%0 Conference Paper %1 kremer98 %A Kremer, Ross %B 11th Workshop on Knowledge Acquisition, Modelling, and Management (KAW '98) %C Banff, Alberta %D 1998 %K knowledge visual languages acquisition %T Visual Languages for Knowledge Representation %U http://pages.cpsc.ucalgary.ca/~kremer/papers/KAW98/visual/kremer-visual.html %V Olympus C-4000 %X Visual languages of many types are used in many disciplines for many purposes. The use of visual languages is compelling for many reasons, not the least of which is that their graphical nature can act as an analogical representation of the actual domain in a way that is not possible with purely textual systems. Concept mapping languages form a large family of visual languages. Concept maps consist of labeled, typed nodes and typed arcs, and may also include contexts that partition the map. Concept maps appear to be particularly useful for knowledge representation; examples include Peirce's Existential Graphs, Sowa's Conceptual Graphs, and Gaines' KRS.

@inproceedings{kremer98, abstract = {Visual languages of many types are used in many disciplines for many purposes. The use of visual languages is compelling for many reasons, not the least of which is that their graphical nature can act as an analogical representation of the actual domain in a way that is not possible with purely textual systems. Concept mapping languages form a large family of visual languages. Concept maps consist of labeled, typed nodes and typed arcs, and may also include contexts that partition the map. Concept maps appear to be particularly useful for knowledge representation; examples include Peirce's Existential Graphs, Sowa's Conceptual Graphs, and Gaines' KRS.}, added-at = {2006-03-24T16:34:33.000+0100}, address = {Banff, Alberta}, author = {Kremer, Ross}, biburl = {https://www.bibsonomy.org/bibtex/2b6529d4f2523567279264ced4db5ca2b/neilernst}, booktitle = {11th Workshop on Knowledge Acquisition, Modelling, and Management (KAW '98)}, citeulike-article-id = {121753}, comment = {- quote: Nosek and Roth (1990) have concluded that semantic networks (graphs) are more "computationally efficient" (understandable) than predicate logic (linear text) - see paper showing that info vis representations can be challenging - "Lambiotte, J. G., Dansereau, D. F., Cross, D. R. \& Reynolds, S. B. 1984. Multirelational Semantic Maps. Educational Psychology Review 1(4): 331-367 classify "multirelational semantic maps" into four categories: * Spatial layout and location systems: These systems rely on spatial proximity, spatial relationship (e.g.: above or below), or boundary lines to convey relationships among items. Layout systems are limited in complex domains because it is difficult to convey more than a very few relationship types spatially. * Node based systems: These systems generally use node attributes (e.g.: shape, color, or style) to distinguish a variety of nodes types to convey information. If arcs are used at all, the relationship they represent is ambiguous, or must be inferred from the adjoining node types. * Link-based systems: These systems convey almost all information in arcs which may be labeled and/or otherwise distinguished by attributes such as style, thickness or color to convey their type either canonically (drawn from a set of predefined types) or idiosyncratically (ad-hoc types). If nodes are present at all, they contain only the data, and are uniform in appearance. * Hybrid systems: These systems take advantage of spatial layout, node type, and link type to convey a much richer set of semantic information." - good descriptions of visual languages, some useful references.}, description = {sdasda}, interhash = {d6b4314f907ccef5f06401cb80486e5f}, intrahash = {b6529d4f2523567279264ced4db5ca2b}, keywords = {knowledge visual languages acquisition}, priority = {0}, timestamp = {2006-03-24T16:34:33.000+0100}, title = {Visual {L}anguages for {K}nowledge {R}epresentation}, url = {http://pages.cpsc.ucalgary.ca/~kremer/papers/KAW98/visual/kremer-visual.html}, volume = {Olympus C-4000}, year = 1998 }