F: to actually take the data , and do it .
F: Uh , and we 'll see .
F: Uh , I actually think it is cleaner ,
F: and the ability to instantiate , you know , instance of people and sites and stuff , um , will help in the expression .
F: Whether the inference gets any faster or not I don't know .
F: Uh , it wouldn't surprise me if it if it doesn't .
D: Mm - hmm .
F: You know , it 's the same kind of information .
F: I think there are things that you can express this way which you can't express in a normal belief - net ,
F: uh , without going to some incredible hacking
F: of sort of rebuilding it on the fly .
F: I mean , the notion of instantiating your el elements from the ontology and stuff fits this very nicely and doesn't fit very well into the extended belief - net .
F: So that was one of the main reasons for doing it .
F: Um .
F: I don't know .
F: So , uh , people who have thought about the problem , like Robert
F: i it looked to me like if <comment> Eva were able to come up with a you know , value for each of a number of uh , sites plus its EVA thing ,
F: that a travel planner should be able to take it from there .
F: And you know , with some other information about how much time the person has and whatever ,
F: and then plan a route .
B: Um - hmm , um , well , first of all uh , uh , great looks , mu much cleaner ,
B: nnn , nnn ,
B: Certain certain beauty in it ,
B: so , um , if beauty is truth , then , uh we 're in good shape .
B: But ,
B: the um ,
B: as , uh , mentioned before we probably should look at t the details .
B: So if you have a write - up then uh , I 'd love to read it
D: Mm - hmm .
B: and uh B: because , um ,
B: i Can you go all the way back to the the very top ?
D: Yeah .
B: Um , uh these @ @ <comment> these w w when these are instantiated they take on the same values ? that we had before ?
D: I can't really see the whole thing .
B: or are they have they changed , in a sense ?
D: Well I think I basically leave them to similar things .
B: Uh - huh .
D: Some of the things might that might be different , maybe like are that the hours for the site .
B: Hmm .
D: And , eventually I meant that to mean whether they 're open at this hour or not .
D: And status would be , you know , more or less like , whether they 're under construction ,
B: Uh - huh .
D: and and or stuff like that .
B: And the , uh , other question I would have is that presumably , from the way the Stanford people talk about it , you can put the probabilities also on the relations .
B: If D: Which is the structural uncertainty ?
F: Yeah .
F: Yeah , I that 's F: That I think was actually in the previous the Ubenth stuff .
F: I don't remember whether they carried that over to this or not ,
A: Mmm .
F: uh , structural uncertainty .
B: It 's sort of in the definition or in the in Daphne 's definition of a PRM
B: is that classes and relations ,
F: OK .
B: and you 're gonna have CPT 's over the classes and their relations .
F: Alright .
B: More uncertainty , or or F: Uh ,
D: I remember them learning when , you know , you don't know the structure for sure ,
B: I should say .
F: Yeah .
D: but I don't remember reading how you specify
F: Right .
B: Yeah , that would be exactly my question .
D: wh to start with .
D: Yeah .
B: Well F: Yeah .
D: Yeah .
F: So , uh , the the plan is is when Daphne gets back , we 'll get in touch
F: and supposedly , um , we 'll actually get s deep seriously connected to to their work
F: and
B: Yep .
F: somebody 'll Uh , you know If it 's a group meeting once a week probably someone 'll go down and , whatever .
F: So , we 'll actually figure all this out .
B: OK .
B: OK .
B: Then I think the w long term perspective is is pretty clear .
B: We get rocking and rolling on this again , once we get a package ,
B: if , when , and how ,
B: then this becomes foregrounded
D: Mm - hmm .
B: profiled ,
B: focused , again .
E: Designated ?
A: Of course .
B: And um ,
B: until then we 'll come up with a something that 's @ @ <comment> that 's way more complicated for you .
B: Right ?
D: OK .
B: Because this was laughingly easy ,
B: right ?
D: Actually I had to take out a lot of the complicated stuff ,
D: cuz I I made it really complicated in the beginning ,
D: and Jerry was like , " this is just too much " .
F: Yeah . So , um ,
F: you could , from this , go on and say suppose there 's a group of people traveling together
F: and you wanted to plan something that somehow , with some Pareto optimal uh , uh , thing for A: That 's good .
A: That 's definitely a job for artificial intelligence .
F: uh , or B: Well that 's not not even something humans A: Except for humans can't really solve it either , so .
B: yeah .
F: Right .
F: Right .
F: Well that 's the that would that would be a uh , you could sell it , as a A: Yeah .
F: OK , eh you don't have to fight about this , just give your preferences to the A: And then you can blame the computer .
F: w Exactly .
B: Hmm .
A: So .
B: But what does it uh Would a pote potential result be to to split up and never talk to each other again ?
B: You know .
A: That should be one of them .
B: Yeah .
F: Yeah . Right .
E: That 'd be nice .
A: Mmm .
F: Anyway .
F: So . So there i there are some some u uh , you know , uh , elaborations of this
F: that you could try to put in to this structure ,
F: but I don't think it 's worth it now .
F: Because we 're gonna see what what else uh what else we 're gonna do .
F: Anyway .
F: But uh ,
F: it 's good , yeah
F: and and there were a couple other ideas of of uh , things for Eva to look at
F: in in the interim .
B: Good . Then , we can move on and see what Andreas has got out his sleeve .
B: Or Andy , for that matter ?
C: OK . So uh , uh , well , thanks for having me here , first of all .
C: Um , so maybe just a a little background on on my visit .
C: So , uh , I 'm not really involved in any project , that 's uh that 's relevant to you uh , a at the moment ,
C: uh , the the reason is really for me uh , to have an opportunity to talk to some other researchers in the field .
C: And and so I 'll just n sort of give you a real quick introduction to what I 'm working on ,
C: and um , I just hope that you have some comments
C: or , maybe you 're interested in it to find out more ,
C: and and so I 'll be uh , happy to talk to you
C: and and uh , I 'd also like to find out some more
C: and and maybe I 'll just walk around the office
C: and
C: and then and ask some some questions , uh , in a couple days .
C: So I 'll be here for uh , tomorrow and then uh , the remainder of uh , next week .
C: OK , so , um , what I started looking at , uh , to begin with is just uh , content management systems uh , i i in general .
C: So
C: um , uh what 's uh Sort of the state of the art there is to um uh you have a bunch of of uh documents or learning units or learning objects ,
C: um , and you store meta - data uh , associate to them .
C: So there 's some international standards
C: like the I - triple - E , uh There 's an I - triple - E , LON standard ,
C: and um , these fields are pretty straightforward , you have uh author information , you have uh , size information , format information and so on .
C: Uh , but they 're two uh fields that are um , more interesting .
C: One is uh you store keywords associated with the uh with the document ,
C: and one is uh , you have sort of a , um ,
C: well , what is the document about ? So it 's some sort of taxonomic uh , ordering of of the of the units .
C: Now , if you sort of put on your semantic glasses , uh you say , well that 's not all that easy ,
C: because there 's an implicit um , uh , assumption behind that
C: is that uh , all the users of this system share the same interpretation of the keyword and the same interpretation of uh , whichever taxonomy is used ,
C: and uh ,
C: I think that 's a that 's a very that 's a key point of these systems
C: and they sort of always brush over this real quickly without really elaborating much of that
C: and uh As a matter of fact , the only thing that m apparently really works out so far are library ordering codes ,
C: which are very , very coarse grain ,
C: so you have some like , science , biology , and then C: But that 's really all that we have at the moment .
C: So I think there 's a huge , um , uh need for improvement there .
C: Now , what this uh a standard like this would give us is we could um , sort of uh with a search engine just query uh , different repositories all over the world .
C: But we can't really C: Um , so what I 'm what I try to do is um , to have um ,
C: uh So . So the scenario is the following , you you 're working on some sort of project and you encounter a certain problem .
C: Now , what what we have at our university quite a bit is that uh , students um , try to u program a certain assignment , for example ,
C: they always run into the same problems ,
C: uh , and they always come running to us ,
C: and they 'll say why 's it not it 's not working ,
C: and we always give out the same answer ,
C: so we thought , well , it 'd be nice to have a system that could sort of take care of this ,
C: and so , what I want to build is basically a a smart F A Q system .
C: Now , what you uh need to do here is you need to provide some context information
C: which is more elaborate than " I 'm looking for this and this and this keyword . "
C: So .
C: And I think that I don't need to tell you this .
C: I 'm I 'm sure you have the same when when somebody utters a sentence in a certain , uh , context
C: it , and and the same sentence in another context makes a huge difference .
C: So , I want to be able to model information like , um , so in the in the context of in the context of developing distributed systems , of a at a computer science school ,
C: um , what kind of software is the person using ,
C: which homework assignment is he or she working on at the moment ,
C: um , maybe what 's the background of that student 's
C: um , which um , which error message was encountered .
C: So this sort of information I think should be transmitted , uh , when a certain document is retrieved .
C: Now ,
C: um ,
C: basically giving this um Uh
C: so we somehow need to have a formalized um , way of writing this down basically ,
C: and that 's where the shared interpretation of of certain terms and keywords comes in again .
C: And , using this and some some uh , knowledge about the domain I think you can do some some simple inferences .
C: Like you know that when somebody 's working about uh , working on on servlets for example ,
C: he 's using Java , cuz servlets are used are written in Java .
C: So some some inferences like that ,
C: now , um , u using this you can infer more information ,
C: and you could then match this to the meta - data of um off the documents you 're you 're searching against .
C: So , uh what I wanna do is basically have some sort of um given these inputs ,
C: and then I can compute how many documents match ,
C: and use this as a metric in the search .
C: Now , what I plan to do is I want to uh sort of do a uh uh try to improve the quality of the search results ,
C: and I want to do this by having a depth uh , um , um steepest descent approach .
C: So if I knew which operating system the person was working on , would this improve my search result ?
C: And and having uh , uh a symbolic formalized model of this I could simply compute that ,
C: and find out which um which questions are worth um , asking .
C: And that 's what I then propagate back to the user ,
C: and and sort of try to optimize the search in this way .
C: Now , the big problem that I 'm facing right now is um , it 's fairly easy to hack up a system uh quickly , that that works in the small domain ,
C: but the problem is obviously the scalability .
C: And uh uh , so Robert was mentioning uh , earlier today is that uh , Microsoft for example with their printer set up program has a Bayesian network ,
C: which does exactly this ,
C: but there you face a problem that these are very hard to extend .
C: And so , uh what I 'm What I try to do is basically try to model this uh , in a way that you could really combine uh , knowledge from very different sources ,
C: and and um , sort of looking into some of the ideas that the semantic web community uh , came up with .
C: Trying to to have uh , an approach how to integrate s uh certain uh representation of certain concepts and also some computational rules ,
C: um , what you can do with those .
C: Um .
C: What I 'm also looking into is a probabilistic approach into this
C: because document retrievals is a very fuzzy procedure ,
C: so it 's probably not that easy to simply have a symbolic uh , computational model .
C: That that probably isn't expressive enough .
C: So . So that 's another thing ,
C: um , which I think you 're also uh , uh looking into right now .
C: And then um , uh sort of as an add - on to this whole idea , um , uh that would be
C: now , depending on what the search engine or the content repository depending on which um , uh , which uh , rules and which ontologies it it uses , or basically its view of the world , uh you can get very different results .
C: So it might ma make a lot of sense to actually query a lot of different search engines .
C: And there you could have an idea where you actually have sort of a a peer to peer approach ,
C: where we 're all sort of carrying around our individual bookshelves ,
C: and um , if you have a question about a homework , it 's probably makes sense to ask somebody who 's in your class with you , sort of the guru in the certain area ,
C: rather than going to some Yahoo - like uh , search engine .
C: So these are some of the just in a nutshell , some of the ideas .
C: And I think a lot of the even though it 's a it 's a very different domain , but I think a lot of the , um , issues are are fairly similar .
C: So .
C: OK .
A: And so some of the I don't know how much you know about the larger Heidelberg project ,
A: I Are you C: Uh I know , yeah I know abou about it .
A: So it seems like a lot of some of the issues are the same .
A: It 's like , um , you know , the c context - based factors that influence how you interpret ,
C: Mm - hmm . Mm - hmm .
A: um , s how to interpret .
A: In in this case , infer in in knowing wanting to know what kinds of things to ask .
A: We - we 've kind of talked about that ,
A: but we haven't worried too much about that end of the discourse .
B: Mm - hmm .
A: But maybe you guys had that in the previous models .
B: Well , in a in one t one s mmm , small difference in a in a way , is that he doesn't have to come up with an answer ,
B: but he wants to point to the places w w
C: Yeah , so . So I 'm I 'm not I 'm not building an expert A: Documents that have the answers .
C: Uh , I want to build a smart librarian , basically
A: Mm - hmm .
A: Right .
C: that can point you to the right reference .
A: Right .
C: I don't wanna compute the answer ,
C: so it 's a little bit easier for me .
B: Well . Uh , you have to s still m understand what the content says about itself , and then match it to what you think the informational needs A: Mm - hmm .
C: Mm - hmm .
A: So you also don't have to figure out what the content is .
A: You 're just taking the keywords as a topic text ,
C: I I assume that that the there will be learning systems that that tag their their content .
A: as A: OK .
C: And um , um ,
A: Right .
C: m @ @ and basically what I what I envision is that you rather than just supplying a bunch of keywords you could basically for for an FAQ for example you could state sort of like a logic condition , when this document applies .
C: So " this document explains how to set up your uh , mail account on Linux " or something like this .
A: Mm - hmm .
C: So . So something something very specific that you can then C: But the I think that the key point with these uh , learning systems is that uh , a learning system is only as good as uh the amount of content it it carries .
A: Mmm ,
A: mm - hmm .
C: You can have the best learning system with the best search interface ,
C: if there 's no content inside of it , it 's not very useful .
C: So I think ultimately because um , uh developing these these rules and these inference uh inferences I think is very costly ,
C: so um , uh I think you must be able to reuse some some existing um , domain domain information , or or or ontologies that that uh other people wrote
C: and then try to integrate them ,
C: and then also search the entire web basically , rather than just the small uh , content management system .
A: OK .
A: Mm - hmm .
C: So I think that 's that 's crucial for for the success of or @ @ A: So , you 're not A: I guess I 'm trying to figure out how how it maps to the kinds of things that we 've talked about in this group , and , actually associated groups ,
C: Mm - hmm .
A: cuz some of us do pretty detailed linguistic analyses ,
A: and I 'm guessing that you you won't be doing that ?
C: No .
A: OK .
A: Just checking .
A: So , OK .
B: Hmm .
C: No .
A: So , you take the query , and and F: On the other hand , uh , FrameNet could well be useful .
F: So do you know the FrameNet story ?
C: Um , yeah .
C: Uh , not not too much ,
F: OK .
C: but uh ,
F: Oh . Th - that 's another thing you might wanna look into while you 're here .
C: I have a rough overview .
F: Because , um , you know , the standard story is that keyworks keywords evoke frames , and the frames may well give you additional keywords
F: or uh , if you know that that that a a bunch of keywords uh , indicate a frame , then you can find documents that actually have the whole frame , rather th than just uh , individual C: Mmm . Mmm .
F: So there 's a lot of stuff ,
F: and people are looking at that .
F: Most of the work here is just trying to get the frames right .
F: There 's linguists and stuff
F: and there 's a lot of it
F: and they 're they 're busily working away .
F: But there are some application efforts trying to exploit it .
F: And this looks t it seems to be that this is a place where you might be able to do that .
C: Yeah . Yeah .
C: Yeah . I 'm sure I could learn a lot about um , yeah , just how to how to come up with these structures ,
A: Mmm .
C: cuz it 's it 's very easy to whip up something quickly ,
C: but it maybe then makes sense to to me , but not to anybody else ,
C: and and if we want to share and integrate things , they must well , they must be well designed really .
B: Remember the uh , Prashant story ?
F: Right .
B: The absolutely no no linguistic background person that the IU sent over here .