The Semantic Advantage

September 16, 2009

Using circles and arrows

Many “semantic” practices and applications — including “brainstorming” and construction of computer ontologies — involve the use of (a) circles or other symbols (“nodes”) to represent concepts or ideas and (b) arrows (connecting arcs or “edges”) to represent the relationships among the concepts or ideas.

(Tim Berners-Lee uses the phrase “circles and arrows” in at least one of his papers: “The Semantic Web starts as a simple circles-and-arrows diagram relating things, which slowly expands and coalesces to become global and vast.” in “The Semantic Web lifts off” by Tim Berners-Lee and Eric Miller. ERCIM News, No. 51, October 2002. http://www.ercim.org/publication/Ercim_News/enw51/berners-lee.html. His original vision is for metadata for documents.)

The graphic representation is not the tool itself in some cases, but a method of helping users visualize and/or manipulate complex, abstract data that is difficult for the average human to understand quickly — for example, RDF expressed in XML.

Mapping arguments on a whiteboard in support of decision making is a common practice in many meetings. (But integrating those representations into subsequent discussions is almost always a challenge.)

We need a much better and more widely usable set of tools for such purposes, but just applying current, limited tools is useful in its own right. One thing you definitely begin to understand as you try to deconstruct your arguments into meaning — especially when using graphical tools for that purpose — is that the process itself is useful in getting to meaning.

The process is useful in exposing what is tangential, peripheral or simply irrelevant. You tend to create and refine elemental, focussed, unambiguous assertions that can be verified as true or debunked.

You certainly expose conditions and constraints that apply to those assertions. Sweeping generalizations quickly become far less general … but often more useful. And you find that most of what you have written is not part of the core meaning that you want to represent and transfer.

That process, however, is still not easy. And you need to have a set of guidelines to keep yourself on track.

I will explore some of the tools and issues in this area in future posts.

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March 26, 2009

Mills Davis’ “Web 3.0 Manifesto”

Filed under: semantic technology — Phil Murray @ 7:10 pm
Tags: ,

A few weeks ago, Mills Davis offered me an evaluation copy of his “Web 3.0 Manifesto: How Semantic Technologies in Products and Services Will Drive Breakthroughs in Capability, User Experience, Performance, and Life Cycle Value.”

I jumped at the opportunity, because Davis is one of those rare intelligences who can get his arms around complex market and technology trends, providing substantive new information and helpful perspective at the same time. A friend accused him of being “too far ahead of the curve,” but I’d love being insulted like that from time to time.

In this dense 32-page report, Davis

  • Differentiates semantic (“Web 3.0”) technologies from “Web 1.0” (connecting information) and “Web 2.0” (social computing) phases.
  • Describes the link between semantic technologies and generation of value.
  • Provides a graphic representation of semantic technology product and service opportunities broken down into 70 discrete “elements of value.” Each opportunity is described in the text. Some random examples: visual language & semantics, semantic cloud computing, and collective knowledge systems.
  • Assesses general market readiness for semantic technologies.
  • Lists over 300 “suppliers” (“research organizations, specialist firms, and major players”) in the semantic technologies space.

What does “Web 3.0” represent?

According to Davis, Web 3.0 is starting now. “It is about representing meanings, connecting knowledge, and putting these to work in ways that make our experience of internet more relevant, useful, and enjoyable.”

What do “semantic solutions” include, according to Davis? Well, pretty much everything that isn’t structured data in the traditional sense. That’s not unreasonable, if you accept — as I do — that if you are dealing with meaning and you believe that everything is connected and meaningful, then it’s really hard to avoid semantics. And I will, once more, quote the simple but extraordinarily astute observation of Aw Kong Koy: “You can’t manage what you can’t describe.”

You may think you’re new to “semantic technologies” but you’re not. If you’re reading this, you probably use and understand relational databases. You may actually design them. And if you do, you have engaged in a form of semantic modeling for business requirements. In fact, as fellow CSE member Samir Batla (See Batla’s Semanticity blog.) observes, the idea of relational databases and the Semantic Web’s Resource Description Format (RDF) both have roots in first-order logic.

This “semantic” thing is simple, really: It’s the necessary solution to having too much information and too little time to consume it. Engineers get it. Just hand me the schematic! You can talk all you want about principles of product or building design — or even about a specific product — but I want to see how, exactly, Tab A fits into Slot B. I want the realities expressed explicitly … and in a consistent way. Tab A doesn’t fit into Slot B until that happens.

The heart of semantic technologies: knowledge representation

It’s simple, really. But that doesn’t mean it’s easy, because we’re dealing with one of the most difficult challenges facing business and computing: representing knowledge. The domain of knowledge representation has been with us for a while, and in his Manifesto, Davis clearly asserts that it is the rock on which semantic technologies rest: “In Web 3.0, knowledge representation (KR) goes mainstream. This is what differentiates semantic technologies from previous waves of IT innovation.”

But we do have to distinguish between (a) KR in the broad sense of representing [common sense] reality — as targeted by the massive Cyc ontology, for example — and (b) the practical and quite limited representations of reality that are and will be used for most business applications in the next few years, in which the representation (typically, perhaps, an ontology or simply an RDF resource) enables applications to understand each other in better (but still limited) ways by referencing a common/shared “understanding” of a narrow domain.

Sometimes the product of a KR project is a life’s work, as Cyc has been for Doug Lenat. At other times, it is much more modest — little more than normalizing and organizing a small part of a domain’s vocabulary.

The core graphic

The core graphic of Davis’ Manifesto (“Web 3.0 Semantic Technology Product and Service Opportunities”) is a quadrant of functions that follows the AQAL model — interior vs. exterior and individual vs. collective axes. (See, for example, Completing the AQAL model: Quadrants, states and types.) This quadrant-based arrangement of semantic applications is actually quite useful in getting a handle on the possible dimensions of semantic solutions, but — in spite of Davis’ high-level descriptions of each area — it doesn’t eliminate the need for more structured explanations of the application areas … let alone validate their existence. (And I’m definitely not ready to commit to the Holon/AQAL perspective on the world.)

Quadrants aside, the core objection from some corners will be that Davis includes activities and solutions that are not drawn from the Semantic Web. Well, I have two responses to that: (1) Davis is absolutely right to talk about more than the Semantic Web and (2) some distinguished folks in the semantic community — which existed long before the Semantic Web — have expressed resentment that academic inquiry into semantic approaches is increasingly limited to the Semantic Web brand. I can’t verify that this is the case; I’m just reporting what has been written by a few experts.

If I have an objection, it is that applying such broad labels to the many real and possible areas of semantic activity in business may contribute to further “siloing” of applications, one of the business problems that semantic approaches should actually help solve. Everybody wants to be a specialist, but this is a time for semantic generalists. And a semantic infrastructure should enable (useful) deconstruction of conventional models for business processes, technology, and creation of value, especially in knowledge work. (Take that, Mills! I can speak high-level, too!)

Another surface criticism: Just putting the word semantic in front of current work practices and technologies does not mean they do or will exist, at least by those names. Let’s not get too far ahead of ourselves with this labelling thing. It’s reminiscent of early (mid 1990s) pontifications on knowledge management, in which one well-known KM “guru” opined a need for “knowledge reporters” and other gurus raced to assert the need for “knowledge engineers.” Well, it turns out that several existing, widely known professions (including, but not limited to, systems analysts and technical writers) were already filling that “knowledge reporter” gap. And “knowledge engineers” had been around for a long time building expert systems. The news of a sudden new need for their job title was a bit of a surprise to them.

Recommendation: Go get it

Mills Davis’ dense, sweeping, high-level look at the promise of “semantic solutions” will open your eyes, give you pause for thought, and make your brain hurt. Each sentence requires — and deserves — careful parsing. And it will at times make you go “Huh?”

Manifestos are like that, I guess. But better your brain should hurt every once in a while than simply be filled up with comfortable fluff.

July 9, 2008

Normalizing ideas

Filed under: semantic technology — Phil Murray @ 2:37 pm
Tags: ,

The relational database model rests on the basic principle of normalization of data.

Semantic technology approaches need to apply this principle, too. Not just at the level of concepts but also — and perhaps just as importantly — at the level of ideas. By ideas I mean complex expressions or assertions about reality, like “Our opportunity in the marketplace is to apply IBM’s UIMA to unstructured information in the enterprise.”
The “truth” of that assertion is obviously critical to the success of a company in that business. However, even if such assertions can be specified in a theory of meaning (like an ontology language), it’s not clear that it can be asserted to be true by any means other than the consensus of experts.

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