I am hung up on a concern about the application of text mining to scientific discovery from which I seem unable to shake free. That simple hang-up is due to the importance of visual analogy to scientific discovery and the rather trivial or secondary narration that follows it. That narrative content (see narrative fallacy – explaining an event post hoc so that it will seem to have a cause) is the very material that text mining seeks to leverage. Language is supposed to capture in some way the network of causes, many of them supposedly sufficient to help presage novel treatments, procedures, further explanations, and so on. But if the generative seed of discovery is visual analogy itself, no amount of linguistic-based reasoning, whether contextual, deductive, or inductive, can ever make new discoveries. Because the explanation is not equivalent to the image.

And yet. And yet we know that we can make discoveries by deductions from multiple texts, as Don Swanson has repeatedly shown us. But Swanson’s discoveries using disjoint literatures are marginal and hypothetical and remain in desperate need of empirical review. Disjoint literatures don’t appear to be radically increasing the speed at which scientific discovery is made, which means that the process of leveraging implicit multi-document logics is missing something essential.

I’ll venture a guess and say that pictures are missing.

If a picture is worth a thousand words, is the relation symmetric? That is to ask, given a thousand words, can we draw a picture? Could we, say, use hypothesis generation to augment the creation of visual metaphor apparently crucial to scientific discovery? Alternately, it seems that a picture is not inherently worth any word whatsoever, and that inequivalence is symmetric.

Most pictures generated these days via automated means are entirely dimensionless, metaphorically speaking. Graphs, trees, constellations of points in a space. But what makes our understanding of constellations rich? Ahh yes, those stars in our southern summer sky appear to look like a scorpion, become known as Scorpius, and that’s how we remember those specks, and that’s how we use them as well. Memory, after all, is inseparable from use. And yet those stars are no more a scorpion than a snake or a lock of hair or whatever else you can make up.

So it’s not enough perhaps to plot networks on a 2D screen. Why not compare those assemblages of seemingly random points to visual shapes? Why not revtrieve the visual metaphor for an item automatically?

This however is utterly unconvincing. There’s no way, for example, special relativity could be arrived at in such a way. And yet, hold on just a sec, elements of the discovery of special relativity are in part a result of a visual search activity–Einstein imagining many rich ways of illustrating previous mathematical expressions and testing the illustrations to measure their utility, their usability, their ability to survive multiple looks and provide a rich metaphor capturing the scientific phenomenon. And then using those images to tell further stories, and then usiong those stories to generate more mathematical expressions. A picture is worth a thousand words and a thousand words is worth many pictures.

For my master’s thesis I performed a case study of a very large multinational drug company to evaluate how it innovates in text mining to drive its central mission of drug innovation. Drug discovery is hard and therefore expensive, but with high performance computing now a commodity, drug companies should be at the bleeding edge of text mining innovation, particularly in the area of virtual hypothesis formation and testing (deriving novel insights from mining multiple inputs, from clinical data stores to genetics databases to research literature collections and even the so-called grey literature). But guess what? At least with respect to the case I studied, they aren’t. They are highly focused on circa-1997 extraction tasks with little to no interest in statistical learning and a confused interest in taxonomies and automated inductive reasoning. They invest in formal logics and in information extraction but the meat in the middle, the statistical learning, is kept strictly to data mining of data sets severly limited in scope. Simply put, the company has little to no coherent and well-articulated vision of how it can tackle its most daunting problem for drug discovery: information overload.

How can this problem arise? Isn’t the central mission of a drug company, its core competency, to create new drugs? Well, historically it has been. But competing with the core competency of the drug company is another, oft conflicting, central mission, to make money. What this means for drug discovery is that it is only kind of important. The company I studied was laying off key drug innovators globally as it was focusing its investments further down the drug pipeline, placing more and more emphasis on Phase 2 & 3 projects, more on lower risk short term gains. What this means is that the central mission has become, to get drugs to market, particularly ones with a recurring revenue model.

Historically the drug companies could hang their hats on introducing drug treatments that have contributed to huge improvements in human health over the last century. Drug companies have been in the business of saving lives. Drugs are largely responsible for the 50%+ increase in life expectancy in the US over the last century.

Sometimes, however, human health improvements are not profitable. Sometimes drug companies will select strategies far less beneficial to human health that are far more financially beneficial to the organization. Consider the focus on marketing deregulation in the US, or FDA deregulation. Why invest in developing drugs when you can invest in removing barriers to sales? Now that deregulation has just about all but run its course, drug companies will soon face the fact that they will need to depend more and more on releasing new drugs. When the two largest drug companies in the world can’t combine for more than a dozen new drugs in any given calendar year, you can tell that something’s clearly broke. You can’t hang the shortage on regulation or on a shortage of actionable research.

So why the institutional emphasis away from innovation? One can only speculate; I will use Portfolio Theory to speculate. The dominant forces controlling large multinational drug companies are people of a certain kind, namely, aging investors. They have invested their dollars and expect something in return. Portfolo Theory tells us that our optimal investment trend as we age is to go from high risk to lower risk, income-generating investment. For example, I’m 35, and if I expect to live to, say, 80, I’m probably at least three decades from retirement, from a time where I need my investments to generate income. Because I have decades to invest, I can handle the risk of higher risk investments, namely because I don’t need the reliable income, and because I have time to recover if I lose. The game of investing depends entirely on how much time you perceive yourself as having, namely because on your death bed all the money in the world is worth nothing, but having a lot of cash on hand that last week before your death bed is pretty damned important. A promise for a check next week won’t do you good if you’re dead. And so I think, unlike me, the investors in large mutinationals are old men, frankly. They need to allocate their assets on the income-generating end of the spectrum. They need that cash and they need it now.

This asset allocation model is confirmed by the reduced interest in technological innovation and the increase of interest in being merely early adopters. Adopting established technologies carries a lower risk, as it has a higher probability of some payoff.

And so why invest in high risk, in innovation? The argument for it would be three-fold: to attract younger investors, to focus on the longevity and long-term stability of the company, and to be true to the core mission, which should be to treat health ailments. Maybe my experience is anecdotal, but at least to me it appears that investors in my parent’s generation (they’re 65) are far more likely to invest in, say Pfizer or GSK, than investors my age. They’re safe, they’ll do OK next quarter, but that picture is very murky a decade from now. Not to mention that younger investors no longer see drug companies as beneficial to human health. There’s nothing attractive for the average younger investor.

One of the saddest consequences of this reluctance to innovate, this focus on profit, is the impact on human health. Drug companies are far more willing to repackage old drugs and market the heck out of them, renewing their proprietary charges, than to find new drugs. And when the drug companies choose new drugs to invest in, they are going to look for “comeback” drugs, drugs that cure nothing but treat indefinitely. No new antibiotics are reaching market because there’s no incentive

I haven’t posted in six weeks.  It’s not because I have nothing to write about.  Rather it’s that I have been extraordinarily busy, mostly in my search for employment.  Fortunately and I do mean fortunately it looks as if the search is drawing to a close.  I’m ready to put my shoulder to the wheel for the right people, the right project.  It’s much harder to find a job in numerous ways when trying to be selective.  It is apparent to me now that, in addition to testing my fortitude, it will pay in the long run.  I hope to share more information as my hunt officially comes to an official resolution.  Namely because I am bubbling with excitement over it.

What I can say is, is that the future is here.  I mean really here.  I mean it in more ways than one.  As in, the technology of the future is now here, and it can bring more of the future into the present.  That means the future is *really* here.

The following comprises a collection of my intuitions and “big picture” insights resulting from graduate study focused on text mining at SILS. These are insights related to feature representation, knowledge engineering, model building, the application of statistics to real-life phenomena, and the greater whole of information science.

Many of these apparently go without saying, yet so many discussions of supposed problems would go away if some of these observations were made explicit. This is my attempt to make them explicit. Maybe it goes without saying that expressing the obvious is sometimes quite necessary.

1. Statistical models often fail because they’re missing key attributes necessary to describe the phenomena they represent

Attributes that are altogether unrecognized, difficult to quantify, difficult to analyze, truncated out, or simply forgotten arguably dominate and confound the predictive/explanatory power of statistical models. These missing variable abound. Their absence dominates to the point where theory itself must give way to empiricism and its sister, skepticism. It also means that we simply don’t see everything and that it never hurts to try and see more things.
2. Feature reduction of highly dimensional linguistic data sets is a misguided, outdated and counterproductive approach

There. I said it.

Claude Shannon’s model of information as that which is located among noise is a metaphor that appears to have been misleading a number of people in information science, particularly those involved with anything even remotely tangential to text mining (or, if you must, “knowledge discovery”). Information in an atomic form (e.g., bits) allows for the differentiation of signal and noise. A bit either is a signal or it isn’t. Attributes of real-life phenomena (e.g., average first down yardage in football for a team) are not like bits, at least not in the way we experience them and interpret those phenomena, whether in written explanations or in databases. “Real-life” phenomenae comprise different sorts of real-world features that can never be honestly reduced to their atomic constituents. And, pragmatically speaking, they won’t be reduced to quantum atomic states any time soon.

Given that every attribute of real-world phenomenae we identify partakes of both signal and noise, the removal of any attribute (save for the case of redundancy) always corresponds to the loss of information. Ultimately the statistical modeling of phenomena such as competitive sports and stock markets and clinical emergency room chief complaints is wholly unlike modeling communication channels. There’s something immediately discontinuous about binary electronic signals while other these other phenomena need dramatic interpretive steps before they can be represented with discontinuous electronic signals. Finally, signal and noise are terms that don’t apply very well because that which we are modeling can only be realistically described by features that are both informative and misleading at the same time.

There’s something rather continuous about language (something that latent semantic indexing attempts to capture) and that even the simplest of approaches, such as applying stop word lists to bag of word representations, lost critical information that dictate the semantics of the document. “Dog,” “a dog” and “the dog” quite clearly mean different things, as do “of the dog”, “out of a dog” and so forth. Representing all of those quotations as “dog” or going a step further and representing all of these quotes with the very same word-sense identifier, dumbs down human language beyond recognition. Garbage in, garbage out is a phrase I learned more than a quarter century ago when learning to program games for the Commodore Vic-20.

Reading a text book from 1993 on the C4.5 algorithm, I came across reflections that some crucial elements of C4.5 appeared to be motivated by economizing on computer resource issues. Not enough memory, too slow of processing, etc. In 2007 high performance computing is a commodity. The pressures for feature reduction in machine learning needed to be heeded 14 years ago, but they’re considerably less of an issue today.

Finally, at the very end of my stretch of graduate school studies I accidentally came across a new strategy for feature representation that is so painfully obvious in retrospect it leaves me wondering why no one else has been doing this. Fortunately for Hypothia it spells one very big competitive advantage. But I digress.

3. There’s always something missing from your set of attributes (cf. 1 & 2)

4. There’s no substitute for knowing your data set (cf. 1)I credit this oft-neglected, oft-devalued approach to my first and truly excellent data mining instructor, Miles Efron, who may be to blame for turning me on to text mining in the first place. What have you wrought? He made sure to repeat this lesson of knowing thy data a few times, and the lesson was surely not lost on me. In fact it seems as it it frames and justifies my confidence in my approach.

5. [DELETED] and let your algorithms optimize your attributes for maximal classification margin (cf. 2 & 3)

Can’t say the deleted part yet. But I will, eventually. It probably should be obvious by now. But still I’m not prepared to say.

6. SVM+SMO is very good for binary classification of highly dimensional data (cf. 5)

Improvements to SVM+SMO are always welcome of course, and it appears there are now numerous implementations of SVM that improve. I should note that, according to Eibe Frank, SMO in Weka (written in Java) is just as fast as Joachims’ SVM-light written in C. SMO’s pretty good.

SMO solves the QP problem created by SVM efficiently.

7. You always need more computing power (cf. 2, 5 & 6)

The curse is not dimensionality, the curse is not intellectual. The curse is economic, a problem of resources.
Likely it will be difficult to produce a dataset that is intractable for a good HPC setup running SVM+SMO but it doesn’t exactly hurt to try as long as you’re trying to harness more and more power.

8. You don’t know everything (cf. 3 & 4)

9. models only forecast well in forecast-influenced environments only when the model has an information advantage over other models (information assymetry, competitive advantage)

10. You’ll never get it quite right ( cf. 8 )

11. There always more left to do (cf. 5, 7 & 10)

12. Disambiguation can be better pursued not in any pure sense by machinic strategies but rather by messier approach of utilizing the greater context surrounding term, document, and corpus, which in turns permits some degree of ambiguity, which is necessary for understanding

13. Word sense disambiguation is quite possibly the wrong way to go to conjure semantics in one’s text representation (cf. 2 & 12)

As I’ve written before, there are other approaches available to leverage semantic information that are better than word-sense diambiguation (WSD) .

14. More formally, the incorporation of ambiguity into linguistic representations (i.e, representing all possible word senses/meanings and POSs for any given word) allows for better representations of intelligence than ones produced at least in part through WSD strategies

15. For artificial intelligence to become smarter than humans, it must at least be as smart as humans first.  A person’s ability to understand multiple senses of a given word at once (of which poetry is perhaps the most striking example) is strikingly intelligent and far more intelligent than most WSD approaches I’ve seen (cf. 14).  And when you consider that the basic unit of meaning is truly not the word but the sentence, WSD seems all the more foolish, and yet makes me feel there’s a huge opportunity to understand language from its wholes and holes.  Discourse analysis anyone?

16. Not knowing everything, not always getting it right, and always having more left to do makes the hard work a great deal of fun. Discoveries are everywhere waiting to be written into existence. (cf. 8, 10, 11)

17. Don’t panic, be good, and have fun. (cf. 16)

18. The essence of human language is nothing less than the totality of the human language in all of its past present and future configurations and possibilities.

The goals of Hypothia are to integrate social feeds, capture embedded ad delivery, reinvent authentic weblogs, capture viral blogospheres, and harness A-list podcasts.

If you think that my last sentence was utterly full of nonsense, you’re right.

Specifically, I’m full of the Web 2.0 Bullsh*t Generator. It’s worth a laugh. And, of course, it’s in Beta, naturally.

1. Introduction
Pharmacogenomics experts have recognized that genomics-based approaches to drug discovery appear to suffer from some sort of information overload problem
(A. D. Roses, Burns, Chissoe, Middleton, & Jean, 2005, p. 179). More specifically, the explosion of human genomics information may have been outpaced by a concurrent explosion of noise within that data, leading to a significant attrition rate in the pharmaceutical pipeline (A. D. Roses et al., 2005, p. 179). However, it is not entirely clear how the concepts of information overload and signal-to-noise apply to information-based struggles in pharmacogenomics. In order to improve our understanding of the barriers to optimal use of pharmacogenomics information for drug discovery purposes we must first briefly unpack competing ideas about information overload and signal-to-noise and then contextualize the appropriate ideas within PGx-based drug discovery (henceforth PGx-DD).

2. Explaining Too Much Information in PGx-based Drug Discovery: Information Theory or Information Overload?

Genomics research pioneer and GSK Senior VP for Genomics Research Allen Roses has recently shed light on why pharmacogenomics-based approaches may not be optimal. According to Roses, who arguably is in a unique position to understand the problem, the central problem is one arising from information struggles. Roses writes,

What factors have limited target selection and drug discovery productivity? Although HTS technologies were successfully implemented and spectacular advances in mining chemical space have been made, the universe for selecting targets expanded, and in turn almost exploded with an inundation of information. Perhaps the best explanation for the initial modest success observed was the dramatic increase in the ‘noise-to-signal’ ratio, which led to a rise in the rate of attrition at considerable expense. The difficulty in making the translation from the identification of all genes to selecting specific disease-relevant targets for drug discovery was not realistically appreciated (A. D. Roses et al., 2005, p. 179).

What Roses calls the “noise-to-signal” ratio sounds like the problem of information overload, yet it also sounds as if it borrows from the language of Information Theory as put forth by Claude Shannon. Roses’ insight seems to corroborate Sean Ekins’ observation that already-extant data is not optimally utilized (2005). Pharmacogenomics is failing to deliver because PGx researchers and organizations utilizing PGx research have been unable to meet the information challenges concomitant with the explosion of data.

The language Allen Roses uses to describe struggles with information in the field of PGx-based drug discovery refers both to a signal-to-noise ratio and to information overload. The terminology appears, however, to be rather ambiguously utilized in the context of PGx-DD. “Noise-to-signal” seems to refer to Claude Shannon’s mathematical theory of communication (Shannon & Weaver, 1949) while the problems described by PGx professionals sound more like cognitive issues related to more formal notions of information overload.

2.1.Shannon’s Mathematical Theory of Communication
In 1948, Claude Shannon of Bell Labs completed work on his mathematical theory of communication. For so doing, Shannon is credited as fathering the field of Information Theory. It is from Shannon’s theory that the notion of signal-to-noise arises, among many other concepts crucial to any understanding of information. In his introduction to the ensuing book publication comprising Shannon’s work on the theory, Warren Weaver explains that the theory was supposed to deal with three distinct levels of communications problems, as follows:

Level A. How accurately can the symbols of communication be transmitted? (The technical problem.)

Level B. How precisely do the transmitted symbols convey the desired meaning? (The semantic problem.)

Level C. How effectively does the received meaning affect conduct in the desired way? (The effectiveness problem.) (Shannon & Weaver, 1949, p. 4)

Information in Shannon’s sense is not used in the ordinary sense of information. While by ‘information’ we ordinarily mean something akin to that which has already been said/written, Shannon means information in the sense of what may possibly be said (Shannon & Weaver, 1949, p. 8). For Shannon, information is a probable message sent over a channel (e.g., a telephone wire) and his concern is with describing general properties of the transmission and interpretation of such electronic signals.

Concerns about the ratio of signal-to-noise with respect to information transmission do originate from Shannon’s own communication theory work. The very ratio of signal-to-noise appears in Shannon’s theoretical examination of channel capacity with power limitation (Shannon & Weaver, 1949, p. 100). Shannon uses the ratio of the power source of the signal (denoted as P) to the power of the noise (denoted as N) in order to provide a general way of calculating how many bits per second any communication pathway can actually transmit. Shannon replaces P with S, the peak allowed transmitter power, in order to adjust channel capacity where peak power limits the rate of the channel to transmit bits. According to Shannon the upper bound rate of a channel is the channel band times the log of the ratio of signal plus noise to noise where the signal-to-noise ratio is low (Shannon & Weaver, 1949, p. 107). Loosely speaking, the rate at which telephone wires, coaxial cables, wireless networks, and the like can transmit messages varies logarithmically with the ratio of peak power (signal) to background noise on the channel (noise).

Shannon & Weaver’s specified problem set does not accurately match the sort of problem a drug discovery researcher is facing, not at least without a considerable stretch. Shannon’s sense of information in his definitive work on communication theory does not seem quite the same as the sort of information we are dealing with when we speak of genomics research data. Finally, Shannon’s notion of signal-to-noise can at best only loosely apply to notions of researchers struggling with too much information in their hands. Shannon is writing about communication channels, not people.

Efforts Shannon may have made to model specifically human communication in his theoretical work appears to be at best tertiary to the central thrust of his work, which was to generalize the properties of electronic communications systems. In short, Information Theory as proffered by Shannon does not appear to apply in any straightforward way to the sort of “noise-to-signal” problem Allen Roses describes or any other human communication problems that can occur independently of electronic signals. The signal-to-noise problem Roses reports is an information problem to be sure but it appears to be an information problem unlikely to be either explained or resolved through the lens of Shannon’s communication theory.

2.2.Information Overload
The concept of the possibility of too much information dates back to ancient times (Bawden, Holtham, & Courtney, 1999, p. 249). The recurring concern of information overload stems from the general notion that a person’s work becomes inefficient from increasing difficulty experienced in locating the best pieces of information. With the advent of computer-based information retrieval systems in the 1950s (Bawden et al., 1999, p. 249) as well as the beginnings of the mass proliferation of scientific research literature (Ziman, 1980), the concern became more frequently and more directly articulated and investigated. While any exact definition of information overload is elusive issues of relevance and efficiency are commonly notes as are issues of both data management and psychic strain (Bawden et al., 1999, p. 250). The constant problem however is that information overload stands for a struggle—a struggle that increases as a collection of information grows beyond human tractability. The recurring solution inevitably takes the form of methods or techniques that allow a person to locate some tractable set of pieces of information of sufficient quality in a reasonable amount of time in order to aid the person in completing the task.

3. Impact of information overload on PGx-based drug discovery
Information overload describes the general problem of “noise-to-signal” referred to by Allen Roses. Roses characterizes the information problem facing PGx-DD as having increased the rate of attrition of drug candidates in the pharmaceutical pipeline. Further, he states that the solution to the problem is an increase in “specific, disease-relevant targets” relative to all genomic data (A. D. Roses et al., 2005, p. 179). In other words, the proliferation of genomic data has drowned out this highly specific disease-relevant genomic information to the point that it increases drug discovery failure. The way to resolve the issue is to reduce information overload in PGx-DD by restricting the flow of information to PGx researchers to highly specific disease-relevant genomic information. As Roses says, providing researchers with validating evidence is crucial.

4. Validating evidence, novelty, and a PGx-info quality model
What, however, frames, delimits, or describes validating evidence for candidate targets? Roses states that disease-specific targets chosen based on well-trod beliefs “have a significant probability of being the totally wrong target” (A. D. Roses et al., 2005, p. 180). It is therefore not enough to identify highly specific disease relevant data efficiently; the data must support infrequent or entirely novel theories. The data must in essence have the characteristic of supporting novelty, of supporting ideas not commonly held, of bolstering theories that appear to be unreasonable.

The quality of PGx information should be evaluated using the following three criteria:

(a) the disease-relevance of the information,

(b) the specificity of the information, and

(c) the novelty of the information or the novelty of the theory supported by the information.

Sources

Bawden, D., Holtham, C., & Courtney, N. (1999). Perspectives on information overload. Aslib Proceedings, 51(8), 249-255.

Ekins, S., Bugrim, A., Nikolsky, Y., & Nikolskaya, T. (2005). Systems biology: Applications in drug discovery. In S. C. Gad (Ed.), Drug discovery handbook (pp. 123-183). Hoboken, New Jersey: Wiley Interscience.

Roses, A. D., Burns, D. K., Chissoe, S., Middleton, L., & Jean, P. S. (2005). Disease-specific target selection: A critical first step down the right road. Drug Discovery Today, 10(3), 177-189.

Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communication. Urbana and Chicago: University of Illinois Press.

Ziman, J. M. (1980). The proliferation of scientific literature – a natural process. Science, 208(4442), 369-371.

(NOTE: the preceding document is a revised expert from my master’s thesis.)

“The best way to predict the future is to invent it”
– Alan Kay

“Don’t listen to the physics majors until you also check with the Vaudevillians.”
– Tom Notti, The Bubble Guy

In my introduction to Hypothia I briefly referred to a paradigm shift in the web that I wish to participate in. That paradigm shift as I imagine it is the change from information retrieval (IR) to information generation as the core technology for utilizing the web.

Web 1.0 was basically the infancy of the web, the Mosaic-Netscape-Alta Vista web. Web 2.0 has ushered in a user-centric era, where the winners will be those who effectively repurpose user data assets generated from agile services.

We can safely say that the web is no longer in its infancy but rather in its adolescence. The somewhat adolesecent appeal of paradigmatic Web 2.0 applications (e.g., YouTube, MySpace) is no mere accident but rather a reflection of a youth-oriented culture of innovation and capital that is Silicon Valley mistaking youth appeal for broad appeal. Mistaking it, or perhaps pushing it and making it so.

The good news about the mistaken horizontal appeal is that there’s still quite a lot of room for the web to grow in terms of utility and users. The world is far from wired. Heck, Sequoia’s grandmom probably isn’t too keen on “the computer nonsense” yet. And why on earth would she be, other than for photos of the grandkids? OK, Google may be usable by most grandmothers, something which has been every bit as important to Google’s success as its PageRank algorithm.

The trend towards Web 3.0, the Semantic Web, has already begun to sprout. Instead of social web applications with so-called “horizontal” appeal (“so-called”, because if you are over 40 and on Friendster, you might get some strange looks from other users), we are already beginning to see niche social tools. In other words, web 2.0 tools are slowly becoming vertically oriented. Services such as LinkedIn, geared towards a semi-broad niche of white collar professionals looking to “network”, are succeeding as even more specialized and vertically-oreitned tools appear.

Such tools as LinkedIn, Gwagle and Trip usher in the Semantic Web simply because in narrowing the content scope, the content to be searched maps better to meaning. In other words, terms that users choose to search these tools become less ambiguous as the content scope shrinks. At the same time that terms map better to their intended senses, such sites make it more and more possible for ontology building and use. We can see this, for example, on Trip, an evidence-based medical search tool that provides faceted search features and leverages the admittedly rudimentary MeSH ontology. (I would add that disambiguation is best performed by the contours of context rather than by any set of rules applied to document collections. This emergent nature of disambiguation, and the concomitant necessity for ambiguity in understanding, is best saved for a later discussion.)

But it’s not just the narrowing semantic spaces that help usher in the Semantic Web. It’s also the more complex sets of user data, things like tags and search terms, applied to specific domains, that help automate user-responsive architectures expanding the possibilities for advanced analytics and responsive content.

Another indication that the Semantic Web in the sense of vertically-oriented semantic retrieval is on its way is the the work of George Miller’s research group at Princeton. Miller is renowned for a number of things, among them the creation and development of WordNet. Christiane Fellbaum, a colleague of Miller’s and long-time participant in the WordNet project, has apparently initiated work on a project called Medical WordNet (also here). Unlike WordNet, Medical WordNet will benefit from the fact that it will be applied to a much narrower semantic space. It will add specialized terms not in WordNet while limiting senses and relations between terms shared with WordNet.

Yet another indication of the rise of the Semantic Web is the finalization of the XQuery standard along with the development of XML content servers. Simply put, why invest months learning, say, data warehousing and OLAP cubes, when you can just implement advanced linguistic representations in XML and query them in an amazingly simple scripting language? Further, with XML content servers such as Mark Logic or eXist, you can query document collections and synthesize new documents, taking pieces of multiple documents and assembling them together, bound only by the limits of XPath and whatever heuristics you can add.

But OK so with Web 3.0 we will have basic semantics incorporated into our content and the ability to leverage meaning in order to find what we want to find. In the article in which he coins the phrase, “Semantic Web,” Tim Berners-Lee speculated extensively about the possibility of using meaning-annontated content to make basic deductions. But while the infusion of meaning into information retrieval is well under way, the infusion of domain rules for drawing conclusions from that which is retrieved is not nearly so immanent.

So Web 3.0 will be yet another era of information retrieval in the literal sense. Finding information will become refined to domain-specific, context-limited, user-experience-friendly, meaning-aware, document synthesis retrieval. But without the maturation of reasoning, retrieval will remain nothing more than that–retrieval. Information processing tools will remain stuck on regurgitation, however elaborate the regurgitation may be.

With all of the power of retrieval extended and leveraged, and the introduction of vertically-oriented ontology tools such as Medical WordNet, the next crush will be to develop systems that think. Well, thinking? No not really thinking. Rather, with the application of context, domain, and user-tuned semantics to search, the need for the development of domain-specific heuristics will become readily apparent. Instead of the emergence of question answering systems whereby such systems answer multiple questions, I imagine we’ll see services dedicated to solving single problems quite well.

What’s crucial in solving problems using information retrieval tools is that the output of rule-using systems is novel content. In other words, such tools are no longer merely finding existing content but rather creating new content. And the creation of useful information places a heavy burden on evaluating statements for their quality.

The long-term goal of Hypothia is to pursue the development of problem-specific information generation services with a particular eye on scientific discovery. Hypothia in short aims to become the first innovation service and help usher in Web 4.0. It’s far-off, far-fetched, far-out, and maybe a bit ridiculous as a vision, but someone’s got to create the future.

Hypothia is the name of my new venture. Hypothia is a new organization dedicated to innovation leveraging the power of text mining and advanced analytical strategies for vertical domains. Hypothia aims to release a set of next-generation information tools with the ultimate goal of replacing search with generation.

The shift from information retrieval (IR) to information generation is a subtle yet revolutionary shift in the way we interact with information.

On this blog I hope to discuss various strategies, technologies and general ideas that might contribute to this paradigm shift. As Google moves away from a business strategy of innovation in IR to a strategy of product/service diversification, they create a tremendous opportunity for everyone else to invent the next best solutions.

I have a number of core interests with respect to text mining. I believe in the concept of know thy data. Hence I believe that myriad complexities in text mining can be reduced and application usability can be maximized by concentrating in specific problem areas. Most of my own work has concentrated in health, from drug discovery to consumer health to clinical diagnosis. I also have a fascination with applying mining strategies to other areas, such as content management, commodities forecasting, real estate pricing analysis, and even sports analysis.

If you wish to learn a little more about me, please see my personal page (http://patrickherron.com). You can find additional information about my previous academic research on my text mining co-op search page (http://proximate.org/tm) and you can learn more about my creative writing and publications on my writing bio page (http://proximate.org/bio).