Posted:July 12, 2010

Benefits from an Incremental Approach
Using Incremental, Low-risk Semantic and Open World Approaches

OK. So, you’re looking at your garage … or your bedroom closet … or your office and its files. They are a mess, and you can’t find anything and you can’t stuff anything more into the nooks, cubbies, crannies or cabinets. What do you do?

Well, when you finally get fed up and have a rainy day or some other excuse, you tackle the mess. Maybe you grab a big mug of coffee to prepare for the pending battle. Maybe you strip down to comfort clothes. Then, if you’re like me, you begin to organize stuff into piles. Labeled piles and throwaway piles and any other piles that can provide a means to start bringing order to the chaos.

In the semantic Web world, there is a phrase coined by Jim Hendler that captures this approach: A little semantics goes a long way [1]. A little semantics, just like your labeled piles, helps to bring order to information chaos.

Mind you, this is not fancy or expensive stuff. In the case of my office, it is colored sheets of paper labeled with Magic Markers as “Taxes” or “Internal” or “Blog Posts” or whatever. Then, I begin sifting and distributing. In the case of the semantic world, these are classifying things into like categories and simply relating them to other categories with simple relationships, such as “is Part Of” or “is Narrower Than”.

Of course, I could have approached my mess in a different way. I could have hired an efficiency expert to come in, interview me and all of my employees and colleagues, gotten a written analysis and report, and then committed to a multi-week project to completely store and place every single last piece of paper in my office or organize every rake and set of abandoned golf clubs in my garage. When done, I would have shelled out much money and I suspect still not have been able to find anything.

Sort of sounds like the traditional way IT does its business, doesn’t it? To clean up their information messes, enterprises need to find a better strategy.

I’m not too long from having returned from the SemTech conference, which overall was quite an excellent show. But despite its emphasis on semantic technologies and their usefulness to businesses and enterprises, I found one critical theme unspoken: the ability of semantic approaches to change how enterprise IT actually does business. New ways have got to be found to clean up the many and growing information piles emerging all around us.

The Changing Nature of IT

IT is — and has been — going through a fundamental set of changes for decades. In the last decade, these changes have led to lowered relative spending, a shift in spending priorities toward services, less innovation, and less productivity. Some data and observations by researchers and analysts document these trends.

The following chart, using US Bureau of Economic Analysis data [2], shows the clear 50-year trend in declining hardware costs for enterprises, mostly resulting from the observation known as Moore’s Law. These massive hardware cost reductions (logarithmic scale) have also resulted in lower prices for IT as a whole. In 2008, for example, total relative IT prices were about two-thirds what they were a mere decade earlier:

US IT Prices in Relation to Each Other, 1960 - 2008

Source: M.K. Bergman and Bureau of Economic Analysis [2] (click for full size)

In contrast, relative prices for software and services have remained remarkably flat over this entire period, including for the past decade. This is somewhat surprising given the emergence of packaged software and more recently open source. However, relative percentage expenditures for custom software and software developed in-house have also remained strong over the past decade [3].

The mid- to late-1990s represented the high-water mark on many bases for enterprise IT, expenditures and vendors. Roughly in 1997 or so, the number of public enterprise software vendors peaked as did venture funding [4] and relative expenditures for IT in relation to GDP. There was a major uptick in relation to preparing for Y2K and a major downtick due to the dot-com bubble, and then of course the past two years or so have seen a global economic downturn. But, as the figure below shows (red), the long-term trend tends to suggest a relative plateau for IT expenditures in relation to GDP somewhat around 2000:

IT and Software Expenditures in Relation to GDP, 1960 - 2008

Source: M.K. Bergman and Bureau of Economic Analysis [2] (click for full size)

Yet, like the first chart, software seems to be bucking this trend (blue lines above). Though perhaps the rate of growth in expenditures for software is slowing a bit, it is still on a growth upslope, especially in relation to overall IT expenditures. The next chart, in fact, specifically compares software expenditures to total IT expenditures. Software expenditures are some 40% higher in relation to total IT than they were a mere decade ago:

US Software Expenditures in Relation to Total IT, 1960 - 2008

Source: M.K. Bergman and Bureau of Economic Analysis [2] (click for full size)

The mix of these software expenditures is also changing in major ways while stagnating in others.

The changing aspect is coming about from the shift of expenditures from license and maintenance fees to services. A number of software vendors began to see revenues from services overcome that from licensing in the 1990s. By the early 2000s, this was true for the enterprise software sector as a whole [4]. Today, service revenues account for 70% or so of aggregate sector revenues. Combined with the emergence of open source and other alternatives such as software as a service (SaaS), I think it fair to say that the era of proprietary software with exceedingly high margins from monopoly rents is over [5].

The stagnating aspect occurs in how the software expenditures are applied. According to Gartner, in the US, more than 70% of IT expenditures are devoted to simply running existing systems, with only about 11% of budgets devoted to innovation; other parts of the world spend nearly double on innovation and much lower for operations [6]. This relative lack of support for innovation and high percentages for running existing systems has held true for about a decade. Meanwhile, IT’s contribution to US productivity has been declining since 2001 [7].

What is the Cause for IT’s Ills?

Last year, PricewaterhouseCoopers published a major report with the provocative title, “Why Isn’t IT Spending Creating More Value?[7]. The 42-page report covered many of the aspects above. Among other factors, the PWC authors speculated that:

As consumption of IT increases and as technologies change and advance, businesses have been left to cobble together disparate software and hardware systems and tools. The end result? Unchecked IT spending, unneeded complexity, redundant systems, underutilized hardware and data centers, the need for expensive IT security, and, inevitably, diminishing returns from IT. In short, low levels of IT productivity create conditions for an IT cost crisis. [7]

I suppose one could add to this litany other factors such as the growth and emergence of the Internet, sector consolidations through mergers and acquisitions, the rise of open source and alternatives such as SaaS, etc.

But which of these are causes? Which are symptoms? And which might only be consequences or coincident?

To be sure, all recognize the explosion of digital data and information, with sources and formats springing up faster than Whack-a-Mole. It is such an evident and ubiquitous phenomenon that pointing to it as a cause appears on the face of it quite obvious. Also obvious is that these new sources carry with them a diversity of systems and tools. While not categorically stated as such, it appears that PWC fingers the difficulties of “cobbling” these systems together as the root cause for low productivity and thus the IT cost crisis.

I agree totally that these are symptoms of what we see in IT’s current circumstance. I would even say these factors are a proximate cause to these ills. But I disagree they are the root cause. To discover that root, I believe, we must look deeper to mindset and assumptions.

Closed World Mindset as the Root Cause

There are some phenomena that are so obvious that they are easily missed. Not seeing your fingertip six inches between your eyes is one of these. We aren’t used to focusing on things so near at hand.

So, let’s look for a moment at the closed world assumption (CWA), a key underpinning to most standard relational data systems and enterprise schema and logics. CWA is the logic assumption that what is not currently known to be true, is false. If CWA is not directly familiar to you that is understandable; it is an implied assumption of these systems and logics. As such, it is not often inspected directly and therefore not often questioned [8].

With regard to standard IT systems, the closed world assumption has two important aspects:

  1. The assumption is that the information domain at hand is complete [9], and
  2. The related negation as failure, which assumes every predicate to be false that cannot be proved to be true.

On the face of them, these assumptions seem tame enough. And, indeed, there are some enterprise data systems that absolutely rely on them for efficient processing and completion times, such as most transaction systems. CWA is absolutely the appropriate design for such applications.

However, for knowledge management or representation applications — that is, applications which involve combining or using heterogeneous data or information from multiple data sources, which are exactly the same sources requiring information “cobbling” noted above by PWC — there are two very critical implications of the closed-world assumption (CWA):

  1. Efforts or projects can not be undertaken incrementally; if done in pieces, each piece must be complete and consistent, which is expensive to scope and do
  2. To be consistent and explicit, the predicates (properties or relationships) must also be complex to model the “reality” of the system, which is also expensive to scope and do [10].

The net effect, which I have argued before, most notably in a major piece about the open world assumption [11], is that typical projects with a knowledge management aspect have become costly, take very long to complete, often fail, and require much planning and coordination. These facts have been true for three decades as enterprises have attempted to extract knowledge from their electronic information using closed world approaches based on relational systems. And, as recognized by PWC, these problems are only getting worse with growth in diversity and scope of systems.

The implications of closed world v. open world approaches are absolutely at the root of the causes leading to declining productivity, low innovation, significant failures and increasing costs — all exacerbated with more data and more systems — now characterizing traditional enterprise IT. Moreover, it is not a problem for open world systems to link to and incorporate closed world approaches. With open world, there is no need for Hobson’s choices. Unfortunately, such is not true when one begins with a closed world premise.

Incremental is Good: Pay as You Go

As best as I can tell, Alon Halevy was the first to use the phrase “pay as you go” in 2006 to describe the incremental aspect of the open world approach in relation to the semantic Web [12]. The “pay as you go” phrase had been applied earlier to data management and storage and had also been used to describe phone calling plans.

Incremental concepts and “agility” have been popular topics for the past five to ten years in IT, most often related to software development. And, while “incremental” sounds good in relation to enterprise projects, especially of a knowledge management or information integration/federation nature, the actual methodologies put forward were anything but incremental in their conceptual underpinnings.

Unfortunately, the “pay as you go” phrase has (and still is) largely confined to incremental, open world approaches involving the semantic Web. How this approach might apply and benefit enterprises has yet to be articulated. Nonetheless, I like the phrase, and I think it evokes the right mindset. In fact, I think with linked data and many other aspects of the current semantic Web we are seeing such approaches come to fruition. Inch-by-inch, brick-by-brick, data on the Web is getting exposed and interlinked. “Pay as you go” is incremental, and that is good.

Purposeful is Better: Pay as You Benefit

Yet the idea of “pay as you benefit” is more purposeful, able to be planned and implemented, and founded on standard enterprise cost-benefit principles. I think it is a better (and more nuanced) expression of the “pay as you go” mindset in an enterprise setting. What it means is you can start small and be incomplete. You can target any domain or department or scope that is most useful and illustrative for your organization. You can deploy your first stand-ups as proofs-of-concept or sandboxes. And, you can build on each prior step with each subsequent one.

One of the reasons we (Structured Dynamics) embraced the MIKE2.0 methodology [13] was its inherent incremental character. (Government deployments often call them “spirals”.) In general, the five phases of MIKE2.0 can be represented as follows:

Five Phases of MIKE2.0

(click for full size)

It is specifically during the fifth phase, testing and improvement, that quantitative and qualitative benefits from the current increment are calculated and documented. This evolving methodology is where the enterprise can assess the results of its prior investment and scope and budget for the next one. These can be quick, rapid increments, or more involved ones, depending on the schedule, prior results and risk profile of the enterprise (or department) at that time.

Much is made of “incremental” or “agile” deployments within enterprises, but the nature of the traditional data system (and its closed world assumption) can act to undermine these laudable steps. The inherent nature of an open world approach, matched with methodologies and best practices, can work wonderfully with KM-related projects.

Quite Simply a Different Way to Do Business

We see in our current IT circumstances a number of embedded practices and assumptions. We have been assuming control and completeness — the closed world opposite to the open world approach. We have thus embraced and promoted “global” or enterprise-wide solutions: be they desktop operating systems or browsers or expensive enterprise-level proprietary software solutions. This scope leads to immense hurdle rates and risks: we better get our choices right up front, because if we don’t, the department or enterprise are at risk. We have an inward focus about our own resources, our own networks, our own systems. Meanwhile, when we look outward, we wonder how all of these new Web companies can grow and expand so rapidly in comparison to us.

Clearly, we are seeing shifts to more services than products, more open source, more outsourcing, and more software as a service. Yet, because of the legacy of decades-long commitments from prior IT investment and the failures of many hyped “solutions” such as ERP or BI or data warehousing or a dozen others, we also see a decline and a reluctance for IT to embrace new and transforming approaches. Our prior choices were practically tantamount to “betting the enterprise.” What if our new approaches fail as so many of their predecessors did? In a demanding, competitive environment can we afford to make such wrong choices again with such immense implications?

Yet, now that information technology is a given, it only seems natural that its role becomes an integral part of the enterprise, and not a special function. Like procurement, IT has matured to become a support function. Businesses should not succeed or fail based on the types of pencils and paper stock they use; so should they not depend on the software support choices that IT makes. Enterprises are now past the need to get “computerized”; they are thoroughly so. But our understanding of IT’s role and position has not evolved with its own success.

The first whiffs of these challenges to IT’s initial hegemony came from the departmental introduction of PCs and local networks in the early 1980s. It has continued with desktop software, spreadsheets and Web portals and sites. Large, mature companies awoke in horror in the last decade to discover they had hundreds — sometimes thousands — of Web sites and content dissemination points over which IT had little or no control. Such is the nature of entropy, and it is a fact for any organization of any size.

So, now, with strategies such as “pay as you benefit,” there is no longer an excuse not to innovate. There is not a justification to put off testing and discovering benefits that the open world and semantic approaches can bring to your organization. There is now a basis to make the case and set the affordable budgets within desirable timelines for becoming a semantic enterprise.

Mindsets and expectations do require some adjustment. For example, not everything will be known or modeled in early phases. But, is that also not true in any “real” real world? We’re not talking high-throughput transaction systems here, but beginning to pull together and link the information that is important to your organization strategically.

Remember the intro statement that “a little semantics goes a long way”? Well, that truth — and it is true — when combined with incremental deployment firmly tied to demonstrable results, promises quite simply a different way to do business. Never before have enterprises had working and winnable approaches such as this to test and innovate and learn and discover. Jump on in; the water is clear and warm.

And, oh, as to that mess in your closet or garage? Well, if you adhere to CWA, you will need to define a place for everything to go before you can start cleaning things up. I say: forget those false hurdles. If you’d really want to make a dent in the mess, grab a broom and start cleaning.

[1] Jim Hendler, “a little semantics goes a long way.” See
[2] All starting data is for the United States only and comes from the U.S. Bureau of Economic Analysis, U.S. Department of Commerce. The data tables were downloaded from the BEA Web site at GDP data is from Section 1; enterprise private investment data from Section 5. For reasons as described in the text, all relative BEA numbers were re-adjusted from a 2005 baseline to 1997 based on absolute figures. Software figures and expenditures include packaged software, custom software and software developed in-house, but excludes software bundled or included within hardware.
[3] Data not shown; see the “Software Investment and Prices, by Type” data on the BEA Web page
[4] Michael A. Cusumano, 2008. “The Changing Software Business: Moving from Products to Services,” Massachusetts Institute of Technology, in Computer, Vol 41 (1): 20-27, January 2008. See This shift has occurred despite the recognition that potential gross margins from software packages can exceed 90% due to zero costs of reproduction. As Cusumano notes in a rule, “99 percent of zero is zero: The great profit opportunity from software products becomes theoretical and not practical” if not sold. Also, another interesting observation made by Cusumano is that in the shift to services vendors with both low percentages and high percentages of services, or what he calls the “sweet spots”, show higher contributions to profitability than vendors in the middle. He posits that low percentage vendors are getting mostly profitable maintenance fees, while those above 60% in services show profitability due to learning more replicable and systematic processes and approaches for service delivery.
[5] While we may occasionally see some vendors successfully buck this trend, I suspect these will only occur for established vendors with established platform advantages or for isolated applications where the innovating vendors have a significant first-mover advantage.
[6] Garnter calls the innovation category “transform”; see Gartner, Incorporated, 2009. “IT Software and Services, 2007-2010,” see Also, see Jed Rubin and Howard Rubin, 2006. “Worldwide IT Benchmark Service New Trends & Findings for 2007: Strategic Performance Management and Measurement,” from Gartner Consulting Worldwide IT Benchmark Service; see
[7] PricewaterhouseCoopers, 2009. “Why Isn’t IT Spending Creating More Value?”, see
[8] Though relational database systems did not begin with an understanding of CWA, but rather Edgar Codd’s 12 rules, the understandings of these were formulated later by Raymond Reiter.  Reiter first described the basis of CWA in 1978, and then provided an axiomatization of relational databases and their deductive generalizations and basis in CWA in 1984; see
[9] Relational database systems also assume unique names for objects, which, while not perhaps the best design for federated systems, can be overcome in other ways.
[10] For semantics-related projects there is a corollary problem to the use of CWA which is the need for upfront agreement on what all predicates “mean”, which is difficult if not impossible in reality when different perspectives are the explicit purpose for the integration.
[11] See M. K. Bergman, 2009. The Open World Assumption: Elephant in the Room, December 21, 2009. The open world assumption (OWA) generally asserts that the lack of a given assertion or fact being available does not imply whether that possible assertion is true or false: it simply is not known. In other words, lack of knowledge does not imply falsity. Another way to say it is that everything is permitted until it is prohibited. OWA lends itself to incremental and incomplete approaches to various modeling problems.
[12] This was also the first instance (I believe) of Alon coining the “dataspace” term. First use of the “pay as you go” phrase was, Alon Halevy, Michael Franklin, and David Maier, 2006. “Principles of Dataspace Systems,” in Proceedings of ACM Symposium on Principles of Database Systems, pp: 1-9. See also the slides accompanying that talk, Alon Halevy, 2006. “Principles of Dataspace Systems (PODS),” June 26, 2006; see, 2006. More explicitly the next year see Jayant Madhavan, Shirley Cohen, Xin (Luna) Dong, Alon Y. Halevy, Shawn R. Jeffery, David Ko, and Cong Yu, 2007. “Web-scale Data Integration: You Can Afford to Pay as You Go.” in 3rd Conf. on Innovative Data Systems Research (CIDR), pp 342-350, see The term has been picked up by many others, notably Rada Chirkova, Dongfeng Cheny, Fereidoon Sadriz and Timo J. Salo, 2007. “Pay-As-You-Go Information Integration: The Semantic Model Approach,” see; and most recently papers by Gerhard Weikum on RDF-3X; see!OpenDocument&ExpandSection=-1.
[13] See M.K. Bergman, 2010. “MIKE2.0: Open Source Information Development in the Enterprise,” AI3 Blog posting, February 23, 2010; and M.K. Bergman, 2010. Open SEAS: A Framework to Transition to a Semantic Enterprise,” AI3 Blog posting, March 1, 2010.

Posted by AI3's author, Mike Bergman Posted on July 12, 2010 at 10:57 pm in Adaptive Innovation, MIKE2.0, Semantic Enterprise | Comments (4)
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Posted:July 6, 2010

Consolidating Under the Open Semantic Framework
Release of Semantic Components Adds Final Layer, Leads to Streamlined Sites

Yesterday Fred Giasson announced the release of code associated with Structured Dynamics‘ open source semantics components (also called sComponents).  A semantic component is an ontology-driven component, or widget, based on Flex. Such a component takes record descriptions, ontologies and target attributes/types as inputs and then outputs some (possibly interactive) visualizations of the records.

Though not all layers are by any means complete, from an architectural standpoint the release of these semantic components provides the last and missing layer to complete our open semantic framework. Completing this layer now also enables Structured Dynamics to rationalize its open source Web sites and various groups and mailing lists associated with them.

The OSF “Semantic Muffin”

We first announced the open semantic framework — or OSF — a couple of weeks back. Refer to that original post for more description of the general design [1]. However, we can show this framework with the semantic components layer as illustrated by what some have called the “semantic muffin”:

Incremental Layers of the Open Semantic Framework

(click for full size)

The OSF stack consists of these layers, moving from existing assets upward through increasing semantics and usability:

  • Existing assets — any and all existing information and data assets, ranging from unstructured to structured. Preserving and leveraging those assets is a key premise
  • scones / irON — this layer is for general conversion of non-RDF data and data schema to RDF (via irON or RDFizers) or for information extraction of subject concepts or named entities (scones)
  • structWSF — is the pivotal Web services framework layer, and provides the standard, common interface by which existing information assets get represented and presented to the outside world and to other layers in the OSF stack
  • Semantic components — the highlighted layer in the “semantic muffin”; in essence, this is the visualization and data interaction layer in the OSF stack; see more below
  • Ontologies — are the layer containing the structured assets “driving” the system; this includes the concepts and relationships of the domain at hand, and administrative ontologies that guide how the user interfaces or widgets in the system should behave, and
  • conStruct — is the content management system (CMS) layer based on Drupal and the thinnest layer with respect to OSF; this optional layer provides the theming, user rights and permissions, or other functionality drawn from Drupal’s 6500 third-party modules.

Not all of these layers are required in a given deployment and their adoption need not be sequential or absolutely depend on prior layers. Nonetheless, they do layer and interact with one another in the general manner shown.

The Semantics Components Layer

Current semantic components, or widgets, include: filter; tabular templates (similar to infoboxes); maps; bar, pie or linear charts; relationship (concept) browser; story and text annotator and viewer; workbench for creating structured views; and dashboard for presenting pre-defined views and component arrangements. These are generic tools that respond to the structures and data fed to them, adaptable to any domain without modification.

Though Fred’s post goes into more detail — with subsequent posts to get into the technical nuances of the semantic components — the main idea of these components is shown by the diagram below.

These various semantic components get embedded in a layout canvas for the Web page. By interacting with the various components, new queries are generated (most often as SPARQL queries) to the various structWSF Web services endpoints. The result of these requests is to generate a structured results set, which includes various types and attributes.

An internal ontology that embodies the desired behavior and display options (SCO, the Semantic Component Ontology) is matched with these types and attributes to generate the formal instructions to the semantic components. These instructions are presented via the sControl component, that determines which widgets (individual components, with multiples possible depending on the inputs) need to be invoked and displayed on the layout canvas. Here is a picture of the general workflow:

Semantic Components Workflow

(click for full size)

New interactions with the resulting displays and components cause the iteration path to be generated anew, again starting a new cycle of queries and results sets. As these pathways and associated display components get created, they can be named and made persistent for later re-use or within dashboard invocations.

Consolidating and Rationalizing Web Sites and Mailing Lists

OpenStructs and Open Semantic Framework LogoAs the release of the semantic components drew near, it was apparent that releases of previous layers had led to some fragmentation of Web sites and mailing lists. The umbrella nature of the open semantic framework enabled us to consolidate and rationalize these resources.

Our first change was to consolidate all OSF-related material under the existing Web site. It already contained the links and background material to structWSF and irON. To that, we added the conStruct and OSF material as well. This consolidation also allowed us to retire the previous conStruct Web site as well, which now re-directs to OpenStructs.

We also had fragmentation in user groups and mailing lists. Besides shared materials, these had many shared members. The Google groups for irON, structWSF and conStruct were thus archived and re-directed to the new Open Semantic Framework Google group and mailing list. Personal notices of the change and invites have been issued to all members of the earlier groups. For those interested in development work and interchange with other developers on any of these OSF layers, please now direct your membership and attention to the OSF group.

There has also been a revigoration of the developers’ community Web site at It remains the location for all central developer resources, including bug and issue tracking and links to SVNs.

Actual code SVN repositories are unchanged. These code repositories may be found at:

We hope you find these consolidations helpful. And, of course, we welcome new participants and contributors!

[1] An alternative view of this layer diagram is shown by the general Structured Dynamics product stack and architecture.
Posted:July 2, 2010

As of July 1, Daily Readership Passed 3000

AI3 Blog

As of yesterday, the readership on this AI3 blog passed 3000 daily for the first time. It has been steadily inching upward, and finally passed that minor milestone. Thank you!

I’ve been writing this blog for five years now, with some 400 total posts, or about 1.5 blog posts per week. I know my style is toward longer articles and less frequent posting, most often of a fairly detailed or technical nature. And, while I have a Twitter account, I do not bleat. My style is for more meaty discussions. Perhaps it belies my age. ;)

The real growth in this blog, however, has come about with my conscious attempt to write for the enterprise audience. RDF, the semantic enterprise, linked data and ontologies need a bridge from the technical community to the one of practitioners. Much progress and uptake has been occurring with these business and government audiences.

At the recent SemTech meeting, I was taken aside by many individuals noting my blog posts and thanking me for the thought and effort behind them. Thank you for noticing, and reading, and you are welcome. We need more translation of semantic topics and technologies to pragmatic terms.

If you have been following the standard W3C and SemWeb mailing lists recently, you will have noticed an anxiety and a continuation of the fractious nature of this “community”. In part this comes about because there are efforts afoot to revisit the RDF specs. But, mostly, I think, it is the ongoing nature of many in this group to snatch defeat from the jaws of victory. The search by some for perfection and insistence on parochial needs and preferences can give a pettiness to this “community” that is unbecoming.

Many of us have abandoned those forums for those reasons. As for myself, I will continue to evangelize to the buying market and keep the gaze pointing outward. There is a wealth of need for tools, techniques, methods, documentation, structures, and narratives. Thanks to all of you, the readership of this blog, for continuing to affirm this value.

So, in the great scheme of things, the readership of this blog is quite small in comparison with the big boys. On the other hand, very few individuals have higher numbers, and all of this for a fairly esoteric area. I think this proves there is a market and a need out there for semantic solutions.

Thanks again! And, for those in the United States, have a most enjoyable 4th of July holiday!

Posted by AI3's author, Mike Bergman Posted on July 2, 2010 at 10:44 am in Blogs and Blogging, Site-related | Comments (0)
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Posted:June 17, 2010

Structured Dynamics logo
Structured Dynamics Completes Design Phase; Citizen Dan First Exemplar

Structured Dynamics has been in a fervent — and, we believe, fruitful — design phase for the past 18 months. All of the working parts related to how to embrace becoming a semantic enterprise have now been defined and designed. Actual tools and components accompany many of these parts and have been deployed.

Recently, I have been speaking and blogging much about rationale, process, mindset and approach for how to bring semantics into the organization. But, prior to now, we have not spoken much about the overall design behind our approach. Today, as we complete our design phase and introduce our first exemplar instance of it — Citizen Dan [1] — we are finally in a position to describe this overall approach.

We term our approach the open semantic framework, also OSF. The open semantic framework is a combination of a layered architecture and modular software. The open semantic framework represents the software component of the four-component total open solution, recently described in a three part series. I return to this topic in the conclusion of this post.

Revisiting Design Objectives

Over the past nine months, I have been focusing my writing largely on the semantic enterprise, with more specificity regarding our Open SEAS (Semantic Enterprise Adoption and Solutions) initiative. In bits and pieces, these writings have tended to reflect a number of objectives:

  • Leverage existing information assets (data + structure) as much as possible
  • Develop incrementally, and validate and justify as you go
  • Emphasize, where possible, open standards and open software
  • Employ Web-oriented architectures
  • Adopt an open-world approach that acknowledges that information is most often incomplete; the approach is a key enabler for incremental deployments
  • Use URIs as object identifiers, and use linked data where practical
  • Embrace any data format found in the wild, but use RDF as the ultimate integration data model
  • Design architectures and APIs that avoid “lock-in” and support multiple tools options across the stack
  • Provide systems and capabilities that put all information sources — text, media, semi-structured and conventional databases — on an equal footing
  • Promote designs that bring the ability to create useful results into the hands of users and decisionmakers; relegate IT to a support role.

To date, the result of these design objectives is perhaps best captured in my Seven Pillars of the Open Semantic Enterprise posting, as well as our general discussions regarding adaptive ontologies. Yet, still, these writings have been somewhat piecemeal. What this document attempts to do is to place all of these perspectives into a single, coherent whole.

The Incremental Layers of the Open Semantic Framework

Structured Dynamics has been a strong advocate for layered architectures, with clear APIs between layers as appropriate. But these layers are not “laminates” that completely cover the layer below, nor are they all needed or necessary. Depending on the circumstance, some layers are unneeded or superfluous. Layers may be added or not incrementally.

In this manner, then, the open semantic framework is perhaps more akin to a pearl, than to a laminate or cocoon. Each subsequent layer does not “embed” the layer prior to it, and some layers actually may inter-operate with multiple layers below or above it (this is notably true for the “ontologies” layer, which has interactions up and down the stack).

Nonetheless, we can envision this pearl of the open semantic framework and its layers as follows:

Incremental Layers of the Open Semantic Framework

(click for full size)

Others have termed this the “semantic muffin” or even “semantic muppet” or “semantic blob”. Whatever (hehe). The real idea is that layers may accrete (as in the growth of a pearl) and occur over time and be uneven. Each layer, though, does have a role to play (though it may not be needed in a given deployment), and does act to augment existing information assets in the transition to a semantic framework. Beginning at the core, each of these layers — with external references as appropriate for more details — is described below.

Existing Assets Layer

The open semantic framework is premised on leveraging existing information assets. Sure, once the framework is in place, new information can be brought into it in a more direct, semantic manner. But, the real thrust and benefit of this framework is to provide an incremental pathway for finally inter-operating and federating prior decades of data, structure and information assets.

These information assets may reside inside or outside the enterprise. They may (and DO!) exist in many formats and are described by many schema. They may come from internal transaction systems or warehouses, or may exist external on the Web or at supplier or partner sites. These information assets may span from conventional databases and relational data systems to XML interchange standards, Web pages and standard internal text or documents. In short, there is NO information asset that is not amenable to be included in this framework.

The Information Transformation (scones/irON) Layer

The information transformation layer provides either: 1) extraction of concepts and entities as structured metadata from source text or documents; or 2) conversion of existing data assets to interoperable form. As implemented by Structured Dynamics, the extractions are conducted by either scones (Subject Concept or Named EntitieS) or third-party utilities, and the conversions occur via irON (instance record Object Notation) or third-party “RDFizers“.

Depending on the source, the net result of the transformation is to produce interoperable data and information that can be ingested and used by other layers in the framework.

Though not strictly analogous, this layer bears some resemblance to the ETL (extract, transfer, load) utilities used in many enterprise information integration applications. Unlike those conventional systems, this information transformation layer also may capture and represent some of the source schema.

In all cases, however, these transformations are relatively simple and get parsed against the available structure (the ontologies, schema and entity reference lists) in the system to generate the semantic metadata (tags).

At this point, the extracted structure is generally at the level of instance records, or the ABox, with simple assertions of attribute-value pairs for specific records [2]. Little schema transformation or mapping occurs at this layer (if such is needed, that occurs at the structWSF layer; see next). Actual federation or interoperation occurs at later layers based on the TBox structures [2].

This modular portion of the framework is explicitly designed with APIs to allow third-party tools to be plugged in and substituted.

The structWSF Layer

The major workhorse of the open semantic framework is the structWSF (Web services framework) layer. structWSF is the most complicated of the OSF layers and has many supporting software packages and capabilities. The structWSF layer provides the standard, common interface (“canonical”) layer by which existing information assets get represented and presented to the outside world and to other layers in the OSF stack.

structWSF is a platform-independent Web services framework for accessing and exposing structured RDF data. Its central organizing perspective is that of the dataset. These datasets contain instance records, with the structural relationships amongst the data and their attributes and concepts defined via ontologies (schema with accompanying vocabularies; see below).

The structWSF middleware framework is generally RESTful in design and is based on HTTP and Web protocols and open standards. The current structWSF framework comes packaged with a baseline set of about twenty Web services in CRUD, browse, search and export and import. All Web services are exposed via APIs and SPARQL endpoints. Each request to an individual Web service returns an HTTP status and optionally a document of resultsets. Each results document can be serialized in many ways, and may be expressed as either RDF or pure XML. An internal representation, structXML [3], is used for internal communications across all structWSF Web services and with other layers.

structWSF has a central service that governs access rights and permissions. These rights occur at the level of the dataset, which gives immense flexibility to how data may be accessed, read, modified, created or deleted (or not). Datasets within a given structWSF instance may be accessed directly via API or via SPARQL queries to the instance’s endpoint. Depending on rights and query, results sets may be returned from a given structWSF instance in an infinite variety of ways.

This latter capability is the essential interface for subsequent layers in the open semantic framework stack. Depending on those subsequent components, pre-staged data and results sets may be returned for an essentially limitless variety of purposes.

Each structWSF instance also has a unique Web address that enables one or a multitude of instances to communicate and share with one another. This simple, but elegant, method enables structWSF instances to participate or not in potentially global or restricted local networks and collaboration environments. This is currently the largest untapped potential of structWSF with respect to its existing deployments.

The Semantic Components Layer

The newest layer in the stack is the semantic components layer. This layer takes results sets — most often generated by a specific query or data slice request — from one or more structWSF instances and then presents that information via a variety of data visualization or data presentation widgets (what we specifically call ‘semantic components‘ due to their design [4]). The operation and sensitivity of these display components are themselves driven by a presentation and data analysis (including statistics) ontology.

Current display widgets include: filter; tabular templates (similar to infoboxes); maps; bar, pie or linear charts; relationship (concept) browser; story and text annotator and viewer; workbench for creating structured views; and dashboard for presenting pre-defined views and component arrangements. These are generic tools that respond to the structures and data fed to them, adaptable without modification to any domain.

As presently implemented by Structured Dynamics, this layer consists either of Flex data visualization components or structured data display templates based on Smarty. The inherent design allows for updates to other bases (such as HTML5). The layer may also be swapped out or substituted with third-party capabilities.

The strength and power of this system is governed by its own ontology, the Semantic Component Ontology (SCO) (see next).

This is an extremely flexible layer in the open semantic framework stack. Expect an ongoing series of explanatory blog posts and online resources in the upcoming weeks to explain this innovative capability.

The Ontologies Layer

The ontologies layer actually refers to all structured assets driving the system. As such, this layer might be considered the “brain” (though rather simply specified!) of the open semantic framework.

At a true schema or TBox level [2], the ontologies layer represents the concept and relationships of the domain at hand. This layer also hosts the specific local entities and prominent things (people, places, events, etc.) useful for extracting local and domain-specific relevance. However, those views are also supplemented with some administrative ontologies (two examples are SCO and irON) that guide how the user interfaces or widgets in the system should behave.

The concept level represents the “world view” of the specific instantiation of the open semantic framework at hand. This conceptual (TBox) view provides the structural organization of information, inferencing capabilities, and navigation, faceting and explorer structure. The entity (ABox) view provides tagging for prominent individuals and instances important to the domain at hand, and guides the structure behind data visualizations of attribute or indicator data.

The administrative level uses simple roles and relationships for attributes and indicators to inform the framework as to how and with what widget to display information. For example, a “type” of information that is geographically related can be instructed to use the map component as an option for display. Whether some information is used for totals, comparison purposes, or other specifications useful to data visualization and graphing may also be specified.

The language and relationships (predicates or properties) of these administrative ontologies are simple and straightforward. It is, for example, relatively easy to define data display functions at the broad dataset and attributes level. Simple determinations drive how results sets and their associated results types may be displayed, no matter what datasets or slices may be generated as a result of the queries or requests fed to the system.

The structure in these layers can be replaced by other structures for other instantiations and circumstances. Indeed, all other layers in the open semantic framework can remain relatively fixed while tailoring the instance to new domains solely via this layer. The ontologies layer is what gives any given instantiation of OSF — such as Citizen Dan — its unique focus and scope.

The Content Management System (conStruct) Layer

The thinnest layer (that is, least substantial with respect to this framework) is the content management system (CMS) layer. In its current form, the open semantic framework uses the Drupal CMS via our conStruct plug-in modules. The design of the framework, however, has explicitly accommodated the possibility that other CMSs may substitute for this role.

The CMS layer is optional if structWSF endpoints are sufficient or if simple Web pages hosting semantic components are deemed as adequate. Very small organizations or deployments may reasonably choose to have no CMS layer at all.

However, for most sites or portals with more than a few active users, it is desirable to have broad flexibility in theming (“skinning”), user rights and permissions, or other functionality. These are the roles of the CMS layer. Drupal, for example, is presently supported by more than 4500 third-party modules in every conceivable function, from polling to blogs and rating systems and bulletin boards.

For such generalized portals or collaboration environments, it makes sense to adopt and install a flexible CMS system, such as Drupal. Much of the user experience and functional environment can be provided through such means.

The open semantic framework is thus designed to reside easily in a CMS while also providing the hooks to take advantage of the generalized user rights and functionality of the CMS. In this manner, the open semantic framework is able to stay focused on its structured data and interoperability purposes, while still gaining the advantages of rich-featured content management systems.

The OSF is a Web-oriented Architecture

With its inherent open-world orientation [5] and distributed and collaborative potential, the open semantic framework was designed from the outset to be Web-capable and Web-oriented:

Open Semantic Framework is a Web-oriented Architecture

(click for full size)

A Web-oriented architecture (WOA) has a number of understood requirements, to which the open semantic framework adheres. Specifically, these design considerations support the framework as being part of WOA:

  • Data and objects are all identified with Web addresses (URIs)
  • Data is generally exposed (and universally available) as linked data
  • SPARQL endpoints and APIs are generally RESTful in design
  • The overall architecture is modular, with inherent decentralized and distributed aspects
  • All display and visualization aspects are cross-browser ready and capable.

OSF is the Basis for Domain-specific Instantiations

Citizen Dan is our first exemplar instance of this open semantic framework. The details page for the project goes into some of Citizen Dan’s functionality and capabilities.

Citizen Dan is specifically geared to local governments and localities, with an emphasis on community indicator systems (CIS). CIS have become a popular way of measuring and tracking measures of local economic and social well-being; they are closely related to sustainability and how to measure it as used in many economic and environmental domains.

However, in the context of this post, what is really interesting about Citizen Dan is that its semantic framework is a completely open and generic one. The same set of tools and capabilities described on its details page can be applied to any domain that needs to manage and understand information in its own domain. This includes from unstructured text or documents to conventional structured databases.

What changes from domain to domain are the data structures (the ontologies, schema and entity reference lists; see above) that are fed to this open semantic framework. By swapping out new structures, what can be called Citizen Dan in one instance can morph to become Curriculum Carla in say, the education instance or Doctor Doolittle in the veterinary science instance [6].

We can illustrate these multiple instances as follows:

The Open Semantic Framework can Spawn Many Different Domain Instances

(click for full size)

What this figure illustrates is that even a branded expression of the framework — such as Citizen Dan — is merely an instance of that framework. And, actually, when expressed in such a packaged manner, we can more accurately call the standard and bundled suite of generic functions and accompanying structure of Citizen Dan as an instantiation of the open semantic framework:

in·stan·ti·ate \in-ˈstan(t)-shē-āt\ (transitive verb) is to:

  1. (transitive) to represent an abstract concept by a concrete instance
  2. (transitive, object orientated computing) to create an object (an instance) of a specific class

in·stan·ti·a·tion \in-stan(t)-shē-ā-shən\ (noun) [7]

By replacing the structure bases, and by tailoring the function suite appropriate to a given market and use, we can create many instantiations of the open semantic framework for different domains and markets. In this manner, Citizen Dan can be seen as an early exemplar of the framework, but not as a definer and limiter to it.

OSF is the Software Leg to a ‘Total Open Solution

So far, this discussion has focused solely on considerations of software and architecture. While we see the power of the open semantic framework, highly useful in itself, this is inadequate alone to achieve acceptance and success in the enterprise (as we noted in our most recent posts). The very forces that are compelling enterprises to look at new options, are also the same ones that pose difficult hurdle rates for acceptance of open source.

To address this issue, we have developed a four-legged foundation to what we termed the total open solution. The solution involves software, structure, documentation and methods (or best practices). Each of these connect and relate to the other foundations.

The open semantic framework is clearly the software (and architecture) leg to this foundation. Again, however, what is interesting is that the mere swapping out of the structure can also make the system relatively ready for other domains.

We see these relationships in the following diagram, that also shows that the DocWiki portions of the solution embody the documentation (aside from code-level comments) and methods legs of the foundation:

DocWiki is a Natural Complement to the Open Semantic Framework

(click for full size)

Differences between domains may also lead to differences as to which components are included or not in that domain’s desired instantiation.

The hugely important implied point, however, from the diagram above, is to show how nearly universal the content and methods in the DocWiki may be to other domains. Because the deltas between domains largely result from structure and what specific functional components are included or not, it becomes clear that most documentation and practices shared with the DocWiki will be applicable across domains. Sure, the use cases and some of the specific terminology may change, but we can also now see a high degree of re-usability of documentation and knowledge base across markets. This realization makes the usefulness and leverage of the DocWiki even higher.

A Common Language and Framework for Moving Forward

Developing “common language” by which to describe and convey things — especially new things like semantics that also have strong technical aspects — is tough, very tough. We are only now beginning on this process; we look to many in the community and elsewhere to help define informative and evocative terminology.

Per the original design objectives above, Structured Dynamics has approached the challenge of the semantic enterprise in what we think is both a pragmatic and a new way. The insistence on preserving and respecting existing information assets, matched with the opportunities and different mindsets arising from an open-world approach [5], have necessitated thinking through new designs and developing new concepts. Any time such new thinking and concepts occurs, new language and new metaphors must accompany it.

While certainly there are components and various software packages that populate and comprise an open semantic framework, the framework is also just as importantly a world view or way to think about information, information development, and its architecture. For example, a pivotal concept is that an open semantic framework is built around generic tools responsive to the information structures fed to them. This realization shifts the locus of emphasis from software development per se to creating, managing and adapting data and information structures. While this democratizes the information development process and is more inclusive of all knowledge workers, it also imposes needs for new toolsets and business processes. We are only at the nascent stages of understanding and learning about these differences.

Similarly, a development approach that is inherently incremental and leverages (rather than replaces or displaces) existing information assets means IT projects need to be considered in a new light. Small projects with more emphasis on tangible and demonstrable benefits will alter budgets, lower risks, and place a need for quicker turnaround. Like the architecture of the open semantic framework itself, projects based on OSF are also more distributed, decentralized and modular.

With such decentralization also comes the need for mechanisms and systems to overcome vendor “lock-in” and proprietary systems. A key thrust in support of what we have called the total open solution and its mixture of documentation and methods to accompany software and structure is specifically targeted at this issue. Tools and means for collaboration and concurrent contributions are another possible answer. Prior software practices in agile development and version control will see extensions to all manner of information development across the enterprise.

We are proud of our design work and proof-testing with clients over the past 18 months. We believe the open semantic framework and its implications to be a fundamental shift in how organizations need to think about their information development, existing information assets, and IT budgets and processes. We know widescale adoption is not yet at hand — enterprises are justifiably conservative when it comes to new thinking. But, given global competition and tight pocketbooks, the open semantic framework is a formulation to which enterprises and governments should pay very close attention.

[1] Citizen Dan is an open source system for aggregating different indicator data concerning local, community well-being. Information sources may include the Web, real-time feeds, government datasets, municipal government information systems, or crowdsourced data. Information can range from standard structured data to local narratives, including from minutes and reports, contributed stories, blogs or news outlets. The ‘raw’ input data can come in essentially any format, which is then converted to a standard form with consistent semantics. See current details with screenshots.

[2] Structured Dynamics’ best practices approach makes explicit splits between the “ABox” (for instance data) and “TBox” (for ontology schema) in accordance with our working definition for description logics, a fundamental underpinning for how we use RDF:

“Description logics and their semantics traditionally split concepts and their relationships from the different treatment of instances and their attributes and roles, expressed as fact assertions. The concept split is known as the TBox (for terminological knowledge, the basis for T in TBox) and represents the schema or taxonomy of the domain at hand. The TBox is the structural and intensional component of conceptual relationships. The second split of instances is known as the ABox (for assertions, the basis for A in ABox) and describes the attributes of instances (and individuals), the roles between instances, and other assertions about instances regarding their class membership with the TBox concepts.”
[3] A subsequent post will document this rather straightforward XML schema.
[4] Contact Structured Dynamics for a early sneak peek. The Citizen Dan application will be publicly released as an online sandbox and demo by the end of summer 2010.
[5] See M. K. Bergman, 2009. The Open World Assumption: Elephant in the Room, December 21, 2009. The open world assumption (OWA) generally asserts that the lack of a given assertion or fact being available does not imply whether that possible assertion is true or false: it simply is not known. In other words, lack of knowledge does not imply falsity. Anothe way to say is it that everything is permitted until it is prohibited. OWA lends itself to incremental and incomplete approaches to various modeling problems.
[6] Of course, things are always not so simple as this. The CMS layer gives the open semantic framework the ready ability to change themes and layouts (“skins), not to mention the breadth and specifics of what ancillary site functionality might be provided. Moreover, the module basis of the open semantic framework also means that entire clusters of functionality might be dropped from a given instantiation (or added to it!) without violating or negating this framework.
[7] Dictionary references are from Merriam-Webster and Wikitionary.
Posted:June 11, 2010

How Shall We Measure Progress Over the Past Three Years?

Friday     Brown Bag Lunch
Colorado  Interstate construction - 1970; courtesy National ArchivesFor a dozen years, my career has been centered on Internet search, dynamic content and the deep Web. For the past few years, I have been somewhat obsessed by two topics.

The first topic, a conviction really, is that implicit structure needs to be extracted from Web content to enable it to be disambiguated, organized, shared and re-purposed. The second topic, more an open question as a former academic married to a professor, is what might replace editorial selections and peer review to establish the authoritativeness of content. These topics naturally steer one to the semantic Web.

A Millennial Perspective

The semantic Web, by whatever name it comes to be called, is an inevitability. History tells us that as information content grows, so do the mechanisms for organizing and managing it. Over human history, innovations such as writing systems, alphabetization, pagination, tables of contents, indexes, concordances, reference look-ups, classification systems, tables, figures, and statistics have emerged in parallel with content growth [19].

When the Lycos search engine, one of the first profitable Internet ventures, was publicly released in 1994, it indexed a mere 54,000 pages [1]. When Google wowed us with its page-ranking algorithm in 1998, it soon replaced my then favorite search engine, AltaVista. Now, tens of billions of indexed documents later, I often find Google’s results to be overwhelming dross — unfortunately true again for all of the major search engines. Faceted browsing, vertical search, and Web 2.0′s tagging and folksonomies demonstrate humanity’s natural penchant to fight this entropy, efforts that will next continue with the semantic Web and then mechanisms unforeseen to manage the chaos of burgeoning content.

An awful lot of hot air has been expelled over the false dichotomy of whether the semantic Web will fail or is on the verge of nirvana. Arguments extend from the epistemological versus ontological (classically defined) to Web 3.0 versus SemWeb or Web services (WS*) versus REST (Representational State Transfer). My RSS feed reader points to at least one such dust up every week.

Some set the difficulties of resolving semantic heterogeneities as absolutes, leading to an illogical and false rejection of semantic Web objectives. In contrast, some advocates set equally divisive arguments for semantic Web purity by insisting on formal ontologies and descriptive logics. Meanwhile, studied leaks about “stealth” semantic Web ventures mean you should grab your wallet while simultaneously shaking your head.

A Decades-Long Perspective

My mental image of the semantic Web is a road from here to some achievable destination — say, Detroit. Parts of the road are well paved; indeed, portions are already superhighways with controlled on-ramps and off-ramps. Other portions are two lanes, some with way too many traffic lights and some with dangerous intersections. A few small portions remain unpaved gravel and rough going.

1919 Wreck in Nebraska

Wreck in Nebraska during the 1919 Transcontinental Motor Convoy

A lack of perspective makes things appear either too close or too far away. The automobile isn’t yet a century old as a mass-produced item. It wasn’t until 1919 that the US Army Transcontinental Motor Convoy made the first automobile trip across the United States.

The 3,200 mile route roughly followed today’s Lincoln Highway, US 30, from Washington, D.C. to San Francisco. The convoy took 62 days and 250 recorded accidents to complete the trip (see figure), half on dirt roads at an average speed of 6 miles per hour. A tank officer on that trip later observed Germany’s autobahns during World War II. When he subsequently became President Dwight D. Eisenhower, he proposed and then signed the Interstate Highway Act.

That was 50 years ago. Today, the US is crisscrossed with 50,000 miles of interstates, which have completely remade the nation’s economy and culture [2].

Today’s Perspective

Like the interstate system in its early years, today’s semantic Web lets you link together a complete trip, but the going isn’t as smooth or as fast as it could be. Nevertheless, making the trip is doable and keeps improving day by day, month by month.

My view of what’s required to smooth the road begins with extracting structure and meaningful information according to understandable schema from mostly uncharacterized content. Then we store the now-structured content as RDF triples that can be further managed and manipulated at scale. By necessity, the journey embraces tools and requirements that, individually, might not constitute semantic Web technology as some strictly define it. These tools and requirements are nonetheless integral to reaching the destination. We are well into that journey’s first leg, what I and others are calling the structured Web.

For the past six months or so I have been researching and assembling as many semantic Web and related tools as I can find [3]. That Sweet Tools listing now exceeds 500 tools [4] (with its presentation using the nifty lightweight Exhibit publication system from MIT’s Simile program [5]). I’ve come to understand the importance of many ancillary tool sets to the entire semantic Web highway, such as natural language processing and information extraction. I’ve also found new categories of pragmatic tools that embody semantic Web and data mediation processes but don’t label themselves as such.

In its entirety, the Sweet Tools listing provides a pretty good picture of the semantic Web’s state. It’s a surprisingly robust picture — though with some notable potholes — and includes impressive open source options in all categories. Content publishing, indexing, and retrieval at massive scales are largely solved problems. We also have the infrastructure, languages, and (yes!) standards for tying this content together meaningfully at the data and object levels.

I also think a degree of consensus has emerged on RDF as the canonical data model for semantic information. RDF triple stores are rapidly improving toward industrial strength, and RESTful designs enable massive scalability, as terabyte- and petabyte-scale full-text indexes prove.

Powerful and flexible middleware options, such as those from OpenLink [6], can transform and integrate diverse file formats with a variety of back ends. The World Wide Web Consortium’s GRDDL standard [7] and related tools, plus various “RDF-izers” from Massachusetts Institute of Technology and elsewhere [8], largely provide the conversion infrastructure for getting Web data into that canonical RDF form. Sure, some of these converters are still research-grade, but getting them to operational capabilities at scale now appears trivial.

Things start getting shakier when trying to structure information into a semantic formalism. Controlled vocabularies and ontologies range broadly and remain a contentious area. Publishers and authors perhaps have too many choices: from straight Atom or RSS feeds and feeds with tags to informal folksonomies and then Outline Processor Markup Language [9] or microformats [10]. From there, the formalism increases further to include the standard RDF ontologies such as SIOC (Semantically-Interlinked Online Communities), SKOS (Simple Knowledge Organizing System), DOAP (Description of a Project), and FOAF (Friend of a Friend) [11] and the still greater formalism of OWL’s various dialects [12].

If we compare the semantic Web to the US interstate highway system, we’re still in the early stages of a journey that will remake our economy and culture.
Many potholes on the road to the semantic Web exist.
One ready task is to transform existing structure to RDF. Another priority is to refine tools to extract structure and meaningful information from uncharacterized content.

Arguing which of these is the theoretical best method is doomed to failure, except possibly in a bounded enterprise environment. We live in the real world, where multiple options will always have their advocates and their applications.

All of us should welcome whatever structure we can add to our information base, no matter where it comes from or how it’s done. The sooner we can embrace content in any of these formats and convert it into canonical RDF form, we can then move on to needed developments in semantic mediation, some of the roughest road on the journey.

Potholes on the Semantic Highway

Semantic mediation requires appropriate structured content. Many potholes on the road to the semantic Web exist because the content lacks structured markup; others arise because existing structure requires transformation. We need improved ways to address both problems. We also need more intuitive means for applying schema to structure. Some have referred to these issues as “who pays the tax.”

Recent experience with social software and collaboration proves that a portion of the Internet user community is willing to tag and characterize content. Furthermore, we can readily leverage that resulting structure, and free riders are welcomed. The real pothole is the lack of easy — even fun — data extractors and “structurizers.” But we’re tantalizingly close.

Tools such as Solvent and Sifter from MIT’s Simile program [13] and Marmite from Carnegie Mellon University [14] are showing the way to match DOM (document object model) inspectors with automated structure extractors. DBpedia, the alpha version of Freebase, and System One now provide large-scale, open Web data sets in RDF [15], including all of Wikipedia. Browser extensions such as Zotero [16] are showing how to integrate structure management into acceptable user interfaces, as are services such as Zoominfo [17]. Yet we still lack easy means to design the differing structures suitable for a plenitude of destinations.

Amazingly, a compelling road map for how all these pieces could truly fit together is also incomplete. How do we actually get from here to Detroit? Within specific components, architectural understandings are sometimes OK (although documentation is usually awful for open source projects, as most of the current tools are). Until our community better documents that vision, attracting new contributors will be needlessly slower, thus delaying the benefits of network effects.

So, let’s create a road map and get on with paving the gaps and filling the potholes. It’s not a matter of standards or technology — we have those in abundance. Let’s stop the silly squabbles and commit to the journey in earnest. The structured Web‘s ability to reach Hyperland [18], Douglas Adam’s prescient 1990 forecast of the semantic Web, now looks to be no further away than Detroit.

Friday      Brown Bag Lunch This Friday brown bag leftover was first placed into the AI3 refrigerator about three years ago on May 3, 2007.  The piece was my answer to a request by Jim Hendler to pen some thoughts on the semantic Web, based on I believe what he thought might be a pragmatic perspective combining Internet business with Web science. The formal piece appeared as a guest editorial in the May/June 2007 issue of IEEE Intelligent Systems. What appears above is unaltered from my original posting (aside from some minor formatting clean-up and — sorry to say — some of the projects are now defunct).

[1] Chris Sherman, “Happy Birthday, Lycos!,” Search Engine Watch, August 14, 2002. See
[2] David A. Pfeiffer, “Ike’s Interstates at 50: Anniversary of the Highway System Recalls Eisenhower’s Role as Catalyst,” Prologue Magazine, National Archives, Summer 2006, Vol. 38, No. 2. See:
[3] The mention of specific tool names is meant to be illustrative and not necessarily a recommendation.
[6] OpenLink Software’s Virtuoso and Data Spaces products; see
[7] W3C’s Gleaning Resource Descriptions from Dialects of Languages (GRDDL, pronounced “griddle”). See
[9] Outline Processor Markup Language (OPML); see
[10] Microformats; see
[12] W3C’s Web Ontology Language (OWL). See
[13] Solvent ( and Sifter ( are from MIT’s Simile program.
[14] Marmite ( is from Carnegie Mellon University.
[15] DBpedia ( and Freebase (in alpha, by invitation only at are two of the first large-scale open datasets on the Web; Wikipedia has also been converted to RDF by System One (
[16] Zotero is produced by George Mason University’s Center for History and New Media; see
[17] ZoomInfo ( provides online structured search of companies and people, plus broader services to enterprises.
[18] The late Douglas Adams, of Doctor Who and A Hitchhiker’s Guide to the Galaxy fame, produced a TV program for BBC2 presaging the Internet called Hyperland. This 50-min video can be seen in five parts via YouTube at Part 1 of 5, 2 of 5, 3 of 5, 4 of 5 and 5 of 5.
[19] Since I first wrote this piece, I have systematized these developments in my Timeline of Information History.