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Concept maps popped onto the radar last week when an article in Wired highlighted a concept mapping tool called Cmap. Cmap is one of a variety of concept mapping tools that’s in use in schools and other educational settings to teach children to model the structure and relationships connecting – well – concepts.
The root idea of using concept mapping in educational settings is to move away from static models of knowledge, and toward dynamic models of relationships between concepts that allow new kinds of reasoning, understanding, and knowledge. That sounds a lot like the purpose of OWL.
It might be a stretch to say that by advocating concept maps, schools are in fact training kids to create ontologies as a basic learning and teaching method, and a vehicle for communicating complex ideas – but it’s a very interesting stretch all the same. As Information Architects, we’re familiar with the ways that structured visualizations of interconnected things – pages, topics, functions, etc. – communicate complex notions quickly and more effectively than words. But most of the rest of the world doesn’t think and communicate this way – or at least isn’t consciously aware that it does.
It seems reasonable that kids who learn to think in terms of concept maps from an early age might start using them to directly communicate their understandings of all kinds of things throughout life. It might be a great way to communicate the complex thoughts and ideas at play when answering a simple question like “What do you think about the war in Iraq?”
Author Nancy Kress explores this excact idea in the science fiction novel ‘Beggars In Spain’, calling the constructions “thought strings”. In Kress’ book, thought strings are the preferred method of communcation for extremely intelligent genetically engineered children, who have in effect moved to realms of cognitive complexity that exceed the structural capacity of ordinary languages. As Kress describes them, the density and multidimensional nature of thought strings makes it much easier to share nuanced understandings of extremely complex domains, ideas, and situations in a compact way.
I’ve only read the first novel in the trilogy, so I can’t speak to how Kress develops the idea of thought strings, but there’s a clear connection between the construct she defines and the concept map as laid out by Novak, who says, “it is best to construct concept maps with reference to some particular question we seek to answer or some situation or event that we are trying to understand”.
Excerpts from the Wired article:
“Concept maps can be used to assess student knowledge, encourage thinking and problem solving instead of rote learning, organize information for writing projects and help teachers write new curricula. “
“We need to move education from a memorizing system and repetitive system to a dynamic system,” said Gaspar Tarte, who is spearheading education reform in Panama as the country’s secretary of governmental innovation.”
“We would like to use tools and a methodology that helps children construct knowledge,” Tarte said. “Concept maps was the best tool that we found.”

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There’s an mSpace demo online.

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While researching and evaluating user interfaces and management tools for semantic structures – ontologies, taxonomies, thesauri, etc – I’ve come across or been directed to two good surveys of tools.
The first, courtesy of HP Labs and the SIMILE project is Review of existing tools for working with schemas, metadata, and thesauri. Thanks to Will Evans for pointing this out.
The second is a comprehensive review of nearly 100 ontology editors, or applications offering ontology editing capabilities, put together by Michael Denny at XML.com. You can read the full article Ontology Building: A Survey of Editing Tools, or go directly to the Summary Table of Survey Results.
The original date for this is 2002 – it was updated July of 2004.

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In the latest issue of ACMQueue, Tim Bray is interviewed about his career path and early involvement with the SGML and XML standards. While recounting, Bray makes four points about the slow pace of adoption for RDF, and reiterates his conviction that the current quality of RDF-based tools is an obstacle to their adoption and the success of the Semantic Web.
Here are Bray’s points, with some commentary based on recent experiences with RDF and OWL based ontology management tools.
1. Motivating people to provide metadata is difficult. Bray says, “If there’s one thing we’ve learned, it’s that there’s no such thing as cheap meta-data.”
This is plainly a problem in spaces much beyond RDF. I hold the concept and the label meta-data itself partly responsible, since the term meta-data explicitly separates the descriptive/referential information from the idea of the data itself. I wager that user adoption of meta-data tools and processes will increase as soon as we stop dissociating a complete package into two distinct things, with different implied levels of effort and value. I’m not sure what a unified label for the base level unit construct made of meta-data and source data would be (an asset maybe?), but the implied devaluation of meta-data as an optional or supplemental element means that the time and effort demands of accurate and comprehensive tagging seem onerous to many users and businesses. Thus the proliferation of automated taxonomy and categorization generation tools…
2. Inference based processing is ineffective. Bray says, “Inferring meta-data doesn’t work… Inferring meta-data by natural language processing has always been expensive and flaky with a poor return on investment.”
I think this isn’t specific enough to agree with without qualification. However, I have seen analysis of a number of inferrencing systems, and they tend to be slow, especially when processing and updating large RDF graphs. I’m not a systems architect or an engineer, but it does seem that none of the various solutions now available directly solves the problem of allowing rapid, real-time inferrencing. This is an issue with structures that change frequently, or during high-intensity periods of the ontology life-cycle, such as initial build and editorial review.
3. Bray says, “To this day, I remain fairly unconvinced of the core Semantic Web proposition. I own the domain name RDF.net. I’ve offered the world the RDF.net challenge, which is that for anybody who can build an actual RDF-based application that I want to use more than once or twice a week, I’ll give them RDF.net. I announced that in May 2003, and nothing has come close.”
Again, I think this needs some clarification, but it brings out a serious potential barrier to the success of RDF and the Semantic Web by showcasing the poor quality of existing tools as a direct negative influencer on user satisfaction. I’ve heard this from users working with both commercial and home-built semantic structure management tools, and at all levels of usage from core to occasional.
To this I would add the idea that RDF was meant for interpretation by machines not people, and as a consequence the basic user experience paradigms for displaying and manipulating large RDF graphs and other semantic constructs remain unresolved. Mozilla and Netscape did wonders to make the WWW apparent in a visceral and tangible fashion; I suspect RDF may need the same to really take off and enter the realm of the less-than-abstruse.
4. RDF was not intended to be a Knowledge Representation language. Bray says, “My original version of RDF was as a general-purpose meta-data interchange facility. I hadn’t seen that it was going to be the basis for a general-purpose KR version of the world.”
This sounds a bit like a warning, or at least a strong admonition against reaching too far. OWL and variants are new (relatively), so it’s too early to tell if Bray is right about the scope and ambition of the Semantic Web effort being too great. But it does point out that the context of the standard bears heavily on its eventual functional achievement when put into effect. If RDF was never meant to bear its current load, then it’s not a surprise that an effective suite of RDF tools remains unavailable.

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