Posted:August 15, 2011

World's Tallest Flagpole; see ref [9]The New Paradigm of ‘Substantive Marketing’ for Innovative IT

This decade has clearly marked a sea change in the move of enterprise software from proprietary to open source, as I have recently discussed [1]. It is instructive that only a mere six years ago I was in heated fights with my then Board about open source; today, that seems so quaint and dated.

Also during this period many have noted how open source has changed the capital required to begin a new software startup [2]. Open source both provides the tooling and the components for cobbling together specialty apps and extensions. Six and seven and even eight figure startup costs common just a decade ago have now dropped to four or five figures. When we see the explosion of hundreds of thousands of smartphone apps we are seeing the glowing residue of these additional sea changes. Dropping startup costs by one to three orders of magnitude is truly democratizing innovation.

But something else has been going on that is changing the face of enterprise software (besides consolidation, another factor I also recently commented on). And that factor is “marketing”. Much less commentary is made about this change, but it, too, is greatly lowering costs and fundamentally changing market penetration strategies. That topic — and my personal experience with it — is the focus of this article.

The Obsolete Recent Past

Besides the few remaining big providers of enterprise software — like IBM, Oracle, HP, SAP — most vendors have totally remade their sales practices of just a few years ago. Large sales forces with big commissions and a year to two year sales cycles can no longer be justified when software license fees and the percentage maintenance annuities that flow from them are dropping rapidly. Today’s mantras are doing more with less and doing it faster, hardly consistent with the traditional enterprise software model. Sure, big enterprises, especially big government and big business, have large sunk costs in legacy systems that will continue to be milked by existing vendors. But the flow is constricting with longer-term trends clear to see. The old enterprise software model is obsolete.

Even if it were not dying, it is hard to square huge investments in sales and marketing when product development has become inexpensive and agile. The proliferation of three-letter marketing acronyms for branding “new” product areas and standard formulas for product hype of just a few years ago also feels old and dated. Cozy relationships with conventional trade press pundits and market analysts seem to be diminishing in importance, possibly because the authoritativeness of their influence is also diminishing. It is harder to justify market firm subscription costs when priority budget items are being cut and new information outlets have emerged.

In response to this, many developers have forsaken the enterprise market for the consumer one. Indeed enterprises themselves are looking more and more to the consumer sector and commodity apps for innovation and answers. But, still, problems unique to enterprises remain and how to effectively reach them in this brave new world is today’s marketing problem for enterprise software vendors.

Most entities today, when opining about these challenges, tend to emphasize the need for “laser focus” and “rifle-shot” targeting of prospects. The advice takes the form of: 1) emphasize well-defined verticals; 2) know your market well; and 3) target and go after your likely prospects. Prospect data mining and targeted ad analysis are the proferred elixirs.

But, there is little evidence such refined methods for prospect identification and targeting are really working. Like politicians doing focus groups and opinion polling to capture the desired “message” of their potential electorates, these are all still “push” models of marketing. Yet we are swamped with pushed messages and marketing everywhere we turn. The model is failing.

Besides message overload, there are two issues with laser targeting. First, despite all that we try to know about ready buyers (for enterprise software), we really don’t know if any particular individual is truly needful, in a position to buy, has the authority to buy, or is the right advocate to make the internal sell. Second, though the idea of “laser” carries with it the image of focus and not flailing, it is in fact expensive to identify the targets and send a focused message their way. Because of these issues, decay rates for laser prospects throughout conventional sales pipelines continue to rise.

A New Marketing ParadigmNew Paradigm Roadsign

There has always been the phenomenon of the “fish jumping into the boat“; that is, the unanticipated inbound inquiry from a previously unknown prospect leading to a surprisingly swift sale. But we have seen this phenomenon increase markedly in recent years. Structured Dynamics‘ current customer base — including recurring customers — comes almost exclusively from this source. As we have noted this trend in comparison with more targeted outreach, we have spent much time trying to understand why it is occurring and how we can leverage what Peter Drucker called the “unexpected success” [3].

What we are seeing, I believe, is a shift from sales to marketing, and within marketing from direct or outbound marketing to a new paradigm of marketing. Others have likened this to inbound marketing [4] or content marketing [5] or permission marketing [6]. What we are seeing at Structured Dynamics bears many resemblances to parts of what is claimed for these other approaches, but not all. And, it is also true that what we are seeing may pertain mostly to innovative IT for emerging enterprise markets, and not a generalized paradigm suitable to other products or markets.

For lack of a better term, what we are seeing we can term “substantive marketing”. By this we mean offering valuable content and solutions-oriented systems for free and without restriction. This shares aspects with content marketing. Then, in keeping with the trend for buyers doing their own research and analysis to fulfill their own needs, similar to the premises of inbound or permission marketing, potential consumers can make their own judgments as to relevance and value of our offerings.

Sometimes, of course, some prospects find our approaches and solutions lacking. Sometimes, they may grab what we have offered for free and use them on their own without compensation to us. But where the match is right — and we need to be honest with both ourselves and the customer when it is not — we can better spend the customer’s limited time and resources to tailor our generic solutions to their specific needs. In doing so, we offer higher value (tailored services) while learning better about another spectrum of consumer need that can virtuously enhance our substantive offerings for the next prospect.

So, let’s decompose these components further to see what they can tell us about this new practice of substantive marketing and how to use it as an engine for moving forward.

Substantive Marketing

The Virtuous Cycle Begins with Substantive Solutions

The premise of substantive marketing is to offer square-deal value to the marketplace in the form of solutions-based content. Like content marketing that offers “the creation or sharing of content for the purpose of engaging current and potential consumer bases” [5], substantive marketing goes even further. The whole basis and premise of the approach is to provide substantive content, in one of more of these areas, preferably all:

  • Knowledge — this substantive area includes papers, commentary, survey results or listings of tools and references useful to the target market
  • Analysis — this content area includes unique analysis of market trends, data, technologies or reviews that pertain to the target market
  • Code — this area relates to the provision of open source code and tools, preferably under licenses that allow users to use the software without restriction (two examples are the Apache 2 license and the MIT license)
  • Documentation — a critical substantive area is the documentation in how to install, use, modify or customize these tools, including a prejudice to APIs and tutorial information
  • Methodologies, workflows and best practices — it is important to also discuss how to properly operate and utilize these tools and information. Taking care to document lessons learned and best practices also helps the user community avoid common mistakes and to speed adoption and utility, and
  • Demos — this area involves setting up (and sharing code and procedures for same) demos that show how the code and its methods actually work. Demos also become first use cases to aid the new user in learning and setting up the code bases.

Further, this substantive content is offered without strings, restrictions or customer fill-in forms. The content is not a come on or a teaser. We are not trying to gather leads or prospect names, because we have no intent to dun them with emails or follow-ups.

This substantive content is as complete as can be to enable new users to adopt the information and tools in their current state without further assistance. (In some cases, the information also educates the marketplace in order to prepare future customers for adoption.) Most importantly, this substantive content is offered for free, either open source (for code) or creative commons for documentation and other content. In return, it is fair to request — and we do — attribution when this material is used.

We have previously termed this complete panoply of substantive content a total open solution [7]. Some might find the provision of such robust information crazy: How can we give away the store of our proprietary knowledge and systems?

But we find this kind of thinking old school. In an open source world where so much information is now available online, with a bit of effort customers can find this information anyway. Rather, our mindset is that customers do not want to pay again for what has already been done, but are willing to pay for what can be done with that knowledge for their own specific problems. Offering the complete storehouse of our knowledge in fact signals our interest in only charging the customer for new answers, new value or new formulations. The customers we like to work with feel they are getting an honest, square deal.

Flagpole Venues Help Increase Awareness

Consider your substantive content to be your flag, a unique banner for conveying and packaging your specific brand. It is thus important to find appropriate flagpoles — in the virtual territories that your customers visit — for raising this content high for them to see. Since the role of these flagpoles is to create awareness in potential prospects — who you do not likely know individually or even by group in advance — it makes sense to raise your offerings up on many flagpoles and on the highest flagpoles. Visibility is the object of the approach.

This approach is distinctly not leafletting or cramming links or emails into as many spaces as possible. The idea of substantive marketing is to fly valuable content high enough that desirous potential customers can discover and then inspect the information on their own, and only if they so choose. In this regard, substantive marketing resembles permission marketing [6].

Being visible helps ensure that the needful, questing prospect that you would never have been able to target on your own is able to see and be aware of your offerings. And, since they are seeking information and answers, your collateral needs to be of a similar nature. Solutions and substance are what they are seeking; what you have run up the flagpole should respond to that.

The mindset here is to respect your prospective customers and to allow them to chose to receive and inspect your offerings, but only if they so choose. If flown in the right venues with the right visibility, customers will see your flags and inspect them if they meet their requirements.

Some of the venues at which you can raise your flags include:

  • Blogs — this venue is especially helpful, since you have complete control over content, message, voice and packaging
  • Social networks — the value of social networks is now accepted, and should be a core component of any visibility strategy. However, it is also important to make sure that your contributions are driven by substance and value and do not become part of the cacophonous background noise
  • Vertical media — there are always existing outlets well-read and -respected by your customer propects. Establishing relationships and value with these third-party outlets can extend your reach
  • Web sites — this venue includes your standard Web sites, of course. But, you should also consider setting up specific project-related sites or sites dedicated to documentation (c.f., our TechWiki site of 300+ technical articles) or to methodologies (the excellent MIKE2.0 site is one great example) or to other ways by which particular content (such as tools with the Sweet Tools site) can raise another flag
  • User forums — user discussion groups and forums also become their own attractants for like-interested prospects, and
  • Conferences and tradeshows — while potentially valuable, presence at conferences and tradeshows must be carefully evaluated. Since participation and opportunity costs are high, the venues should be clearly relevant to your market space with likely decision makers in attendance.

The observant reader will have already concluded that each of these venues develops slowly, and therefore raising visibility is generally a slow-and-steady game that requires patience. Start-up vendors backed by venture firms or those looking for quick visibility and cashout will not find this approach suitable. On the other hand, customer prospects looking for answers and self-sustaining solutions are not much interested in flash in the pan vendors, either.

A Model Responsive to the Changing Nature of Customer Prospects

The real drivers for this changing paradigm come from customer prospects. Sophisticated buyers of enterprise IT and instrumental change agents within organizations share most if not all of these characteristics:

  • They are inundated with marketing messages and jaded about hype and “pushed” messages
  • They are generally knowledgeable about their needs and problem spaces and about approximate technologies. They are eager and desirous of learning independently and know that their recommendations affect their personal reputations and standing within their enterprises
  • With the many volatile external and internal changes, including staff reductions and fluid assignments, leadership for new technology adoption can come from many different and unknown corners of the organization; it is extremely difficult to identify and target prospects
  • The economic and competitive environment places a premium on affordability and low-risk evaluations of new technologies
  • Lock-ins of any kind — be it to specific vendors or technologies — are understood as inherently risky. This understanding is raising the importance of open and standards-based approaches
  • Being the subject of a pushy sales effort is distasteful and a negative to an eventual sale. Education and learning, however, is respected
  • Because of all that is at stake, honesty with no bullshit is highly appreciated. If you as a vendor do not offer an appropriate solution or have fulfillment weaknesses, tell the prospect so. Further, tell them who can supply the solution. One never knows when and where the next problem may arise, and providing trustworthy advice can lead to later engagements.

More often than not we find our customers to have already installed and used our existing substantive materials for some time before they approach us about further work. They appreciate the tutorial information and have taught themselves much in advance. By the time we engage, both parties are able to cost-effectively focus on what is truly missing and needed and to deliver those answers in a quick way. Re-engagements tend to occur when a next set of gaps or challenges arise.

Though it may sound trite or even unbelievable to those who have not yet experienced such a relationship, the square deal value offered by substantive marketing can really lead to true partnerships and trust between vendor and customer. We experience it daily with our customers, and vice versa. We also think this is the adaptive approach that our new environment demands.

The Free Path to Open Source and Solutions

Once prospects learn of our substantive offerings, many may decide independently that what we have is not suitable. Others may simply download and use the information on their own, for which we often never know let alone receive revenue. We are completely fine with this, as shown for three different cases.

First, some of these prospects need no more than what we already have. This increases our user base, increases our visibility and often results in contributions to our forums and documentation.

Then, some of these prospects come to learn they need or want more than what our current offerings provide, leading to two possible forks. In one fork, the second case, they may have sufficient skills internally or with other suppliers to extend the system on their own. Some of this flows back to an improved code base or improved installation or documentation bases.

In the other fork, the third case, they may decide to engage us in tailoring a solution for them. That case is the only one of the three that leads to a direct revenue path.

In all three cases we win, and the customer wins. Maybe enterprise software vendors of decades past rue this reality of lower margins and shared benefits; we agree that the absolute profit potential of substantive marketing is much less. But we gladly accept the more enjoyable work and steady revenue relationships resulting from these changes. We are not engaged in some pollyann-ish altruism here, but in a steely-eyed honest brokering that best serves our own self-interest (and fairly that of the customer, as well).

A Square Deal Baseline for Tailored Services

Great IT product does not come from idle musings or dreamed up functionality. It comes solely and directly from solving customer problems. Only via customers can software be refined and made more broadly usable.

A slipstream of those who have previously become aware and tested our offerings will choose to engage our services. This generally takes the form of an inbound call, where the prospect not only qualifies itself, but also establishes the terms and conditions for the sale. They have chosen to select us; they are fish that have jumped into the boat.

To again quote Peter Drucker, “. . . the aim of marketing is to make selling superfluous. The aim of marketing is to know and understand the customer so well that the product or service fits him and sells itself. Ideally, marketing should result in a customer who is ready to buy. All that should be needed then is to make the product or service available . . .” [8]. This is precisely what I meant earlier about the shift in emphasis from sales to marketing.

Even at this point there may be mismatches in needs and our skills and availabilities. If such is the case, we do not hesitate to say so, and attempt to point the prospect in another direction (from which we also gain invaluable market knowledge). If there is indeed a match, we then proceed to try to find common ground on schedule and budget.

Paradoxically, this square deal and honesty about the readiness and weaknesses of our offerings often leads to forgiveness from our customers. For example, for some time we have lacked automated installation scripts that would make it easier for prospects to install our open semantic framework. But, because of compensating value in other areas, such gaps can be overlooked and tackled later on (indeed, as a current customer is now funding). By not pretending to be everything to everyone, we can offer what we do have without embarrassment and get on with the job of solving problems.

For larger potential engagements, we typically suggest a fixed price initial effort to develop an implementation plan. The interviews and research to support this typical 4- to 6-weeks effort (generally in the $5 K to $10 K range, depending) then result in a detailed fulfillment proposal, with firm tasks, budget and schedule, specific to that customer’s requirements. Just as we respect our prospects’ time and budget, we expect the same and do not conduct these detailed plans without compensation. With respect to fulfillment contracts, we cap contract amount and limit milestone payments to pre-set percentages or time expended, whichever is lower.

This approach ensures we understand the customer’s needs and have budgeted and tasked accordingly. Capped contracts also put the onus on us the contractor to understand our own effort and tasking structures and realities, which leads to better future estimating. For the customer, this approach caps risk and potential exposure, and ensures milestones are being met no matter the time expenditures by us, the contractor. This approach extends our square-deal basis to also embrace risks and payments.

New (and Open Source) Developments Fuel the Substance Pipeline

Thus, when customers engage us, they spend almost solely on new functionality specifically tailored to their needs. In doing so, we suggest they agree to release the new developments they fund as open source. We argue — and customers predominantly agree — that they are already benefitting from lower overall costs because other customers have funded sharable, open source before them. We point out that the new customers that follow them will also be independently creating new functionality, to which they will also later benefit.

(This argument does not apply to specific customer data or ontologies, which are naturally proprietary to the customer. Also, if the customer wants to retain intellectual ownership of extensions, we charge higher development fees.)

Once these new developments are completed, they are fed back into a new baseline of valuable content and code. From this new baseline the cycle of substantive marketing can be augmented anew and perpetuated.

Three Guidelines to Leverage Substantive Marketing

All of these points can really be boiled down to three guidelines for how to make substantive marketing effective:

  • First, whatever your domain or market, provide useful and substantive content. The content you offer is indeed your marketing collateral. Prospective customers can gauge from it directly whether it meets their needs, appears sound and workable, and has value. If you have little of substance to offer, this paradigm is not for you
  • Second, plant many flagpoles and raise your flags high in territories your market prospects are likely to visit. This is a process that requires thoughtfulness and patience. Thoughtfulness, because that is how you determine where to plant your flags. If you yourself are a consumer of what you offer, it is easier to find those venues. And patience, because it takes time to stack valuable content upon valuable content in order to raise visibility
  • And, third, be honest and respectful. Help your prospect work within available budget to achieve the most possible at lowest risk. And help them find others, if need be, who might be better able than you to truly solve their problems.

What we are finding — as we continue to refine our understanding of this new paradigm — is that through substantive marketing the fish are finding us and they sometimes jump into the boat. We like our enterprise customers to pre-qualify themselves and already be “sold” once they knock on the door. One never knows when that phone might ring or the email might come in. But when it does, it often results in a collaborative customer as a partner who is a joy to work with to solve exciting new problems.


[1] M.K. Bergman, 2011. “Declining IT Innovation in the Enterprise,” in AI3:::Adaptive Innovation blog, January 17, 2011. See http://www.mkbergman.com/940/declining-it-innovation-in-the-enterprise/.
[2] Paul Graham has been the most prominent observer of this scene; see P. Graham, 2008. “Why There Aren’t Any More Googles,” April 2008 (see http://www.paulgraham.com/googles.html) and subsequent articles.
[3] See esp. Peter F. Drucker, 1985. Innovation and Entrepreneurialship: Practice and Principals, Harper & Row, New York, NY, 277 pp.
[4] Inbound marketing is a marketing strategy that focuses on getting found by customers. According to David Meerman Scott, inbound marketers “earn their way in” (via publishing helpful information on a blog etc.) in contrast to outbound marketing where they used to have to “buy, beg, or bug their way in” (via paid advertisements, issuing press releases in the hope they get picked up by the trade press, or paying commissioned sales people, respectively). Brian Halligan, cofounder and CEO of HubSpot, claims he first coined the term of inbound marketing.
[5] Content marketing is an umbrella term encompassing all marketing formats that involve the creation or sharing of content for the purpose of engaging current and potential consumer bases. In contrast to traditional marketing methods that aim to increase sales or awareness through interruption techniques, content marketing subscribes to the notion that delivering high-quality, relevant and valuable information to prospects and customers drives profitable consumer action. See also Holger Shulze, 2011. B2B Content Marketing Trends slideshow, see http://www.slideshare.net/hschulze/b2b-content-marketing-report.
[6] Seth Godin coined the term permission marketing wherein marketers obtain permission before advancing to the next step in the purchasing process. It is mostly used by online marketers, notably email marketers and search marketers, as well as certain direct marketers who send a catalog in response to a request. Godin contrasts this approach to traditional “interruption marketing” where messages are sent without prior permission.
[7] See the three-part series, M.K. Bergman, 2010. “Listening to the Enterprise: Total Open Solutions,” “Part 1,” “Part 2” and “Part 3,” AI3:::Adaptive Information blog, May 12 – 31, 2010.
[8] Peter F. Drucker, 1974. Management: Tasks, Responsibilities, Practices. New York, NY: Harper & Row. pp. 864. ISBN 0-06-011092-9.
[9] The intro photo is of the world’s tallest flagpole (at 165 m), in Dushanbe, Tajikistan. The photo is courtesy of CentralAsiaOnline.com.
Posted:August 8, 2011

Geshi NetworkVisualization + Analysis Pushes Aside Cytoscape

Though I never intended it, some posts of mine from a few years back dealing with 26 tools for large-scale graph visualization have been some of the most popular on this site. Indeed, my recommendation for Cytoscape for viewing large-scale graphs ranks within the top 5 posts all time on this site.

When that analysis was done in January 2008 my company was in the midst of needing to process the large UMBEL vocabulary, which now consists of 28,000 concepts. Like anything else, need drives research and demand, and after reviewing many graphing programs, we chose Cytoscape, then provided some ongoing guidelines in its use for semantic Web purposes. We have continued to use it productively in the intervening years.

Like for any tool, one reviews and picks the best at the time of need. Most recently, however, with growing customer usage of large ontologies and the development of our own structOntology editing and managing framework, we have begun to butt up against the limitations of large-scale graph and network analysis. With this post, we announce our new favorite tool for semantic Web network and graph analysis — Gephi — and explain its use and showcase a current example.

The Cytoscape Baseline and Limitations

Three and one-half years ago when I first wrote about Cytoscape, it was at version 2.5. Today, it is at version 2.8, and many aspects have seen improvement (including its Web site). However, in other respects, development has slowed. For example, version 3.x was first discussed more than three years ago; it is still not available today.

Though the system is open source, Cytoscape has also largely been developed with external grant funds. Like other similarly funded projects, once and when grant funds slow, development slows as well. While there has clearly been an active community behind Cytoscape, it is beginning to feel tired and a bit long in the tooth. From a semantic Web standpoint, some of the limitations of the current Cytoscape include:

  • Difficult conversion of existing ontologies — Cytoscape requires creating a CSV input; there was an earlier RDFscape plug-in that held great promise to bridge the software into the RDF and semantic Web sphere, but it has not remained active
  • Network analysis — one of the early and valuable generalized network analysis plug-ins was NetworkAnalyzer; however, that component has not seen active development in three years, and dynamic new generalized modules suitable for social network analysis (SNA) and small-world networks have not been apparent
  • Slow performance and too-frequent crashes — Cytoscape has always had a quirky interface and frequent crashes; later versions are a bit more stable, but usability remains a challenge
  • Largely supported by the biomedical community — from the beginning, Cytoscape was a project of the biomedical community. Most plug-ins still pertain to that space. Because of support for OBO (Open Biomedical and Biological Ontologies) formats and a lack of uptake by the broader semantic Web community, RDF- and OWL-based development has been keenly lacking
  • Aside from PDFs, poor ability to output large graphs in a viewable manner
  • Limited layout support — and poor performance for many of those included with the standard package.

Undoubtedly, were we doing semantic technologies in the biomedical space, we might well develop our own plug-ins and contribute to the Cytoscape project to help overcome some of these limitations. But, because I am a tools geek (see my Sweet Tools listing with nearly 1000 semantic Web and -related tools), I decided to check out the current state of large-scale visualization tools and see if any had made progress on some of our outstanding objectives.

Choosing Gephi and Using It

There are three classes of graph tools in the semantic technology space:

  1. Ontology navigation and discovery, to which the Relation Browser and RelFinder are notable examples
  2. Ontology structure visualization (and sometimes editing), such as the GraphViz (OWLViz) or OntoGraf tools used in Protégé (or the nice FlexViz, again used by the OBO community), and
  3. Large-scale graph visualization in order to gain a complete picture and macro relationships in the ontology.

One could argue that the first two categories have received the most current development attention. But, I would also argue that the third class is one of the most critical:  to understand where one is in a large knowledge space, much better larger-scale visualization and navigation tools are needed. Unfortunately, this third category is also the one that appears to be receiving the least development attention. (To be sure, large-scale graphs pose computational and performance challenges.)

In the nearly four years since my last major survey of 26 tools in this category, the new entrants appear quite limited. I’ve surely overlooked some, but the most notable are Gruff, NAViGaTOR, NetworkX and Gephi [1]. Gruff actually appears to belong most in Category #2; I could find no examples of graphs on the scale of thousands of nodes. NAViGaTOR is biomedical only. NetworkX has no direct semantic graph importing and — while apparently some RDF libraries can be used for manipulating imports — alternative workflows were too complex for me to tackle for initial evaluation. This leaves Gephi as the only potential new candidate.

From a clean Web site to well-designed intro tutorials, first impressions of Gephi are strongly positive. The real proof, of course, was getting it to perform against my real use case tests. For that, I used a “big” ontology for a current client that captures about 3000 different concepts and their relationships and more than 100 properties. What I recount here — from first installing the program and plug-ins and then setting up, analyzing, defining display parameters, and then publishing the results — took me less than a day from a totally cold start. The Gephi program and environment is surprisingly easy to learn, aided by some great tutorials and online info (see concluding section).

The critical enabler for being able to use Gephi for this source and for my purposes is the SemanticWebImport plug-in, recently developed by Fabien Gandon and his team at Inria as part of the Edelweiss project [2]. Once the plug-in is installed, you need only open up the SemanticWebImport tab, give it the URL of your source ontology, and pick the Start button (middle panel):

SemWeb Plug-in for GephiNote the SemanticWebImport tool also has the ability (middle panel) to issue queries to a SPARQL endpoint, the results of which return a results graph (partial) from the source ontology. (This feature is not further discussed herein.) This ontology load and display capability worked without error for the five or six OWL 2 ontologies I initially tested against the system.

Once loaded, an ontology (graph) can be manipulated with a conventional IDE-like interface of tabs and views. In the right-hand panels above we are selecting various network analysis routines to run, in this case Average Degrees. Once one or more of these analysis options is run, we can use the results to then cluster or visualize the graph; the upper left panel shows highlighting the Modularity Class, which is how I did the community (clustering) analysis of our big test ontology. (When run you can also assign different colors to the cluster families.) I also did some filtering of extraneous nodes and properties at this stage and also instructed the system via the ranking analysis to show nodes with more link connections as larger than those nodes with fewer links.

At this juncture, you can also set the scale for varying such display options as linear or some power function. You can also select different graph layout options (lower left panel). There are many layout plug-in options for Gephi. The layout plugin called OpenOrd, for instance, is reported to be able to scale to millions of nodes.

At this point I played extensively with the combination of filters, analysis, clusters, partitions and rankings (as may be separately applied to nodes and edges) to: 1) begin to understand the gross structure and characteristics of the big graph; and 2) refine the ultimate look I wanted my published graph to have.

In our example, I ultimately chose the standard Yifan Hu layout in order to get the communities (clusters) to aggregate close to one another on the graph. I then applied the Parallel Force Atlas layout to organize the nodes and make the spacings more uniform. The parallel aspect of this force-based layout allows these intense calculations to run faster. The result of these two layouts in sequence is then what was used for the results displays.

Upon completion of this analysis, I was ready to publish the graph. One of the best aspects of Gephi is its flexibility and control over outputs. Via the main Preview tab, I was able to do my final configurations for the published graph:

Publication Options for GephiThe graph results from the earlier-worked out filters and clusters and colors are shown in the right-hand Preview pane. On the left-hand side, many aspects of the final display are set, such as labels on or off, font sizes, colors, etc. It is worth looking at the figure above in full size to see some of the options available.

Standard output options include either SVG (vector image) or PDFs, as shown at the lower left, with output size scaling via slider bar. Also, it is possible to do standard saves under a variety of file formats or to do targeted exports.

One really excellent publication option is to create a dynamically zoomable display using the Seadragon technology via a separate Seadragon Web Export plug-in. (However, because of cross-site scripting limitations due to security concerns, I only use that option for specific sites. See next section for the Zoom It option — based on Seadragon — to workaround that limitation.)

Outputs Speak for Themselves

I am very pleased with the advances in display and analysis provided by Gephi. Using the Zoom It alternative [3] to embedded Seadragon, we can see our big ontology example with:

  • All 3000 nodes labeled, with connections shown (though you must must zoom to see) and
  • When zooming (use scroll wheel or + icon) or panning (via mouse down moves), wait a couple of seconds to get the clearest image refresh:

Note: at standard resolution, if this graph were to be rendered in actual size, it would be larger than 7 feet by 7 feet square at full zoom !!!

To compare output options, you may also;

Still, Some Improvements Would be Welcomed

It is notable that Gephi still only versions itself as an “alpha”. There is already a robust user community with promise for much more technology to come.

As an alpha, Gephi is remarkably stable and well-developed. Though clearly useful as is, I measure the state of Gephi against my complete list of desired functionality, with these items still missing:

  • Real-time and interactive navigation — the ability to move through the graph interactively and to issue queries and discover relationships
  • Huge node numbers — perhaps the OpenOrd plug-in somewhat addresses this need. We will be testing Gephi against UMBEL, which is an order of magnitude larger than our test big ontology
  • More node and edge control — Cytoscape still retains the advantage in the degree to which nodes and edges can be graphically styled
  • Full round-tripping — being able to use Gephi in an edit mode would be fantastic; the edit functionality is fairly straightforward, but the ability to round-trip in appropriate formats (OWL, RDF or otherwise) may be the greater sticking point.

Ultimately, of course, as I explained in an earlier presentation on a Normative Landscape for Ontology Tools, we would like to see a full-blown graphical program tie in directly with the OWL API. Some initial attempts toward that have been made with the non-Gephi GLOW visualization approach, but it is still in very early phases with ongoing commitments unknown. Optimally, it would be great to see a Gephi plug-in that ties directly to the OWL API.

In any event, while perhaps Cytoscape development has stalled a bit for semantic technology purposes, Gephi and its SemanticWebImport plug-in have come roaring into the lead. This is a fine toolset that promises usefulness for many years to come.

Some Further Gephi Links

To learn more about Gephi, also see the:

Also, for future developments across the graph visualization spectrum, check out the Wikipedia general visualization tools listing on a periodic basis.


[1] The R open source math and statistics package is very rich with apparently some graph visualization capabilities, such as the dedicated network analysis and visualization project statnet. rrdf may also provide an interesting path for RDF imports. R and its family of tools may indeed be quite promising, but the commitment necessary to R appears quite daunting. Longer-term, R may represent a more powerful upgrade path for our general toolsets. Neo4j is also a rising star in graph databases, with its own visualization components. However, since we did not want to convert our underlying data stores, we also did not test this option.
[2] Erwan Demairy is the lead developer and committer for SemanticWebImport. The first version was released in mid-April 2011.
[3] For presentations like this blog post, the Seadragon JavaScript enforces some security restrictions against cross-site scripting. To overcome that, the option I followed was to:
  • Use Gephi’s SVG export option
  • Open the SVG in Inkscape
  • Expand the size of the diagram as needed (with locked dimensions to prevent distortion)
  • Save As a PNG
  • Go to Zoom It and submit the image file
  • Choose the embed function, and
  • Embed the link provided, which is what is shown above.
(Though Zoom.it also accepts SVG files directly, I found performance to be spotty, with many graphical elements dropped in the final rendering.)

Posted by AI3's author, Mike Bergman Posted on August 8, 2011 at 3:27 am in Ontologies, Open Source, Semantic Web Tools, UMBEL | Comments (0)
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Posted:July 25, 2011

WordPressOvercoming the Limitations of WordPress Search

Since the inception of this AI3 blog a bit over six years ago, I have gone through five different approaches to local site search, all geared to overcome the limitations of WordPress‘ native search function. The current and last iteration uses the Relevanssi plug-in, the best I have used so far. (Check it out yourself in the search box to the upper right.) I describe these five iterations in this post.

Iteration #1: Native WordPress Search

When first released, AI3 used the native search that comes with the WordPress installation (when first installed that was WP version 1.5; the current version is at 3.2.1). That was OK when few knew of my site and the number of visitors was low.

But the WP search is known to suck, mostly because of search results based on date posted not relevance and its slow performance. Once I began to get more traffic, it was time for a change.

Iteration #2: Google Custom Search

The option I have kept longest on this site is Google’s Custom Search. When first announced at the end of 2006 it was a real godsend and very innovative. I installed my first version in January 2007 and continued to make modifications and use it up through April 2010. I used it on various sites with many different types of Custom Search implementations.

Unfortunately, to use the free version it is necessary to include ads that Google provides. For a while, this served my purposes, since I was actively trying to learn whether ad revenues were viable for a standard blog and what kinds of traffic are necessary to produce meaningful revenues. However, by early 2010 I had come to the conclusion that — even with a quite popular blog for its niche — that ad revenues would never be that meaningful and it was not worth cluttering up my site. So I ended my experiment with Google ads and, being cheap, chose not to use the paid version of the search service and thus dropped the system.

What I liked:

  • Easy set up
  • Familiar search syntax and interface.

What I did not like:

  • Inclusion of Google ad panels
  • Lack of flexibility is styling search results presentation
  • Need for a Google key
  • Inability to tweak ordering of search results
  • Intrusive Google logos in multiple places.

Iteration #3: Bing Site Owner

Microsoft’s Bing was starting to come on strongly at that time so I decided next to try the Bing Site Owner’s service. I began this new approach immediately upon retiring Google.

What I liked:

  • Very easy set up
  • Acceptable flexibility in styling results
  • Nice popup implementation
  • Not overly intrusive with the Bing (MS) brand.

However, without direct notice, Microsoft ended this service as of April of this year.

What I did not like:

  • Service went dark
  • Cancelled service without any notification (except on the Bing webmaster’s site, a location I never visited)
  • No alternatives to the Bing API 2.0 with its difficult set up.

I was pretty pleased with the Bing service and would likely have continued using it because it wasn’t broke. But, the sudden plug-pulling was offputting.

Thus, I decided, heck, if I was going to have to go through the effort of learning the new Bing API, I might as well learn to do it all myself.

Iteration #4: WPSearch 2

So, it was back to researching options and WP plug-ins on the Web. After assembling the options, I first chose to go with WPSearch 2. The thing that most initially attracted me to this option was its reliance on the Lucene open source search engine, the same option that my company Structured Dynamics uses in its Solr text indexing for the Open Semantic Framework (OSF).

Since my AI3 blog theme is of my own design with many changes over the years, I had lost its original capabilities in having a native search form and search results page. So, my first task after installing the WPSearch plugin and indexing my content was to add these pages to my theme. The WP Codex has an OK set of instructions on creating a search page and related discussion.

There are some valuable tutorials out there that explain how this is done; I refer to them rather than repeat such information here.

I completed this work and kept WPSearch 2 up and active on my site for roughly the past week. But, I also kept trying to achieve some of the aspects I wanted in formatting and organizing search results and became increasingly frustrated. I also experienced numerous freezes and white screens and fatal PHP errors while editing new pages or deleting comment spam that told me I simply had to abandon this option.

In summary, what I liked:

  • Use of Lucene search engine
  • Very fast performance
  • Known search syntax.

What I did not like:

  • Duplicate results
  • Freezes and timeouts when managing comments or new edits
  • Inability to capture total search count (at least with my own PHP skills)
  • Inability to highlight search terms.

I’m sorry that I needed to abandon this option, since I do view highly the underlying Lucene text engine. But, the integration with existing WP functionality and other modules was not fully baked. I think with more work, including exposing more of the Lucene search API functionality, that this option could redeem itself. But, as of today, it is not reliable enough for my site.

Iteration #5: Relevanssi

In trying to find hacks and workarounds to some of the desires and issues noted above, I had come across reference to the Relevanssi plug-in, which appeared to embrace much of what I was looking to achieve. The download is quite small (100 K) and must therefore use the native WP MySQL for the index, but it is feature rich and has a strong relevance-ranking and with ranking flexibility. There is great flexibility and configurability in how search results get presented, also an attraction.

Installation of this system and then indexing was very clean and straightforward. It has a syntax that readily supports the Boolean AND operator (the default behavior I have set for the site) (if the AND search finds no matches, it will automatically do an OR search) and phrase searching, with the prior links showing examples from this blog (also see the search form at upper right).

As implemented, then, here is the listing of major features in Relevanssi:

  • Total number of search results (implemented)
  • Search term highlighting (implemented)
  • Contextual excerpt snippets (implemented)
  • Sort by date (not implemented)
  • Category search (not implemented)
  • Filter by date (not implemented)
  • Filter by category or tag (not implemented).

Here is a screen capture of the complete configuration menu in WordPress for Relevanssi:

Relevanssi Configuration Options

For further information, you may also want to see some more advanced search functions and the Relevanssi knowledge base.

Posted by AI3's author, Mike Bergman Posted on July 25, 2011 at 3:40 am in Blogs and Blogging, Site-related | Comments (1)
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Posted:July 18, 2011

Photo courtesy of levelofhealth.comA Decade of Remarkable Advances in Ten Grand IT Challenges

I’ve been in the information theory and technology game for quite some time, but believe nothing has matched the pace of advances of the past ten years. As one example, it was a mere eight years ago that I was sitting in a room with language translation vendors contemplating automated translation techniques for US intelligence agencies. The prospects finally looked doable, but the success of large-scale translation was not assured.

At about that same time, and the years until just recently, a whole slew of Grand Challenges [1] in computing hung out there: tantalizing yet not proven. These areas ranged from information extraction and natural language understanding to speech recognition and automated reasoning.

But things have been changing fast, and with a subtle steadiness that has caused it to go largely unremarked. Sure, all of us have been aware of the huge changes on the Web and search engine ubiquity and social networking. But some of the fundamentally hard problems in computing have also gone through some remarkable (but largely unremarked) advances.

We now have smart phones that speak instructions to us while we instruct them by voice in turn. Virtually all information conceivable is now indexed and made available through the Web; structure is now rapidly characterizing that information, making it even more useful to discover and organize. We can translate documents online with acceptable accuracy into more than 60 languages [2]. We can get directions to or see satellite views of virtually any place on earth. We have in fact become accustomed to new technology magic on a nearly daily basis, so much so that the pace of these advances seems to be a constant, blunting our perspective of just how rapid these advances have been progressing.

These advances are perhaps not the realization of artificial intelligence as articulated in the 1950s to 1980s, but are contributing to a machine-based ability to do tasks useful to humans heretofore impossible and at scales unimaginable. As Google and IBM’s Watson are showing, statistics (among other techniques) applied to massive knowledge bases or text corpora are breaking down all of the Grand Challenges of symbolic computing. The image that is emerging is less one of intelligent machines working autonomously than it is of computers working interactively or semi-automatically with humans to address previously unsolvable problems.

By using a perspective of the decade past, we also demark the seminal paper on the semantic Web by Berners-Lee, Hendler and Lassila from May 2001 [3]. Yet, while this semantic Web vision has been a contributor to the success of the Grand Challenge advances of the past ten years, I think we can also say that it has not been the key or even a primary driver. That day may still yet come. Rather, I think we have to look to natural language and statistics surrounding large-scale corpora as the more telling drivers.

Ten Grand Challenge Advances

Over the past ten years there have been significant advances on at least ten Grand Challenges in symbolic computation. As the concluding section notes, these advances can be traced in most part to broader advances in natural language processing, the logical and semiotic bases for interoperability, and standards (nominally in the semantic Web) for embracing them. Here are these ten areas of advance, all achieved over the past ten years:

#1 Information Extraction

Information extraction (IE) uses various forms of natural language processing (NLP) to identify structured information within unstructured or semi-structured documents. These documents are presented in machine-readable form (including straight text, various document formats or HTML) with the various types of information “tagged” or prompted for inclusion. Information types that can be extracted with one of the various techniques include entities, relations, topics, categories, and so forth. Once tagged or extracted, the information in the documents can now be included and linked to standard structured information (as might come from conventional databases) or to structure in other documents.

Most recently, a large number of online services and open source systems have also become available with strengths in one or more of these extraction types [4]. Some current examples include Yahoo! Term Extraction, OpenCalais, BeliefNetworks, OpenAmplify, Alchemy API, Evri, Extractiv, Illinois Tagger, and about 80 others [4].

#2 Machine Translation

Machine translation is the automatic translation of machine-readable text from one human language to another. Accurate and acceptable machine translation requires applying different types of knowledge including grammar, semantics, facts about the real world, etc. Various approaches have been developed and refined over time.

Especially helpful has been the availability of huge corpora in multiple languages to which large-scale statistical analysis may be applied (as is the case of Google’s machine translation) or human editing and refinement (as is the case with the more than 280 language versions of Wikipedia).

While it is true none of these systems have 100% accuracy (even human translators show much variation), the more advanced ones are truly impressive with remaining ambiguities flagged for resolution by semi-automatic means.

#3 Sentiment Analysis

Though sentiment analysis is strictly speaking a subset of information extraction, it has the more demanding and useful task of extracting subjective information, often across a group of documents or texts. Sentiment analysis can be applied to online reviews to determine the “polarity” about specific objects, and it is especially useful for identifying public opinion trends or evaluating social media for ranking, polling or marketing purposes.

Because of its greater difficulty and potential high value, many of the leading sentiment analysis capabilities remain proprietary. Some capable open source versions are available nonetheleless. There is also an interesting online application using Twitter feeds.

#4 Disambiguation

Many words have more than one meaning. Word sense disambiguation uses either machine learning, dictionaries (gazetteers) of known entities and concepts, ontologies or linguistic databases such as WordNet, or combinations thereof to evaluate ambiguous terms or phrases and resolve them based on context. Some systems need to be “trained” or some work automatically or others are based on evaulation and prompting (semi-automatic) to complete the disambiguation process.

State-of-the-art systems have greater than 90% precision [5]. Most of the leading open source NLP toolkits have quite capable disambiguation modules, and even better proprietary systems exist.

#5 Speech Synthesis and Recognition

Speech synthesis is the conversion of text to spoken speech and has been around for quite some time. Speech recognition is a far more difficult task in that a given sound clip or real-time spoken speech of a person must be converted to a textual representation, which itself can then be acted upon such as navigating or making selections. Speech recognition is made difficult because of individual voice differences, the variations of human languages and speech patterns, and the need to segment speech into a sequence of words. (In most spoken languages, the sounds representing successive letters blend into each other, so the conversion of the modulated wave form to discrete characters or tokens can be a very difficult process.)

Crude systems of a decade ago required much training with a specific speaker’s voice to show much effectiveness. Today, the range and ability to use these systems without training has markedly improved.

Until recently, improvements largely were driven by military and intelligence requirements. Today, however, with the ubiquity of smart phones and speech interfaces, the consumer market is greatly accelerating progress.

#6 Image Recognition

Image recognition is the ability to determine whether or not an electronic image contains some specific object, feature, or activity, and then to extract the image data associated with it. Today, under specific circumstances and for specific tasks, this can be done by computer. However, for the general case of arbitrary objects in arbitrary situations this challenge has not yet been fully met. The systems of today work best for simple geometric objects (e.g., polyhedra), human faces, printed or hand-written characters, or vehicles, and in specific situations, typically described in terms of well-defined illumination, background, and orientation of the object relative to the camera.

Auto license recognition at intersections, face recognition by security cameras, and greatly expanded and improved character recognition systems (machine vision) represent some of the current state-of-the-art. Again, smart phone apps are helping to drive advances.

#7 Interoperability Standards and Methods


Rapid Progress in Climbing the Data Federation Pyramid

Most of the previous advances are related to extracting structured information or mapping or deriving additional structured information. Once obtained, of course, the next challenge is in how to relate that information together; that is, how to make it interoperate.

We have been steadily climbing a data federation pyramid [6] — and at an impressively accelerating rate since the adoption of the Internet and Web. These network innovations gave us a common basis and protocols for connecting distributed devices. That, in turn, has freed us to concentrate on the standards for data representation and interoperability.

XML first provided a means for a common data serialization that encouraged various communities and industries to devise exchange vocabularies. RDF provided a means for a common data model, one that was both simple and extensible at the same time [7]. OWL built upon that basis to enable us to build common domain models (see next).

There are alternatives to the semantic Web standards of RDF and OWL such as common logic and there are many competing data exchange formats to XML. None of these standards is essential on its own and all have their communities and advocates. However, because they are standards and they share common network bases, it has also been relatively easy to convert amongst the various available protocols. We are nearly at a global level where everything is connected, machine-readable, and in structured form.

#7 Common Domain Models

Semantics in machine-readable form means that we can more confidently link and combine available information. We are seeing a veritable explosion of domain models to represent various domains and viewpoints in consensual, interoperable form. What this means is that we are now gaining the computing vocabularies and grammars — along with shared community models (world views) — to get this stuff to work together.

Five years ago we called this phenomena mashups, but no one uses that term any longer because these information brewpots are everywhere, including in our very hands when we interact with the apps on our smart phones. This glue of domain models is generally as invisible to us as is the glue in laminates or the resin in plastics. But they are the strength and foundations nonetheless that enable much of the computing magic unfolding around us.

#9 Virtual Apps (Cloud Computing)

Once the tyranny of physical separation was shattered between data and machine by the network, the rationale for keeping the data with the app or even the user with the app disappeared. Cloud computing may seem mysterious or sound to have some high-octave hum, but it really is nothing more than saying that the Web enables us to treat all of our computing resources as virtual. Data can be anywhere; machines and hard drives can be anywhere; and applications can be anywhere.

And, virtualness brings benefits in and of itself. Whole computing environments can be installed or removed nearly instantaneously. Peak computing demands can be met with virtual headrooms. Backup and rollover and redundancy practices and strategies can change. Web services mean tailored capabilities can be invoked from anywhere and integrated for local needs. Massive computing resources and server farms can be as accessible to the individual as they are to prior computing behemoths. Combined with continued advances in underlying computing hardware and chips, the computing power available to any user is rising exponentially. There is now even more power in the power curve.

#10 Big Data

One hears stories of Google or the National Security Agency having access and managing servers measured in the hundreds of thousands. Entirely new operating systems and computing environments — many with roots in open source — such as virtual operating systems and MapReduce approaches like Hadoop have been innovated to deal with the current era of “big data”.

MapReduce is a framework for processing huge datasets using a large number of servers. The “map” step partitions the problem into tractable sub-problems, organized in a tree structure. The “reduce” step then takes the answers to all the sub-problems and combines them to produce the final output.

Such techniques enable analysis of datasets of a size impossible before. This has enabled the development of statistics and analytical techniques that have been able to make correlations and find patterns for some of the Grand Challenge tasks noted before that simply could not be addressed within previous limits. The “big data” approach is providing a brute force alternative to previously intractable problems.

Why Such Progress?

Declining hardware costs and increasing performance (such as from Moore’s Law), combined with the adoption of the Internet + Web network, set the fertile conditions for these unprecedented advances in computing’s Grand Challenges. But the adaptive radiation in innovations now occurring has its own dynamics. In computing terms, we are seeing the equivalent of the Cambrian explosion in evolutionary history.

The dynamics driving this computing explosion are based largely, I believe, on the statistics of information retrieval and extraction needed to cope with the scale of documents on the Web. That, in turn, has impelled innovations in big data and distributed architectures and designs that have pried open previously closed computing lockboxes. As data from everywhere and from every provenance pours into the system, means for handling and interoperating with it have become imperatives. These forces, in turn, have been channeled and are being met through the open and standards-based approaches that helped lead to the development of the Internet and its infrastructure in the first place.

These powerful evolutionary forces in computing are clearly evident in the ten Grand Challenge advances above. But the challenges above are also silent on another factor, underpinning the interoperability initiatives, that is only now just becoming evident and exerting its own powerful force. That is the workable, intellectual foundations for interoperability itself.

Clearly, as the advances in the Grand Challenges show, we are seeing immense exposures of new structured information and impressive means for accessing and managing it on a global, distributed scale.  Yet all of this data and this structure begs the question of how to get the information to work together. Further, the sources and viewpoints and methods by which all of this data has been created also puts a huge premium on means to deal with the diversity. Though not evident, and perhaps not even known to many of the innovators and practitioners, there has been a growing intellectual force shaping our foundational views about the nature of things and their representations. This force has been, I believe, one of those root cause drivers helping to show the way to interoperability.

John Sowa, despite his unending criticism of the semantic Web in favor of common logic, has nonetheless been a very positive evangelist for the 19th century American logician and philosopher, Charles Sanders Peirce. Sowa points out that the entire 20th century largely neglected Peirce’s significant contributions in many areas and some philosophers appropriated Peircean insights without proper attribution [8]. Indeed, Peirce has only come to wider attention within the past decade or so. Much of his voluminous lifetime writings have still not yet been committed to publication.

Among many notable contributions, Peirce was passionate about signs and their triadic representations, in a field known as semiotics. The philosophical and logical basis of his triangle of signs deserves your attention, which can not be adequately treated here [9]. However, as summarized by Sowa [8], “A semiotic view of language and logic gets to the heart of the philosophical controversies and their practical implications for linguistics, artificial intelligence, and related subjects.”

In essence, Peirce’s triadic logic of semiotics helps clarify philosophical questions about things, how they are perceived and how they are named that has vexed philosophers at least since the time of Aristotle. What Peirce was able to put forward was a testable logic for how things and the names of things can be understood and related to one another, via logical statements or structures. These, in turn, can be symbolized and formalized into logical constructs that can capture the structure of natural language as well as more structured data.

The clarity of Peirce’s logic of signs is an underlying factor, I believe, for why we are finally seeing our way clear to how to capture, represent and relate information from a diversity of sources and viewpoints that is defensible and interoperable [10]. As we plumb Peircean logics further, I believe we will continue to gain additional insights and methods for combining and relating information. The next phase of our advances on these Grand Challenges is likely to be fueled more by connections and interoperability than in basic extraction or representation.

The Widening Explosion

We are not seeing the vision of artificial intelligence unfold as posed three decades ago. Nor are we seeing the AI-complete type of problems being solved in their entirety [11]. Rather, we are seeing impressive but incomplete approaches. Full automation and autonomy are not yet at hand, and may be so far in the future as to never be. But we are nevertheless seeing advances across the board in all Grand Challenge areas.

What is emerging is a practical achievement of the Grand Challenges, the scale and scope of which is unprecedented in symbolic computing. As we see Peircean logic continue to take hold and interoperability grow in usefulness and stature, I think it fair to say we can look back in ten years to describe where we stand today as having been in the midst of an evolutionary explosion.


[1] Grand Challenges were United States policy objectives for high-performance computing and communications research set in the late 1980s. According to “A Research and Development Strategy for High Performance Computing”, Executive Office of the President, Office of Science and Technology Policy, 29 pp., November 20, 1987, “A grand challenge is a fundamental problem in science or engineering, with broad applications, whose solution would be enabled by the application of high performance computing resources that could become available in the near future.”
[2] For example, as of July 17, 2011, Google offered 63 different source or target languages for translation.
[3] Tim Berners-Lee, James Hendler and Ora Lassila, 2001. “The Semantic Web”. Scientific American Magazine; see http://www.scientificamerican.com/article.cfm?id=the-semantic-web.
[4] Go to Sweet Tools, and enter the search ‘information extraction’ to see a list of about 85 tools.
[5] See, for example, Roberto Navigli, 2009. “Word Sense Disambiguation: A Survey,” ACM Computing Surveys, 41(2), 2009, pp. 1–69. See http://www.dsi.uniroma1.it/~navigli/pubs/ACM_Survey_2009_Navigli.pdf.
[6] M.K. Bergman, 2006. “Climbing the Data Federation Pyramid,” AI3:::Adaptive Information blog, May 25, 2006; see http://www.mkbergman.com/229/climbing-the-data-federation-pyramid/.
[7] M. K. Bergman, 2009. “Advantages and Myths of RDF,” AI3:::Adaptive Information blog, April 8, 2009. See http://www.mkbergman.com/483/advantages-and-myths-of-rdf/
[8] John Sowa, 2006. “Peirce’s Contributions to the 21st Century”, in H. Schärfe, P. Hitzler, & P. Øhrstrøm, eds., Conceptual Structures: Inspiration and Application, LNAI 4068, Springer, Berlin, 2006, pp. 54-69. See http://www.jfsowa.com/pubs/csp21st.pdf.
[9] See, as a start, the Wikipedia article on Charles Sanders Peirce (pronounced “purse”), as well as the Arisbe collection of his assembled papers (to date). Also see John Sowa, 2010. “The Role of Logic and Ontology in Language and Reasoning,” from Chapter 11 of Theory and Applications of Ontology: Philosophical Perspectives, edited by R. Poli & J. Seibt, Berlin: Springer, 2010, pp. 231-263. See http://www.jfsowa.com/pubs/rolelog.pdf. Sowa also says, “Although formal logic can be studied independently of natural language semantics, no formal ontology that has any practical application can ever be developed and used without acknowledging its intimate connection with NL semantics.”
[10] While Peirce’s logic and clarity of conceptual relationships is compelling, I find reading his writings quite demanding.
[11] In the field of artificial intelligence, the most difficult problems are informally known as AI-complete or AI-hard, meaning that the difficulty of these computational problems is equivalent to solving the central artificial intelligence problem of making computers as intelligent as people. Computer vision, autonomous robots and understanding natural language are amongst challenges recognized by consensus as being AI-complete. However, practical advances on the Grand Challenges were never defined as needing to meet the AI-complete criterion. Indeed, it is even questionable whether such a hurdle is even worthwhile or meaningful on its own.

Posted by AI3's author, Mike Bergman Posted on July 18, 2011 at 10:00 pm in Adaptive Innovation, Semantic Web, Structured Web | Comments (3)
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Posted:June 2, 2011

Schema.orgContrary to Some Views, Google and Co.’s Microdata Effort will Also Boost RDF

In my opinion, perhaps the most important event for the structured Web since RDF was released a dozen years ago was today’s joint announcement by the search engine triumvirate of Google, Bing and Yahoo! releasing Schema.org. Schema.org is a vendor specification for nearly 300 mini-schema (or structured record definitions) that can be used to tag information in Web pages. These schema are organized into a clean little hierarchy and cover many of the leading things — from organizations to people to products and creative works — that can be written about and characterized on the Web.

These schema specifications are based on the microdata standard presently under review as part of the pending HTML5 specification. Microdata are set record descriptions of key-value pair attributes that can be embedded into the HTML Web page language. These microdata schema are similar to microformats, but broader in coverage and extensible. Microdata is also simpler than RDFa, another W3C specification that the Schema.org organizers call “. . . extensible and very expressive, but the substantial complexity of the language has contributed to slower adoption.”

Is the Initiative a Slap in RDF’s Face?

Various forums have been alive with howls and questions from many RDF and RDFa advocates that this initiative negates years of effort behind those formats. Yet I and my company, Structured Dynamics, which base our entire technology approach on semantics and RDF, do not see this announcement as a threat or rejection. What gives; what is the difference in perspective?

In our view, RDF and its triple representations in its data model, is the simplest and most expressive means to represent any data or any data relationship. As such, RDF, and its language extensions such as OWL and ontologies, provide a robust and flexible canonical data model for capturing any extant data or schema. No matter what the native form of the source information, we can boil it down to RDF and inter-relate it to any other information. It is for these reasons (and others) we have frequently termed RDF as the universal data solvent.

But, simple records and simple data need not be encumbered with the complexity of RDF. We have long argued for the importance of naive data structs. Many of these are simple key-value pairs where the subject is implied. The so-called little structured data records in Wikipedia, called infoboxes, are of this form. JSON and many simple data formats also have cleaner data formats.

The basic fact that RDF provides a universal data model for any kind of native data does not necessarily translate into its use as the actual data exchange format. Rather, winning data exchange formats are those that can be easily understood, easily expressed and therefore widely used. I think there is a real prospect that microdata, ready for ingest and expression by the Web’s leading search engines, may represent a real sea change in the availability and expression of structured data on the Web.

More structure — not less — is the real fuel that will promote greater adoption of RDF when it comes time to interoperate that data. The RDF community should rejoice that more structure will be coming to the Web from Google et al.’s announcement. We should also soon see an explosion of tools and utilities and services that make it easy to automatically add such structure to Web pages via single clicks. Then, once this structure is available, watch out!

So, while the backers of Schema.org also announced their continued support for microformats and RDFa as they presently exist, I rather suspect today’s announcement represents a denouement for these alternative formats. Though these formats may be creatively destroyed, I think the effect on RDF itself will be a profound and significant boost. I foresee clarity coming to the marketplace regarding RDF’s role:  as a canonical means for expressing data of any form, and not necessarily as a data exchange format.

The Initiative is No Surprise

This initiative, led by Google, should be no surprise. Google is the registered agent for the Schema.org Web site and has been the key proponent of microdata via its support of Ian Hickson in the WhatWG and HTML5 work groups. As I stated a couple of years back, Google has also not hidden its interests in structured data. Practically daily we see more structured data appear in Google search results and it has maintained a very active program in structured data extraction from text and tables for some years.

Google and its search engine partners recognize that search needs are evolving from keyword retrievals to structure, relationships, and filtered, targeted results. Those advances come from structure — as well as the semantic relationships between things that something like the Schema.org begins to represent.

Many within the W3C and elsewhere questioned why Google was pushing microdata when there were competing options such as microformats or RDFa (or even earlier variants). Of course, like Microsoft of a decade earlier, some ascribed Google’s microdata advocacy as arising from commercial interests or clout in advertising alone. Of course Google has an economic interest in the growth and usefulness of the Web. But I do not believe its advocacy to be premised on clout or “my way or the highway.”

Google and the search engine triumvirate understand well — much better than many of the researchers and academics that dominate mailing list discussions — that use and adoption trump elegance and sophistication. When one deconstructs the design of microdata and the nearly 300 schema now released behind it, I think the pragmatic observer can only come to one conclusion: Job well done!

Why This is Exciting

I have been a fervent RDF advocate for nearly a decade and have also been a vocal proponent of the structured Web as a necessary stepping stone to the semantic Web. In fact, here is a repeat of a diagram I have used many times over the past 5 years:

Transition in Web Structure
Document Web Structured Web
Semantic Web
Linked Data
  • Document-centric
  • Document resources
  • Unstructured data and semi-structured data
  • HTML
  • URL-centric
  • circa 1993
  • Data-centric
  • Structured data
  • Semi-structured data and structured data
  • XML, JSON, RDF, etc
  • URI-centric
  • circa 2003
  • Data-centric
  • Linked data
  • Semi-structured data and structured data
  • RDF, RDF-S
  • URI-centric
  • circa 2007
  • Data-centric
  • Linked data
  • Semi-structured data and structured data
  • RDF, RDF-S, OWL
  • URI-centric
  • circa ???

When one looks at the schema of schema that accompany today’s announcement, it is really clear just how encompassing and important these instant standards will become:

DataType

Thing

Intangible

CreativeWork

Event

Organization

LocalBusiness

AnimalShelter
AutomotiveBusiness

ChildCare
DryCleaningOrLaundry
EmergencyService

EmploymentAgency
EntertainmentBusiness

FinancialService

FoodEstablishment

GovernmentOffice

HealthAndBeautyBusiness

HomeAndConstructionBusiness

InternetCafe
Library
LodgingBusiness

MedicalOrganization

ProfessionalService

RadioStation
RealEstateAgent
RecyclingCenter
SelfStorage
ShoppingCenter
SportsActivityLocation

Store

TelevisionStation
TouristInformationCenter
TravelAgency

NGO

SportsTeam

Organization (con’t)

Person
Place

Product

Today’s announcement is the best news I have heard in years regarding the structured Web, RDF, and the semantic Web. This announcement is — I believe — the signal event of the structured Web. With regard to my longstanding diagram above, I can go to bed tonight knowing we have now crossed the threshold into the semantic Web.

Posted by AI3's author, Mike Bergman Posted on June 2, 2011 at 8:57 pm in Adaptive Information, Structured Web | Comments (7)
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