%0 Book %1 fairchild88 %A Fairchild, K. %A Poltrock, S. %A Furnas, G. %B Cognitive Science and its Applications for Hu %D 1988 %I Lawrence Erlbaum Associates %K information visualization %P 201--233 %T SemNet: Three-dimensional graphic representations of large knowledge bases %U http://www.cs.toronto.edu/~nernst/papers/SEMNET.txt

@book{fairchild88, added-at = {2006-03-24T16:34:33.000+0100}, author = {Fairchild, K. and Poltrock, S. and Furnas, G.}, biburl = {https://www.bibsonomy.org/bibtex/2cdd64947b6f9ab3fd217e6e70d72e2db/neilernst}, citeulike-article-id = {111821}, comment = {papers/SEMNET.txt --- SemNet basically addresses everything Shrimp does only does it 10 years before (a lifetime in this industry!). So in essence we are only expanding on ideas that the SemNet system and its counterparts were positing. This begs the question as to what is actually original. So we use Java, so what. What are we doing that's special? We must drive to innovation at every opportunity. symbolic knowledge vs. numeric knowledge p201 - back then, there was a difference, now I believe even the numeric data have meaning or semantics. A negative number for income may associate with declining stock price for instance. Previously in the 80s you'd get "98982" and it would be meaningless but now we can markup this with metadata such as <rdfs:Type=income year=1999>98982</rdfs:Type> 3 issues in navigating large info spaces: 1. user recognition of identities of individual elements in KB 2. relative positions of elements in the network context 3. explicit relationships between elements Exploit skills people have in spatial cognition and 3-d space p 204 "3-d graphic representations of KBs... can help reveal the organization of the KB. However, graphic representations do not automatically solve the problems associated with exploring, manipulating, and modifying very large KBs. They simply transform very large KBs into very large directed graphs." p206 idea of two modes: editing and browsing ** task models and user models ** Shook MCC tech report, Formal eval.of SemNet. in 1986 p207 the reason for choosing 3d - arbitrary graph can be drawn such that no 2 arcs intersect problem: how to layout the elements in a meaningful manner - differs for each KB. Layout knowledge may reside with the user. 1. mapping fns: choose some variables char of all elements (size, predacity and domesticity for example) and position accordingly. Allow user to select the dimensions. 2. proximity fns: spring etc but these are slow. 2b. k-means choose a random centroid and group connected elements 2c. annealling with a random vector 3. let the user specify certain anchor elements based on specific knowledge p218 coping with large KBs - filtering, fisheyes, clustering, 3D fisheye (perspective reduces elements "farther' away p222 browsing the large KB - idea of navigational landmarks as user browses more and more, certain structure becomes familiar (the mental map), cookie crumbs, hyperspace, teleporting, p231 - a KB can never hope to solve all problems since they depend on the application and the tasks that are to be performed.}, description = {sdasda}, interhash = {87920d36b4f0ff0470cd52db50addf71}, intrahash = {cdd64947b6f9ab3fd217e6e70d72e2db}, keywords = {information visualization}, pages = {201--233}, priority = {0}, publisher = {Lawrence Erlbaum Associates}, series = {Cognitive Science and its Applications for Hu}, timestamp = {2006-03-24T16:34:33.000+0100}, title = {Sem{N}et: {T}hree-dimensional graphic representations of large knowledge bases}, url = {http://www.cs.toronto.edu/~nernst/papers/SEMNET.txt}, year = 1988 }