Anyway. So. So there i there are some  some u uh , you know , uh , elaborations of this that you could try to put in to this structure , but I don't think it 's worth it now. Because we 're gonna see what  what else uh  what else we 're gonna do. Anyway. But uh , it 's good , yeah and  and there were a couple other ideas of  of uh , things for Eva to look at in  in the interim.
Good. Then , we can move on and see what Andreas has got out his sleeve. Or Andy , for that matter ?
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.
And so some of the  I don't know how much you know about the larger Heidelberg project , I  Are you