome to make light edits to their own contributions as they see fit.     (1H10C)

-- begin of chat session --     (1H10D)

Welcome to the Ontolog Invited Speaker Presentation - Professor Ian Horrocks - Thu 2010.07.22 (2EQX)     (1H10F)

  • Presentation Title: "Scalable Ontology-Based Information Systems" (2F39)     (1H10I)

anonymous morphed into Ian Horrocks     (1H10L)

anonymous morphed into Julita Bermejo-Alonso     (1H10M)

anonymous11 morphed into Mike Hewett     (1H10N)

anonymous1 morphed into Peter Chan     (1H10O)

anonymous2 morphed into Jess Turner     (1H10P)

anonymous3 morphed into Elisa Kendall     (1H10Q)

anonymous3 morphed into Sina Madani     (1H10S)

anonymous6 morphed into Fabian Neuhaus     (1H10T)

anonymous1 morphed into Guoqian Jiang     (1H10U)

anonymous7 morphed into Mikhail Soutchanski     (1H10V)

anonymous1 morphed into Pierluigi Miraglia     (1H10W)

anonymous6 morphed into Amanda Vizedom     (1H10X)

anonymous31 morphed into Bobbin Teegarden     (1H10Y)

anonymous3 morphed into Cui Tao     (1H10Z)

anonymous4 morphed into Peter Wagner     (1H10AA)

anonymous6 morphed into Melanie Courtot     (1H10AB)

anonymous1 morphed into Pavithra Kenjige     (1H10AC)

anonymous7 morphed into Sudarsan Rachuri     (1H10AD)

anonymous4 morphed into Donald Kretz     (1H10AE)

{{{ { ... dog barking in the background ... (laughs) } }}} }}}     (1H10AG)

Joel Bender: The place is going to the dogs!     (1H10AH)

Ian Horrocks: As a DL person I am well used to that sort of response     (1H10AI)

anonymous11 morphed into Mark Johnson     (1H10AJ)

anonymous5 morphed into Peter Bruhn Andersen     (1H10AK)

anonymous1 morphed into Elizabeth Florescu     (1H10AL)

anonymous6 morphed into MyCoyne     (1H10AM)

John Singer: trying to download the pdf, but its going slow     (1H10AN)

Amanda Vizedom: Hello Pierluigi, good to see you here.     (1H10AP)

anonymous6 morphed into Rob Hausam     (1H10AQ)

anonymous7 morphed into Marcia Zeng     (1H10AR)

anonymous3 morphed into Ravi Sharma     (1H10AS)

anonymous5 morphed into Simon Spero     (1H10AU)

Todd Schneider: Peter, Ian is fading in and out.     (1H10AV)

Michael Riben: ok here     (1H10AY)

Clarence Dillon: ok here.     (1H10AZ)

Mike Hewett: Sounds pretty good to me. A little weak. But no fading.     (1H10AAA)

anonymous1 morphed into Lynn Leitte     (1H10AAB)

anonymous4 morphed into Bruce Peoples     (1H10AAC)

anonymous3 morphed into Jae Hyun Lee     (1H10AAD)

Michael Riben: what slide is he on?     (1H10AAE)

Michael Riben: okay..thanks     (1H10AAG)

anonymous1 morphed into Anantha Narayanan     (1H10AAH)

Arturo Sanchez: @Prof. Horrocks: Although the ontologies are not what the systems are supposed to do,     (1H10AAI)

but what the data the systems will use are all about. Do you agree with this?     (1H10AAJ)

Amanda Vizedom: Comment for discussion later, perhaps: Ian, you've just remarked (on slide 12) that     (1H10AAK)

you see the low complexity of DLs as desirable from a performance perspective, and from a     (1H10AAL)

perspective I might paraphrase as tractability of representation (degree to which tiny changes     (1H10AAM)

ripple out in changes not easily anticipated). This may be true on very small, narrow domain,     (1H10AAN)

controlled representation spaces. In my experience, however, the actual result of KR language     (1H10AAO)

simplicity is more complex, and often the opposite. In complex, RW problem-driven representation, a     (1H10AAP)

lower-expressivity language is *at best* like a logical language with only 3 minimal operators. In     (1H10AAQ)

theory, you can represent whatever you need; in practice, doing so may require extraordinarily     (1H10AAR)

complex expressions and mental contortions. In the ontology case, lower expressivity has a     (1H10AAS)

significant cost, increasing with number of collaborators or users and with scale of task. The more     (1H10AAT)

ontologists have to stretch the language extra-ordinarily, the worse performance becomes and the     (1H10AAU)

more problems arise for collaboration and interoperation. Have you considered this cost of     (1H10AAV)

low-expressivity, as in DLs? Have you seen DLs used for very large scale and/or broad domain real     (1H10AAW)

world cases?     (1H10AAX)

Bill Hogan: perhaps it's only an artifact of my medical training, but I don't think of all heart     (1H10AAY)

diseases as being a subtype of vascular disease.     (1H10AAZ)

Frank Chum: There are s lot of business cases for the O&G industry in ontology-based information     (1H10AAAA)

Sudarsan Rachuri: How to we define a notion of quality for ontology?     (1H10AAAC)

Cory Casanave: These are good real world examples. As you said, the real world and our understanding     (1H10AAAD)

is not perfect. Can you speak to the fragility of description logics in the face of a less than     (1H10AAAE)

perfect ontology.     (1H10AAAF)

Frank Chum: Does OWL 2 has Full/Lite DL for complexity and computability?     (1H10AAAG)

Leo Obrst: Ian, along the lines of Amanda's question, some believe that there are 2 problems related     (1H10AAAH)

to KR expressiveness: 1) having a rich enough expressiveness in your modeling/KR language to express     (1H10AAAI)

what you need to for your domain, and 2) having an efficient enough representation to do automated     (1H10AAAJ)

reasoning in near real time. And so folks think that you need: A) a FOL expressive language for     (1H10AAAK)

representing your ontology, B) a knowledge compilation process to transform (A) to (C), probably     (1H10AAAL)

losing information, and C) an efficient runtime representation that enables fast automated     (1H10AAAM)

reasoning. Some say that DLs try to shoehorn the (1) and (2) language into the same language, and so     (1H10AAAN)

there is no B.     (1H10AAAO)

Amanda Vizedom: Another question about the DL expressivity trade-off: How common is it, really, to     (1H10AAAP)

have a sufficiently complex and demanding case that completeness and decidability are going to be     (1H10AAAQ)

serious issues, and yet *not* need to create constraints that terminate queries and inference well     (1H10AAAR)

before the "some finite amount of time" in which decidable systems can guarantee results?     (1H10AAAS)

Todd Schneider: Ian, how do implementations of tableau algorithms make use of logical independence?     (1H10AAAT)

Peter P. Yim: we've skipped from slide 31 to slide 50 ... now at slide#51     (1H10AAAU)

Peter P. Yim: skipping to slide#68     (1H10AAAV)

Peter P. Yim: skipping to slide#72     (1H10AAAW)

Michael Grüninger: What is the citation for the experiments in slide 68? What were the     (1H10AAAX)

queries/reasoning problems? Were all of the ontologies written in EL?     (1H10AAAY)

Peter P. Yim: @Michael, if you can locate the literature Ian referred us to, would you kindly paste it     (1H10AAAZ)

onto this chat board, please     (1H10AAAAA)

Michael Grüninger: @Peter: The citation I was looking for is: Consequence-Driven Reasoning for Horn     (1H10AAAAB)

SHIQ Ontologies by Yevgeny Kazakov ... In Proceedings of the 21st International Conference on     (1H10AAAAC)

Artificial Intelligence (IJCAI 2009). Pages 2040-2045. , 2009.     (1H10AAAAD)

Peter P. Yim: @MichaelGruninger - thank you, Michael     (1H10AAAAE)

Peter P. Yim: skipping to slide#89     (1H10AAAAF)

Leo Obrst: Does saturation include quick computation of subsumption ala Ait-Kaci et al's methods,     (1H10AAAAG)

i.e.,optimal hierarchical encoding of types in bit encodings, prime numbers, etc.?     (1H10AAAAH)

Mike Hewett: What slide is Ian on?     (1H10AAAAJ)

Peter P. Yim: @JimDisbrow ... we are holding off Q&A until the prepared presentation is through ... you     (1H10AAAAL)

can try capturing your question on the chat-board as a placeholder for now     (1H10AAAAM)

anonymous morphed into Jim Disbrow     (1H10AAAAN)

Amanda Vizedom: OWL 2 question for later, unless you answer it anyway before then: Slide #53, the     (1H10AAAAO)

profiles are distinguished by whether they are optimized for size of ontology or size of data sets.     (1H10AAAAP)

But of course, size is not the only major distinction in application contexts. Prior versions of OWL     (1H10AAAAQ)

have proven much more or less usable depending on a variety of other application context     (1H10AAAAR)

characteristics, e.g.: complexity and variety of relationships to be represented; whether use is     (1H10AAAAS)

within a single domain/community or crosses multiple domains/communities; complexity of reasoning     (1H10AAAAT)

required (mostly subsumption or not, for example); whether the ontology is used to represent     (1H10AAAAU)

instance-level data or to represent, at a meta-level, the semantics of data stored elsewhere, or     (1H10AAAAV)

both; whether the ontology must support machine usability, human usability, or both, and so on. Were     (1H10AAAAW)

these and other features of the (increasingly varied!) variety of ontology applications considered     (1H10AAAAX)

in the development of OWL 2? If so, what were some of the effects on the final form of the language?     (1H10AAAAY)

Peter P. Yim: will the "anonymous" participants please click on the "Settings" button (top center) and     (1H10AAAAZ)

morph into your real name, so we can tell who you are, and properly attribute your contributions     (1H10AAAAAA)

anonymous1 morphed into Kathy Ellis     (1H10AAAAAB)

Stavros Macrakis: Is OWL 2 QL expressive enough to handle arbitrary-length chains, e.g. "A is a     (1H10AAAAAC)

direct or indirect ancestor of B"?     (1H10AAAAAD)

Pierluigi Miraglia: Question on QL: is the optimization expected for general inference engines, or     (1H10AAAAAE)

only if reasoning is delegated to RDBMS? I.e., do I see a gain if I use OWL QL in a Pellet or FaCT     (1H10AAAAAF)

based application?     (1H10AAAAAG)

John Singer: how is IBM involved in this?     (1H10AAAAAH)

Arturo Sanchez: @Prof. Horrocks: can you mention classes of problems for which OWL 2 is particularly     (1H10AAAAAI)

good for, and also classes of problems for which OWL 2 is particularly bad for?     (1H10AAAAAJ)

Sudarsan Rachuri: How to we define a notion of quality for ontology?     (1H10AAAAAK)

Amanda Vizedom: Slide #96: Indeed, the infrastructure mentioned regarding privacy and information     (1H10AAAAAL)

hiding is partially overlapping with infrastructure needed for a variety of kinds of KR and     (1H10AAAAAM)

management of ontology content. Provenance issues are important in many interoperability     (1H10AAAAAN)

applications; cross-community uses may also call for considerable capture of knowledge relevant to     (1H10AAAAAO)

dynamically selecting relevant ontology content. Many people have resorted to reification of triples     (1H10AAAAAP)

for this. Others have built complicated containers for pre-defined contexts and ways of moving     (1H10AAAAAQ)

between them. None of that has been great. Does OWL 2 significantly improve on this?     (1H10AAAAAR)

Stavros Macrakis: I have to get off the call -- Pierluigi Miraglia will follow up on my question.     (1H10AAAAAS)

Arturo Sanchez: Great session. Thank you!     (1H10AAAAAU)

Todd Schneider: I also need to leave for another meeting around 3 PM EDT.     (1H10AAAAAV)

Sudarsan Rachuri: Thanks for the excellent presentation. Bye     (1H10AAAAAW)

Amanda Vizedom: Will ask this, but capturing to chat as well: Excellent point about mismatch between     (1H10AAAAAX)

OWL "constraint" behavior and what most people think of as "constraint-checking." Any comment on use     (1H10AAAAAY)

of SPARQL and other query languages to off-load some of these features and requirements to another     (1H10AAAAAZ)

Peter P. Yim: please hold off "OWL 2" specific questions until our two "OWL 2" sessions next Thursday     (1H10AAAAAAB)

and the following one     (1H10AAAAAAC)

Cory Casanave: Follow up on fragility: Your response assumes an ontology under a single authority -     (1H10AAAAAAD)

in a federated knowledge base this is not practical. Can DL be used in an open, federated     (1H10AAAAAAE)

environment like the web.     (1H10AAAAAAF)

Amanda Vizedom: Follow-up to Cory's question: Indeed, I think much of the high payoff work in     (1H10AAAAAAG)

semantic interoperability -- even across communities and data sources within a single, large     (1H10AAAAAAH)

enterprise -- is a federated, not singly-controlled environment. Ontologies can be and have been     (1H10AAAAAAI)

used to enable more and better information sharing in such cases. But this requires considerable     (1H10AAAAAAJ)

richness of representation to make contextual assumptions explicit, as well as considerable     (1H10AAAAAAK)

provenance and other metadata. This is a significant motivation for my earlier questions.     (1H10AAAAAAL)

Cory Casanave: My mike did not seem to work; I am wondering how Ian responds to Benjamin Grosof's     (1H10AAAAAAM)

assertion that a rule system is more capabile in an onology that may have local inconsistancies, and     (1H10AAAAAAN)

that such inconsistencies are inevitable - even in a selected subset of "the web".     (1H10AAAAAAO)

Amanda Vizedom: Strong second to Brian's follow-up about reasoning about provenance. Annotations are     (1H10AAAAAAP)

not sufficient, and one does indeed need to represent relevant provenance details and reason about     (1H10AAAAAAQ)

them as relevant to specific uses or users. The folks creating the data in once source are not going     (1H10AAAAAAR)

to conveniently note that their data doesn't meet the standards of my need. But an intermediate     (1H10AAAAAAS)

infrastructure can reason about whether their data meets my provenance requirements.     (1H10AAAAAAT)

Joel Bender: An outstanding session, thank you!     (1H10AAAAAAU)

Frank Chum: Great session. Thank you!!!     (1H10AAAAAAW)

Amanda Vizedom: Thank you, Prof. Horrocks. I look forward to the next two sessions.     (1H10AAAAAAX)

Peter P. Yim: Great talk ... thank you VERY much, Professor Horrocks     (1H10AAAAAAY)

Peter P. Yim: Thank you all for your participation     (1H10AAAAAAZ)

Peter P. Yim: Look forward to having all of you back here in the next two weeks, when Professor Horrocks     (1H10AAAAAAAA)

will lead the two "OWL 2" sessions, see     (1H10AAAAAAAB)

... drop me a note (rsvp to <peter.yim@cim3.com>) if you are coming     (1H10AAAAAAAE)

and haven't registered for those sessions yet, please     (1H10AAAAAAAF)

Peter P. Yim: @ALL: if what we do at ONTOLOG aligns well with your professional interest, join the     (1H10AAAAAAAG)

community if you are not already a member of Ontolog - see:     (1H10AAAAAAAH)

Ian Horrocks: Thanks to everyone for listening. Look forward to talking to you again next week.     (1H10AAAAAAAJ)

Peter P. Yim: - session ended 12:31pm PDT -     (1H10AAAAAAAK)

-- end of chat session --     (1H10AAAAAAAL)

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