Site upgrade
Well, it’s time to upgrade to WordPress 2.02, and this is the least active of my blogs.
So if something bad happens to this site, that’s a good suspect as the culprit …
EDIT: Well, it looks OK, with the changes being on the writing/admin side (good) and not in the display (very good). I look forward to discovering whether the glitches that limit trackbacks have been fixed …
| Categories: About this blog | 2 Comments |
Misunderstandings of text management
I argue a lot with relational purists. On the whole they’re smart people, but they do have their blindspots.
One of the biggest is in the area of text. They fail to see how text data management is fundamentally different from tabular data management. Here’s a little article explaining why text doesn’t fit well into the relational model.
| Categories: Search engines | 1 Comment |
The text technologies market 4: Requirements for an industry-altering ontology management system
In previous posts I argued that what’s holding the text technology industry back is the lack of a viable ontology management system. The obvious objection to such a suggestion is: Who would use it? There is no business process for ontology management, even less than there is for “knowledge management,” and for that matter less than there was for “knowledge engineering” during the expert systems bubble of the 1980s. Enterprises do not have anything like a “chief ontologist.” Indeed, that job title sounds like a joke — a touchy-feely liberal-artsy nonstarter.
The only way a successful product category of ontology management systems can emerge is if the products are usable by ordinary IT personnel. Vendor-supplied product training can be required, of course. Some day there can be certifications, and maybe a single class in a computer science curriculum. But almost nobody is going to buy a product whose use requires a masters degree in library science or “ontology management.”
So here are some very high-level requirements I think an ontology management system needs to meet.
1. Basic knowledge representation has to be flexible. It has to accommodate semantic net kinds of relationships (is_an_instance_of, is_a_subcategory_of). It also has to accommodate machine learning/statistical kinds of evidence (both positive and negative evidence).
2. There has to be strong layering/versioning. Pieces of the ontology will come from the vendor. Pieces will come from frequently-updated machine-learning exercises against an enterprise’s own corpus(es). Pieces will be added by hand, through a collaboration between IT and (at first) power users. It will have to be possible to reverse any of those pieces out, to apply different pieces for different specific applications, and so on.
3. There need to be standard, open ways for different kinds of applications to use the ontologies. UIMA could be a starting point.
4. The product needs to be industrial-strength – reliable, scalable, secure, sufficiently easy to administer, available on a sufficient range of platforms, and compliant with general standards (not just the text-specific ones).
Obviously, these requirements are nontrivial to achieve. But if some vendor does do a good job on them, the payoff could be huge. Dominance of the enterprise text technologies market – which would be a greatly expanded market – is at stake.
| Categories: Ontologies | 4 Comments |
The text technologies market 3: Here’s what’s missing
The text technologies market should be booming, but actually is in disarray. How, then, do I think it should be fixed? I think the key problem can be summed up like this:
There’s a product category that is a key component of the technology, without which it won’t live up to nearly its potential benefits. But there’s widespread and justified concern over its commercial viability. Hence, the industry cowers in niches where it can indeed eke out some success despite products that fall far short of their true potential.
The product category I have in mind, for lack of a better name, is an ontology management system. No category of text technology can work really well without some kind of semantic understanding. Automated clustering is very important for informing this understanding in a cost-effective way, but such clustering is not a complete solution – hence the relative disappointment of Autonomy, the utter failure of Excite, and so on. Rather, there has to be some kind of concept ontology that can be use to inform disambiguation. It doesn’t matter whether the application category is search, text mining, command/control, or anything else; semantic disambiguation is almost always necessary for the most precise, user-satisfying results. Maybe it’s enough to have a thesaurus – i.e., a list of synonyms. Maybe it’s enough to define “concepts” by simple vectors of word likelihoods. But you have to have something, or your search results will be cluttered, your information retrieval won’t fetch what you want it to, your text mining will have wide error bars, and your free-speech understanders will come back with a whole lot of “I’m sorry; I didn’t understand that.”
This isn’t just my opinion. Look at Inquira. Look at text mining products from SPSS and many others. Look at Oracle’s original text indexing technology and also at its Triplehop acquisition. For that matter, look at Sybase’s AnswersAnywhere, in which the concept network is really just an object model, in the full running-application sense of “object.” Comparing text to some sort of thesaurus or concept representation is central to enterprise text technology applications (and increasingly to web search as well).
Could one “ontology management system,” whatever that is, service multiple types of text applications? Of course it could. The ideal ontology would consist mainly of four aspects:
1. A conceptual part that’s language-independent.
2. A general language-dependent part.
3. A sensitivity to different kinds of text – language is used differently when spoken, for instance, than it is in edited newspaper articles.
4. An enterprise-specific part. For example, a company has product names, and competitors with product names, and those names have abbreviations, and so on.
Relatively little of that is application-specific; for any given enterprise, a single ontology should meet most or all of its application needs.
Coming up: The legitimate barriers to the creation of an ontology management system market, and ideas about how to overcome them.
| Categories: Enterprise search, Language recognition, Ontologies, Search engines, Speech recognition, Text mining | 5 Comments |
The text technologies market 2: It’s actually in disarray
The text technologies market should be huge and thriving. Actually, however, it’s in disarray. Multiple generations of enterprise search vendors have floundered, with the Autonomy/Verity merger being basically a combination of the weak. The RDBMS vendors came up with decent hybrid tabular/text offerings, and almost nobody cared. (Admittedly, part of the reason for that is that the best offering was Oracle’s, and Oracle almost always screws up its ancillary businesses. Email searchability has been ridiculously bad since — well, since the invention of email. And speech technology has floundered for decades, with most of the survivors now rolled into the new version of Nuance.
Commercial text mining is indeed booming, but not to an extent that erases the overall picture of gloom. It’s at most a several hundred million dollar business, and one that’s highly fragmented. For example, at a conference on IT in life sciences not that long ago, two things became evident. First, the text mining companies were making huge, intellectually fascinating, life-saving contributions to medical research. Second, more than ten vendors were divvying up what was only around a $10 million market.
If text technology is going to achieve the prominence and prosperity it deserves, something dramatic has to change.
| Categories: Enterprise search, Language recognition, Search engines, Speech recognition, Text mining | 2 Comments |
The text technologies market 1: It should be huge
From a number of standpoints, the market for enterprise technologies that explicitly* manage text SHOULD be huge. Consider:
1. The market for consumer text search is huge — think of Google.
2. The market for implicit* management of text is huge. Email management is a significant fraction of the IT budget, if you factor in the predominance of email in the use of networks and PCs. Now regulations are compelling email to be stored and managed at great expense. People spend hours per day working on email, word processors, etc.
3. The text mining market has recently boomed, and good ROI appears to be the norm.
*My implicit vs. explicit distinction here is meant to distinguish technologies that manage text as some sort of BLOB or other blob vs. technologies that take account of the fact that it is text, which contains words, phrases, synonyms, and so on.
If text technologies could live up to researchers’ dreams, typical knowledge workers would save hours per week and in many cases hours per day. The benefits would rival at least those of the whole PC/office productivity/messaging set of technologies. Thus, at least in theory, the market potential for these technologies is enormous.
| Categories: Search engines, Text mining | 3 Comments |
Autonomy + Verity — so what?
On some levels, the Autonomy/Verity merger makes total sense. The text search industry now has an unquestionably dominant vendor of shelfware. Somewhat less snarkily, I could say that it has a dominant OEM vendor of search technology. And while Verity’s management team has never recovered from the dizzying cycles of turnover in the 1990s, Autonomy’s obviously was quite effective. However, I see no obvious reason to believe that combined company will actually ship good products, or ones that lead to fundamentally greater adoption for enterprise search than the fairly marginal role it plays today.
Verity and Autonomy represent different philosophies of text search — Boolean vs. concept-based, basically. Neither works very well on its own, whether in the enterprise or on the web, with concept-based being the weaker of the two. That’s why Altavista et al. failed, and Excite failed yet more completely. It’s why Verity’s text search is generally more respected, and has more hardcore users, than Autonomy’s. (Being a vastly older company than Autonomy helps a lot too, of course.)
I hope that the merged company will soon introduce some new and/or synthesized approaches to search, significantly improving the overall quality of available products. If anybody has the resources and motivation, it will. The recent boom in text data mining, and the general increase of seriousness about ontologies, at least raises the possibility that concept-oriented search will evolve into something significantly useful. But I’m not holding my breath.
| Categories: Enterprise search, Ontologies, Search engines | 4 Comments |
Entity-Attribute-Value everywhere?
Jon Udell’s rumination on metadata in Infoworld reminds me how pervasive entity-attribute-value knowledge representation is becoming. You knew that, of course, because you’ve heard of “XML.” But did you also know that operating systems were gaining rich metadata representation at the file level? I must confess that I didn’t.
| Categories: Ontologies | Leave a Comment |
Linkage among different text technologies
The first post in this blog describes its subject as “a group of interrelated, linguistics-based technology sectors, including text mining, search, speech recognition, and text command-and-control.” So I might as well kick off the discussion by summarizing some reasons why I think these sectors really are connected to each other. Very quickly:
IBM says so, and nobody (that I know of) is contradicting them. The essence of the UIMA story is that a lot of different pieces of technology need to be swapped in and out, not just among different brands of the same text applications, but among different kinds of text app. The vendors I checked with are uniformly skeptical about whether UIMA will have a real market impact, but none disputes UIMA’s underlying premise.
The tokenization chain. This general industry agreement is only one of three major reasons I believe in the general UIMA premise (while sharing the skepticism about that particular framework’s early adoption). A second dates back to when I was first learning about text search. At a Verity User Conference in, I think, April 1997, I had a very interesting conversation about Verity’s new architecture. (Probably with Phil Nelson, maybe with somebody else, such as Hugh Njemanze or Nick Arnett.) Basically, the system had been modularized, and the way it had been modularized was to create a flow of tokenization after tokenization after tokenization. The third reason is the observation that Inxight, so central to the tokenization strategies of text search vendors, plays pretty much the same role for the text mining companies.
The centrality of concept ontologies. I don’t currently have an opinion about the Semantic Web, but in a more limited sense it’s clear that ontologies will rule text applications. Whether for search, text data mining, or application command/control, it just doesn’t suffice to identify, find, weigh, or respond to individual words. Rather, you need to add other words indicating similar meaning – or a similar user “intent” — into the mix.*
This is a big deal, because simple minded ontologies don’t work. They can’t just be automatically generated, and they can’t just be hand-built. They can’t just be custom to each user or user enterprise, but they also can’t be provided entirely by technology vendors. Almost no large enterprises currently have a good system of ontology building and management, but in the near future most will have to. Evolution in this area will be a crucial determinant of how multiple text technology submarkets are shaped.
In particular, this is a big enough deal that I think search and text data mining and other text technologies will, for each enterprise, tend to use the same ontology.
*Note: There’s a whole other question as to how long we’ll be able to get by just looking at semantics, or whether syntactic analysis absolutely also should be in the mix. But first things first; without a good ontology, syntactic analysis is a pretty hopeless endeavor.
The use of text data mining in other areas. The automated part of the ontology building process involves a lot of text data mining. Large search engine companies generally do a lot of data mining to establish and validate tweaks to their search algorithms. The same goes for spam filters and more questionable forms of censorware. You can’t act intelligently without learning, and machines don’t learn well without doing statistical analyses.
I hope to post soon on each of these issues at more length, and I encourage comments on any of them as inputs to further work. But for now, I’ll just claim to have provided strong evidence for my initial point: Seemingly different text technologies are indeed closely related.
| Categories: Ontologies, Search engines, Text mining | 2 Comments |
About the author
I’m having trouble with static pages in WordPress right now, so I’ll just do the “About” pages for the blog inline as posts.
About the author
Curt A. Monash, Ph.D., has been a top-level software industry analyst and participant since 1981, and has been involved in text technologies ever since he helped raise money for Artificial Intelligence Corporation in 1983. Since the mid-1990s he has done extensive research on text search, some of which appeared in The Spider’s Apprentice, which for many years was an industry-leading guide to search engine use and understanding. He was a panelist at the inaugural Text Mining Summit in 2005.
Fuller biographical information about Curt can be found on the “About” page for the Monash Report and at Curt’s Monash Information Services bio page; software industry leaders’ views of Curt may be seen on the Monash Information Services testimonials page. (Apologetic note: Those pages not excepted, the Monash Information Services site hasn’t been updated for a while, and needs a bit of freshening.)
Curt’s views may also be found in the Monash Report (analysis of software and related industries), Software Memories (personal reminiscences and other historical notes about the last three decades or so of the software and internet industries), and DBMS2 (covering developments in enterprise database management and XML-based SOAs).
Curt’s primary email address follows the template FirstnameLastname@Lastname.com, although disguising it that way is tantamount to closing the pantry door after the spam has already gotten in. Thus, please put a distinctive title on your email, so that your email won’t mistakenly be thrown out with the bad stuff. Mentioning “Text Technologies” would be one excellent idea.
| Categories: About this blog | 2 Comments |