Grad C: OK . So uh , uh , well , thanks for having me here , first of all . Um , so maybe just a a little background on on my visit . So , uh , I 'm not really involved in any project , that 's uh that 's relevant to you uh , a at the moment , uh , the the reason is really for me uh , to have an opportunity to talk to some other researchers in the field . And and so I 'll just n sort of give you a real quick introduction to what I 'm working on , and um , I just hope that you have some comments or , maybe you 're interested in it to find out more , and and so I 'll be uh , happy to talk to you and and uh , I 'd also like to find out some more and and maybe I 'll just walk around the office and and then and ask some some questions , uh , in a couple days . So I 'll be here for uh , tomorrow and then uh , the remainder of uh , next week . OK , so , um , what I started looking at , uh , to begin with is just uh , content management systems uh , i i in general . So 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 , um , and you store meta - data uh , associate to them . So there 's some international standards like the I - triple - E , uh There 's an I - triple - E , LON standard , and um , these fields are pretty straightforward , you have uh author information , you have uh , size information , format information and so on . Uh , but they 're two uh fields that are um , more interesting . One is uh you store keywords associated with the uh with the document , and one is uh , you have sort of a , um , well , what is the document about ? So it 's some sort of taxonomic uh , ordering of of the of the units . Now , if you sort of put on your semantic glasses , uh you say , well that 's not all that easy , because there 's an implicit um , uh , assumption behind that 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 , and uh , I think that 's a that 's a very that 's a key point of these systems and they sort of always brush over this real quickly without really elaborating much of that and uh As a matter of fact , the only thing that m apparently really works out so far are library ordering codes , which are very , very coarse grain , so you have some like , science , biology , and then But that 's really all that we have at the moment . So I think there 's a huge , um , uh need for improvement there . 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 . But we can't really Um , so what I 'm what I try to do is um , to have um , uh So . So the scenario is the following , you you 're working on some sort of project and you encounter a certain problem . 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 , they always run into the same problems , uh , and they always come running to us , and they 'll say why 's it not it 's not working , and we always give out the same answer , so we thought , well , it 'd be nice to have a system that could sort of take care of this , and so , what I want to build is basically a a smart F A Q system . Now , what you uh need to do here is you need to provide some context information which is more elaborate than " I 'm looking for this and this and this keyword . " So . And I think that I don't need to tell you this . I 'm I 'm sure you have the same when when somebody utters a sentence in a certain , uh , context it , and and the same sentence in another context makes a huge difference . 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 , um , what kind of software is the person using , which homework assignment is he or she working on at the moment , um , maybe what 's the background of that student 's um , which um , which error message was encountered . So this sort of information I think should be transmitted , uh , when a certain document is retrieved . Now , um , basically giving this um Uh so we somehow need to have a formalized um , way of writing this down basically , and that 's where the shared interpretation of of certain terms and keywords comes in again . And , using this and some some uh , knowledge about the domain I think you can do some some simple inferences . Like you know that when somebody 's working about uh , working on on servlets for example , he 's using Java , cuz servlets are used are written in Java . So some some inferences like that , now , um , u using this you can infer more information , and you could then match this to the meta - data of um off the documents you 're you 're searching against . So , uh what I wanna do is basically have some sort of um given these inputs , and then I can compute how many documents match , and use this as a metric in the search . 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 , and I want to do this by having a depth uh , um , um steepest descent approach . So if I knew which operating system the person was working on , would this improve my search result ? And and having uh , uh a symbolic formalized model of this I could simply compute that , and find out which um which questions are worth um , asking . And that 's what I then propagate back to the user , and and sort of try to optimize the search in this way . 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 , but the problem is obviously the scalability . 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 , which does exactly this , but there you face a problem that these are very hard to extend . 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 , and and um , sort of looking into some of the ideas that the semantic web community uh , came up with . Trying to to have uh , an approach how to integrate s uh certain uh representation of certain concepts and also some computational rules , um , what you can do with those . Um . What I 'm also looking into is a probabilistic approach into this because document retrievals is a very fuzzy procedure , so it 's probably not that easy to simply have a symbolic uh , computational model . That that probably isn't expressive enough . So . So that 's another thing , um , which I think you 're also uh , uh looking into right now . And then um , uh sort of as an add - on to this whole idea , um , uh that would be 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 . So it might ma make a lot of sense to actually query a lot of different search engines . And there you could have an idea where you actually have sort of a a peer to peer approach , where we 're all sort of carrying around our individual bookshelves , 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 , rather than going to some Yahoo - like uh , search engine . So these are some of the just in a nutshell , some of the ideas . 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 . So . OK .
Grad A: And so some of the I don't know how much you know about the larger Heidelberg project , I Are you Grad C: Uh I know , yeah I know abou about it .
Grad A: So it seems like a lot of some of the issues are the same . It 's like , um , you know , the c context - based factors that influence how you interpret ,
Grad C: Mm - hmm . Mm - hmm .
Grad A: um , s how to interpret . In in this case , infer in in knowing wanting to know what kinds of things to ask . We - we 've kind of talked about that , but we haven't worried too much about that end of the discourse .
Grad B: Mm - hmm .
Grad A: But maybe you guys had that in the previous models .
Grad 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 , but he wants to point to the places w w
Grad A: Documents that have the answers .
Grad C: Yeah , so . So I 'm I 'm not I 'm not building an expert Grad A: Mm - hmm .
Grad C: Uh , I want to build a smart librarian , basically
Grad A: Right . Right .
Grad C: that can point you to the right reference . I don't wanna compute the answer , so it 's a little bit easier for me .
Grad 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 Grad A: Mm - hmm .
Grad C: Mm - hmm .
Grad A: So you also don't have to figure out what the content is . You 're just taking the keywords as a topic text , as Grad C: I I assume that that the there will be learning systems that that tag their their content .
Grad A: OK . Right .
Grad C: And um , um , 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 . So " this document explains how to set up your uh , mail account on Linux " or something like this .
Grad A: Mm - hmm .
Grad C: So . So something something very specific that you can then 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 .
Grad A: Mmm , mm - hmm .
Grad C: You can have the best learning system with the best search interface , if there 's no content inside of it , it 's not very useful . So I think ultimately because um , uh developing these these rules and these inference uh inferences I think is very costly , 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 and then try to integrate them , and then also search the entire web basically , rather than just the small uh , content management system .
Grad A: OK . Mm - hmm .
Grad C: So I think that 's that 's crucial for for the success of or @ @ Grad A: So , you 're not 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 ,
Grad C: Mm - hmm .
Grad A: cuz some of us do pretty detailed linguistic analyses , and I 'm guessing that you you won't be doing that ? OK .
Grad C: No .
Grad A: Just checking . So , OK .
Grad B: Hmm .
Grad C: No .
Grad A: So , you take the query , and and Professor F: On the other hand , uh , FrameNet could well be useful . So do you know the FrameNet story ?
Grad C: Um , yeah . Uh , not not too much ,
Professor F: OK .
Grad C: but uh ,
Professor F: Oh . Th - that 's another thing you might wanna look into while you 're here .
Grad C: I have a rough overview .
Professor F: Because , um , you know , the standard story is that keyworks keywords evoke frames , and the frames may well give you additional keywords 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 Grad C: Mmm . Mmm .
Professor F: So there 's a lot of stuff , and people are looking at that . Most of the work here is just trying to get the frames right . There 's linguists and stuff and there 's a lot of it and they 're they 're busily working away . But there are some application efforts trying to exploit it . And this looks t it seems to be that this is a place where you might be able to do that .
Grad C: Yeah . Yeah . Yeah . I 'm sure I could learn a lot about um , yeah , just how to how to come up with these structures ,
Grad A: Mmm .
Grad C: cuz it 's it 's very easy to whip up something quickly , but it maybe then makes sense to to me , but not to anybody else , and and if we want to share and integrate things , they must well , they must be well designed really .
Grad B: Remember the uh , Prashant story ?
Professor F: Right .
Grad B: The absolutely no no linguistic background person that the IU sent over here .
Professor F: Right .
Grad B: And Andreas and I tried to come up wi or we had come up actually with a eh with him working on an interface for FrameNet , as it was back then , that would p do some of the work for this machine ,
Professor F: Right . Yeah .
Grad B: which uh , never got done because Prashant found a happy occupation
Professor F: W yeah , I know , I mean it it he w he did w what what he did was much more s sensible for him .
Grad B: which in the Absolutely . Yeah .
Professor F: I think uh ,
Grad B: But so I 'm just saying , the uh , we had that idea
Professor F: you know Yeah . The idea was there . Yeah , OK .
Grad B: uh to to exploit FrameNet there as well .
Professor F: Yeah .
Grad A: Hmm .
Grad B: And um .
Professor F: Yeah , actually you guys never Grad B: And Srini 's doing information extraction also , right ?
Professor F: Right .
Grad B: with that FrameNet base .
Grad C: Mmm .
Professor F: Yeah .
Grad B: Mm - hmm .
Professor F: So you you guys never sent anybody else from I U .
Grad C: Except except Prashant ?
Professor F: You were y no Yeah .
Grad C: Um ,
Professor F: Uh , this was supposedly an exchange program , and I we you know , it 's fine . We don't care , but it just I 'm a little surprised that uh , Andreas didn't come up with anyone else he wanted to send .
Grad A: Hmm .
Grad C: Uh I don't know , I mean the uh Professor F: Alright . I mean I had forgotten a I To be honest with you , I 'd totally forgotten we had a program .
Grad B: Uh it 's in the program ?
Grad C: Uh I I think it 's it 's really the lack of students uh , at IU at the moment .
Professor F: Yeah . Yeah . No , no . There was a whole co There was a little contract signed . It was Yeah .
Grad C: Yeah , yeah . I think it 's ju it 's more the lack of of students , really , and w we have all these sponsors that are always sort of eager to get some teams .
Professor F: Yeah , I know .
Grad A: Mmm .
Professor F: Right .
Grad C: But Professor F: Right .
Grad C: Well I mean if if I were a student , I 'd love to come here , rather than work for some German {nonvocalsound} company , or Professor F: Yeah . Right .
Grad B: You are being recorded right now , so beware .
Professor F: Oh , right !
Grad C: Well , I didn't say anybody to anything to offend well , except for the sponsors maybe , but Professor F: Right . Anyway . Right . So I thi tha that 's that 's one of the things that might be worth looking into while you 're here .
Grad C: Mm - hmm .
Professor F: Uh , unfortunately , Srini , who is heavily involved in DAML and all this sort of stuff is himself out of town .
Grad C: Mm - hmm . Well I 'll go to the uh , Semantic Web Workshop , uh , in two weeks .
Professor F: Right , and Yeah , for for some reason he 's not doing that .
Grad A: Yeah . Well , he had other things to do .
Professor F: I don't know why he @ @ oh , I , who knows ?
Grad A: The uh Professor F: Anyway , s yeah , you 'll see you 'll certainly see a lot of the people there .
Grad A: The other person I thought of is Dan Gildea ? because he did some work on topic spotting
Professor F: Yeah . St - statistical stuff . That would be a very good idea .
Grad A: w um , which is , I mean , you I mean . I don't Depending on how well you wanna integrate with that end ,
Grad C: Mm - hmm .
Grad A: you know , like , taking the data and fig you said the learning systems that figure out We There 's someone in ICSI who actually has been working on has worked on that kinda stuff , and he 's worked with frame net , so you could talk to him about , you know , both of those things at once .
Grad C: Mm - hmm . Mm - hmm .
Grad A: So . And he just finished writing a draft of his thesis . So . I u Dan Gildea , GILDEA .
Grad C: So , uh , who is that again ?
Grad A: And , he 's in one of the rooms on the fifth floor and stuff ,
Grad B: Who ? I can take you to his office .
Grad A: and Grad B: It 's just around the corner .
Grad C: OK , great .
Grad A: Hmm . Well , if you fal solve the problem , hope you can do one for us too .
Professor F: Alright , was there anything else for this ? One of these times soon we 're gonna hear about construal .
Grad B: Yeah . I 'm sure . I have um I think it was November two thousand three or some No . Wh - I had something in my calendar .
Professor F: Oh , OK . Right .
Grad B: Um ,
Grad E: Wait a second . That 's a long way away .
Professor F: Good thinking !
Grad B: Uh well , maybe I can I can bribe my way out of this . So . So I did some double checking and it seems like spring break in two thousand one .
Grad A: Talk about changing the topic .
Grad B: No .
Professor F: Well , no , but he 's he 's he 's he 's as you said , he 's , like the state legislature , he 's trying to offer us bribes .
Grad A: At least this is a private meeting . Right , exactly , OK , that 's the link .
Grad B: This uh Oh , they refused the budget again ? Is it so about CITRIS ? Yeah , still nothing .
Professor F: Uh , this this this t the s we 're , uh , involved in a literally three hundred million dollar uh , program . Uh , with the State of California . And , the State of California is now a month and a half behind its legis its legally required date to approve a budget . So the budget has not been approved . And two days ago There 's two l you know , so , two branches of legislature . One branch approved it ,
Grad C: Mm - hmm .
Professor F: and , um , yesterdayday {comment} there was this uh uh I thought that the other branch would just approve it , but now there 's actually a little back sliding to people who who approved it got flak from there , eh anyway . So , um Oh ! I have to tell you a wonderful story about this , OK ? And then we 'll go . So , I it turns out I wound up having lunch today with a guy named Tom Kalil . KILL KALIL . And , uh , he now works at Berkeley . In fact he 's hired to run a lot of CITRIS , even though we don't have the money they So they 've been hiring people right and left , so , uh , they think the money 's coming . So and he was , I think , the chief staffer to Clinton on technology matters . He was in the White House , I don't remember what he was saying . A anyway , like that . And , is now doing all the politics for CITRIS , but also , has a uh , a lot of interest in uh , actually doing things for society , so digital divide and stuff like that . So that 's s interesting to me but maybe not to you . But the really interesting thing was , he st he s he s said something about , you know I 'm interested in things that have high social multiplier , something that is of great social value . He said , " for example " , this was his only example , " if you had a adult literacy program that was as good as an individual tutor , and as compelling as a video game , then that would have a huge social impact " . I said , " Oh great ! That 's a good problem to work on . " Anyway . So it was nice that uh , he 's got this view , of A , that 's what you should try to do , and B , uh , language would be a good way to do it .
Grad A: Mmm . Definitely .
Professor F: So that 's So anyway , that 's the end of the story .
Grad A: But for adults and not for the children .
Professor F: This was Yeah . I didn't push him on the ch on the child thing ,
Grad A: Uh - huh .
Professor F: but , uh , you know , a again , if if you if you
Grad A: Oh .
Professor F: um , and this was this was literacy , which actually is somewhat different problem .
Grad A: Mm - hmm .
Professor F: Maybe easier . I don't know . So this is reading , rather than teaching Another project we started on , and and didn't get funded for was , uh , to try to build an automatic tutoring program , for kids whose first language wasn't English . Which is like half the school population in California . Something like that ,
Grad A: Mm - hmm .
Professor F: isn't it ? Yeah . So , enormous problem in California , and the idea was if we 're so smart about language understanding and speech understanding , couldn't we build uh , programs that would be tutors for the kids . We think we could . Anyway . So so But this is a slightly different problem ,
Grad A: Mm - hmm .
Professor F: and um , I know none of us have the spare time to look at it right now , but it i it 's it 's interesting and I may um , talk to him some more about is em somebody already doing this , and stuff like that . So anyway , that was that was today 's little story .
Grad E: Hmm .
Grad B: OK . So I I did manage to get pull my head out of the sling by sidetracking into CITRIS ,
Professor F: No , no .
Grad B: but uh or a temporarily putting it out of the sling
Professor F: Right .
Grad B: but , I I 'll volunteer to put it right back in by stating that I am n uh among some other things in the process of writing up stuff that we have been discussing at our daily meetings ,
Professor F: Yeah .
Grad B: and also revising , thanks for all the comments , the c the original construal proposal . And , if I put one and one together , I may end up with a number that 's greater than one and that I I can potentially present once you get back .
Grad A: Greater than two ?
Professor F: You 're good .
Grad B: Nnn . {comment} s sometimes , you know the sum is not uh less than the Grad A: Uh , right , right .
Professor F: Right . Right . Anyway . Yeah , so OK , so that 'd be great , but I 'd I think it 's it 's time again , right ?
Grad B: Absolutely . Yeah .
Professor F: Yeah . OK .
Grad B: But um , and hopefully all sidetracking um , other things will have disappeared , soon .
Professor F: Good . Yep . Done ?