Niice – a Search Engine Bent on Aesthetic Appeal

August 1, 2014

A new search engine has appeared on the radar called Niice. It is focused on inspiration search and the presentation of quality images to spark ideas. The Niice blog lays out their mission statement (to be a sort of upscale Google for graphic designers, photographers, tasteful people in general) as well as stating their design principles. These include remaining safe work, not getting in the way, and being restrained in content choices. They also stress embracing serendipity,

We want to give you results that you don’t expect, presented in a way that inspires your brain to make new connections. Niice isn’t for finding an image that you can copy, it’s for bringing together lots of ideas for you to combine into something new…. The internet is full of inspiration, but since Google doesn’t have a ‘Good Taste’ filter, finding it means jumping back and forth between blogs and gallery sites.”

Somewhat like Pinterest, Niice allows users to create “moodboards” or collections of images which can be saved, collaborated on, or downloaded as JPEGs. When I searched the term “water”, a collage of images appeared that included photographs of ocean waves and a sunset over a lake, a glass sculpture resembling a dew drop, and a picture that linked to a story on an artist who manipulates water with her mind among many others.

Chelsea Kerwin, August 01, 2014

Sponsored by, developer of Augmentext

The IHS Invention Machine: US 8,666,730

July 31, 2014

I am not an attorney. I consider this a positive. I am not a PhD with credentials as impressive Vladimir Igorevich Arnold, my distant relative. He worked with Andrey Kolmogorov, who was able to hike in some bare essentials AND do math at the same time. Kolmogorov and Arnold—both interesting, if idiosyncratic, guys. Hiking in the wilderness with some students, anyone?

Now to the matter at hand. Last night I sat down with a copy of US 8,666,730 B2 (hereinafter I will use this shortcut for the patent, 730), filed in an early form in 2009, long before Information Handing Service wrote a check to the owners of The Invention Machine.

The title of the system and method is “Question Answering System and Method Based on  Semantic Labeling of Text Documents and User Questions.” You can get your very own copy at (Be sure to check out the search tips; otherwise, you might get a migraine dealing with the search system. I heard that technology was provided by a Canadian vendor, which seems oddly appropriate if true. The US government moves in elegant, sophisticated ways.

Well, 730 contains some interesting information. If you want to ferret out more details, I suggest you track down a friendly patent attorney and work through the 23 page document word by word.

My analysis is that of a curious old person residing in rural Kentucky. My advisors are the old fellows who hang out at the local bistro, Chez Mine Drainage. You will want to keep this in mind as I comment on this James Todhunter (Framingham, Mass), Igor Sovpel (Minsk, Belarus), and Dzianis Pastanohau (Minsk, Belarus). Mr. Todhunter is described as “a seasoned innovator and inventor.” He was the Executive Vice President and Chief Technology Officer for Invention Machine. See, Linked In at (if you are lucky), and  this YouTube video at Igor Sovpel, co inventor of 730, has racked up some interesting inventions. See Mr. Pastanohau was on the 730 team and he also helped invent US 8,583,422 B2, “System and Method for Automatic Semantic Labeling of Natural Language Texts.”

The question answering invention is explained this way:

A question-answering system for searching exact answers in text documents provided in the electronic or digital form to questions formulated by user in the natural language is based on automatic semantic labeling of text documents and user questions. The system performs semantic labeling with the help of markers in terms of basic knowledge types, their components and attributes, in terms of question types from the predefined classifier for target words, and in terms of components of possible answers. A matching procedure makes use of mentioned types of semantic labels to determine exact answers to questions and present them to the user in the form of fragments of sentences or a newly synthesized phrase in the natural language. Users can independently add new types of questions to the system classifier and develop required linguistic patterns for the system linguistic knowledge base.

The idea, as I understand it, is that I can craft a question without worrying about special operators like AND or field labels like CC=. Presumably I can submit this type of question to a search system based on 730 and its related inventions like the automatic indexing in 422.

The references cited for this 2009 or earlier invention are impressive. I recognized Mr. Todhunter’s name, that of a person from Carnegie Mellon, and one of the wizards behind the tagging system in use at SAS, the statistics outfit loved by graduate students everywhere. There were also a number of references to Dr. Liz Liddy, Syracuse University. I associated her with the mid to late 1990s system marketed then as DR LINK (Document Retrieval Linguistic Knowledge). I have never been comfortable with the notion of “knowledge” because it seems to require that subject matter experts and other specialists update, edit, and perform various processes to keep the “knowledge” from degrading into a ball of statistical fuzz. When someone complains that a search system using Bayesian methods returns off point results, I look for the humans who are supposed to perform “training,” updates, remapping, and other synonyms for “fixing up the dictionaries.” You may have other experiences which I assume are positive and have garnered you rapid promotion for your search system competence. For me, maintaining knowledge bases usually leads to lots of hard work, unanticipated expenses, and the customary termination of a scapegoat responsible for the search system.

I am never sure how to interpret extensive listings of prior art. Since I am not qualified to figure out if a citation is germane, I will leave it to you to wade through the full page of US patent, foreign patent documents, and other publications. Who wants to question the work of the primary examiner and the Faegre Baker Daniels “attorney, agent, or firm” tackling 730.

On to the claims. The patent lists 28 claims. Many of them refer to operations within the world of what the inventors call expanded Subject-Action-Object or eSAO. The idea is that the system figures out parts of speech, looks up stuff in various knowledge bases and automatically generated indexes, and presents the answer to the user’s question. The lingo of the patent is sufficiently broad to allow the system to accommodate an automated query in a way that reminded me of Ramanathan Guha’s massive semantic system. I cover some of Dr. Guha’s work in my now out of print monograph, Google Version 2.0, published by one of the specialist publishers that perform Schubmehl-like maneuvers.

My first pass through the 730’s claims was a sense of déjà vu, which is obviously not correct. The invention has been award the status of a “patent”; therefore, the invention is novel. Nevertheless, these concepts pecked away at me with the repetitiveness of the woodpecker outside my window this morning:

  1. Automatic semantic labeling which I interpreted as automatic indexing
  2. Natural language process, which I understand suggests the user takes the time to write a question that is neither too broad nor too narrow. Like the children’s story, the query is “just right.”
  3. Assembly of bits and chunks of indexed documents into an answer. For me the idea is that the system does not generate a list of hits that are probably germane to the query. The Holy Grail of search is delivering to the often lazy, busy, or clueless user an answer. Google does this for mobile users by looking at a particular user’s behavior and the clusters to which the user belongs in the eyes of Google math, and just displaying the location of the pizza joint or the fact that a parking garage at the airport has an empty space.
  4. The system figures out parts of speech, various relationships, and who-does-what-to-whom. Parts of speech tagging has been around for a while and it works as long as the text processed in not in the argot of a specialist group plotting some activity in a favela in Rio.
  5. The system performs the “e” function. I interpreted the “e” to mean a variant of synonym expansion. DR LINK, for example, was able in 1998 to process the phrase white house and display content relevant to presidential activities. I don’t recall how this expansion from bound phrase to presidential to Clinton. I do recall that DR LINK had what might be characterized as a healthy appetite for computing resources to perform its expansions during indexing and during query processing. This stuff is symmetrical. What happens to source content has to happen during query processing in some way.
  6. Relevance ranking takes place. Various methods are in use by search and content processing vendors. Some of based on statistical methods. Others are based on numerical recipes that the developer knows can be computed within the limits of the computer systems available today. No N=NP, please. This is search.
  7. There are linguistic patterns. When I read about linguistic patterns I recall the wild and crazy linguistic methods of Delphes, for example. Linguistics are in demand today and specialist vendors like Bitext in Madrid, Spain, are in demand. English, Chinese, and Russian are widely used languages. But darned useful information is available in other languages. Many of these are kept fresh via neologisms and slang. I often asked my intelligence community audiences, “What does teddy bear mean?” The answer is NOT a child’s toy. The clue is the price tag suggested on sites like eBay auctions.

The interesting angle in 730 is the causal relationship. When applied to processes in the knowledge bases, I can see how a group of patents can be searched for a process. The result list could display ways to accomplish a task. NOTting out patents for which a royalty is required leaves the searcher with systems and methods that can be used, ideally without any hassles from attorneys or licensing agents.

Several questions popped into my mind as I reviewed the claims. Let me highlight three of these:

First, computational load when large numbers of new documents and changed content has to be processed. The indexes have to be updated. For small domains of content like 50,000 technical reports created by an engineering company, I think the system will zip along like a 2014 Volkswagen Golf.


Source: US8666730, Figure 1

When terabytes of content arrived every minute, then the functions set forth in the block diagram for 730 have to be appropriately resourced. (For me, “appropriately resourced” means lots of bandwidth, storage, and computational horsepower.)

Second, the knowledge base, as I thought about when I first read the patent, has to be kept in tip top shape. For scientific, technical, and medical content, this is a more manageable task. However, when processing intercepts in slang filled Pashto, there is a bit more work required. In general, high volumes of non technical lingo become a bottleneck. The bottleneck can be resolved, but none of the solutions are likely to make a budget conscious senior manager enjoy his lunch. In fact, the problem of processing large flows of textual content is acute. Short cuts are put in place and few of those in the know understand the impact of trimming on the results of a query. Don’t ask. Don’t tell. Good advice when digging into certain types of content processing systems.

Third, the reference to databases begs this question, “What is the amount of storage required to reduce index latency to less than 10 seconds for new and changed content?” Another question, “What is the gap that exists for a user asking a mission critical question between new and changed content and the indexes against which the mission critical query is passed?” This is not system response time, which as I recall for DR LINK era systems was measured in minutes. The user sends a query to the system. The new or changed information is not yet in the index. The user makes a decision (big or small, significant or insignificant) based on incomplete, incorrect, or stale information. No big problem is one is researching a competitor’s new product. Big problem when trying to figure out what missile capability exists now in an region of conflict.

My interest is enterprise search. IHS, a professional publishing company that is in the business of licensing access to its for fee data, seems to be moving into the enterprise search market. (See My researchers (an unreliable bunch of goslings) and I will be monitoring the success of IHS. Questions of interest to me include:

  1. What is the fully loaded first year cost of the IHS enterprise search solution? For on premises installations? For cloud based deployment? For content acquisition? For optimization? For training?
  2. How will the IHS system handle flows of real time content into its content processing system? What is the load time for 100 terabytes of text content with an average document size of 50 Kb? What happens to attachments, images, engineering drawings, and videos embedded in the stream as native files or as links to external servers?
  3. What is the response time for a user’s query? How does the user modify a query in a manner so that result sets are brought more in line with what the user thought he was requesting?
  4. How do answers make use of visual outputs which are becoming increasingly popular in search systems from Palantir, Recorded Future, and similar providers?
  5. How easy is it to scale content processing and index refreshing to keep pace with the doubling of content every six to eight weeks that is becoming increasingly commonplace for industrial strength enterprise search systems? How much reengineering is required for log scale jumps in content flows and user queries?

Take a look at 730 an d others in the Invention Machine (IHS) patent family. My hunch is that if IHS is looking for a big bucks return from enterprise search sales, IHS may find that its narrow margins will be subjected to increased stress. Enterprise search has never been nor is now a license to print money. When a search system does pump out hundreds of millions in revenue, it seems that some folks are skeptical. Autonomy and Fast Search & Transfer are companies with some useful lessons for those who want a digital Klondike.

The New SearchCIO Presents 219 Definitions of Failure

July 29, 2014

I received an email about the new “www.Search” Here it is:


I was not aware of the old search CIO. I clicked a link that delivered me to a page asking me to log in. I ignored that and navigated to the search box and entered the query “failure.” The system responded with 13,060 articles with the word failure in them, 103 conversations, and 219 definitions of failure.

The first hit was to an IBM mainframey problem with a direct access storage device. Remember those from 2003 and before? The second hit was a 2002 definition about “failure protection.”


The new search system appears to pull matching strings from articles and content objects across TechTarget’s different publications/information services. I clicked on the DASD failure link and was enjoined to sign up for a free membership. Hmmm. Okay. Plan B.

In my lectures about tactics for getting useful open source information, I focus on services like No registration and no invasive tracking. The approach is different from the marketing-oriented, we want an email address in use at Here’s what Ixquick displayed, quite quickly as well:


The first hit was to a detailed chunk of information from IBM called “DASD Ownership Notification (DVHXDN). No registration required. The hits were on point and quite useful in my opinion. A happy quck for Ixquick.

If you have an appetite for TechTarget information, navigate to If you want helpful search results from a pretty good metasearch engine, go for Ixquick.

Stephen E Arnold, July 30, 2014


IHS Enterprise Search: Semantic Concept Lenses Are Here

July 29, 2014

I pointed out in that IDC, a mid tier consulting firm that has marketed my information without permission on Amazon of all places, has rolled out a new report about content processing. The academic sounding title is “The Knowledge Quotient: Unlocking the Hidden Value of Information.” Conflating knowledge and information is not logically satisfying to me. But you may find the two words dusted with “value” just the ticket to career success.

I have not read the report, but I did see a list of the “sponsors” of the study. The list, as I pointed out, was an eclectic group, including huge firms struggling for credibility (HP and IBM) down to consulting firms offering push ups for indexers.

One company on my list caused me to go back through my archive of search information. The firm that sparked my interest is Information Handling Services or IHS or Information Handling Service. The company is publicly traded and turning a decent profit. The revenue of IHS has moved toward $2 billion. If the global economy perks up and the defense sector is funded at pre-drawdown levels, IHS could become a $2 billion company.

IHS is a company with an interesting history and extensive experience with structured and unstructured search. Few of those with whom I interacted when I was working full time considered IHS a competitor to the likes of Autonomy, Endeca, and Funnelback.

In the 2013 10-K on page 20, IHS presents its “cumulative total return” in this way:


The green line looks like money. Another slant on the company’s performance can be seen in a chart available from Google Finance.

The Google chart shows that revenue is moving upwards, but operating margins are drifting downward and operating income is suppressed. Like Amazon, the costs for operating and information centric company are difficult to control. Amazon seems to have thrown in the towel. IHS is managing like the Dickens to maintain a profit for its stakeholders. For stakeholders, is the hope is that hefty profits will be forthcoming?


Source: Google Finance

My initial reaction was, “Is IHS trying to find new ways to generate higher margin revenue?”

Like Thomson Reuters and Reed Elsevier, IHS required different types of content processing plumbing to deliver its commercial databases. Technical librarians and the competitive intelligence professionals monitoring the defense sector are likely to know about IHS different products. The company provides access to standards documents, regulatory information, and Jane’s military hardware information services. (Yep, Jane’s still has access to retired naval officers with mutton chop whiskers and interesting tweed outfits. I observed these experts when I visited the company in England prior to IHS’s purchase of the outfit.)

The standard descriptions of IHS peg the company’s roots with a trade magazine outfit called Rogers Publishing. My former boss at Booz, Allen & Hamilton loved some of the IHS technical services. He was, prior to joining Booz, Allen the head of research at Martin Marietta, an IHS customer in the 1970s. Few remember that IHS was once tied in with Thyssen Bornemisza. (For those with an interest in history, there are some reports about the Baron that are difficult to believe. See

Large professional publishing companies were early, if somewhat reluctant, supporters of SGML and XML. Running a query against a large collection of structured textual information could be painfully slow when one relied on traditional relational database management systems in the late 1980s. Without SGML/XML, repurposing content required humans. With scripts hammering on SGML/XML, creating new information products like directories and reports eliminated the expensive humans for the most part. Fewer expensive humans in the professional publishing business reduces costs…for a while at least.

IHS climbed on the SGML/XML diesel engine and began working to deliver snappy online search results. As profit margins for professional publishers were pressured by increasing marketing and technology costs, IHS followed the path of other information centric companies. IHS began buying content and services companies that, in theory, would give the professional publishing company a way to roll out new, higher margin products. Even secondary players in the professional publishing sector like Ebsco Electronic Publishing wanted to become billion dollar operations and then get even bigger. Rah, rah.

These growth dreams electrify many information company’s executives. The thought that every professional publishing company and every search vendor are chasing finite or constrained markets does not get much attention. Moving from dreams to dollars is getting more difficult, particularly in professional publishing and content processing businesses.

My view is that packaging up IHS content and content processing technology got a boost when IHS purchased the Invention Machine in mid 2012.

Years ago I attended a briefing by the founders of the Invention Machine. The company demonstrated that an engineer looking for a way to solve a problem could use the Invention Machine search system to identify candidate systems and methods from the processed content. I recall that the original demonstration data set was US patents and patent applications. My thought was that an engineer looking for a way to implement a particular function for a system could — if the Invention Machine system worked as presented — could present a patent result set. That result set could be scanned to eliminate any patents still in force. The resulting set of patents might yield a procedure that the person looking for a method could implement without having to worry about an infringement allegation. The original demonstration was okay, but like most “new” search technologies, Invention Machine faced funding, marketing, and performance challenges. IHS acquired Invention Machine, its technologies, its Eastern European developers, and embraced the tagging, searching, and reporting capabilities of the Invention Machine.

The Goldfire idea is that an IHS client can license certain IHS databases (called “knowledge collections”) and then use Goldfire / Invention Machine search and analytic tools to get the knowledge “nuggets” needed to procure a missile guidance component.

The jargon for this finding function is “semantic concept lenses.” If the licensee has content in a form supported by Goldfire, the licensee can search and analyze IHS information along with information the client has from its own sources. A bit more color is available at

The IHS search system is described in terms familiar to a librarian and a technical analyst; for example, here’s the attributes for Goldfire “cloud” from an IHS 2013 news release:

  • “Patented semantic search technology providing precise access to answers in documents. [Note: IHS has numerous patents but it is not clear what specific inventions or assigned inventions apply directly to the search and retrieval solution(s)]
  • Access to more than 90 million scientific and technical “must have” documents curated by IHS. This aggregated, pre-indexed collection spans patents, premium IHS content sources, trusted third-party content providers, and the Deep Web.
  • The ability to semantically index and research across any desired web-accessible information such as competitive or supplier websites, social media platforms and RSS feeds – turning these into strategic knowledge assets.
  • More than 70 concept lenses that promote rapid research, browsing and filtering of related results sets thus enabling engineers to explore a concept’s definitions, applications, advantages, disadvantages and more.
  • Insights into consumer sentiment giving strategy, product management and marketing teams the ability to recognize customer opinions, perceptions, attitudes, habits and expectations – relative to their own brands and to those of their partners’ and competitors’ – as expressed in social media and on the Web.”

Most of these will resonate with those familiar with the assertions of enterprise search and content processing vendors. The spin, which I find notable, is that IHS delivers both content and information retrieval. Most enterprise search vendors provide technology for finding and analyzing data. The licensee has to provide the content unless the enterprise search vendor crawls the Web or other sources, creates an archive or a basic index, and then provides an interface that is usually positioned as indexing “all content” for the user.

According to Virtual Strategy Magazine (which presumably does not cover “real” strategy), I learned that US 8666730:

covers the semantic concept “lenses” that IHS Goldfire uses to accelerate research. The lenses correlate with the human knowledge system, organizing and presenting answers to engineers’ or scientists’ questions – even questions they did not think to ask. These lenses surface concepts in documents’ text, enabling users to rapidly explore a concept’s definitions, applications, advantages, disadvantages and more.

The key differentiator is claimed to move IHS Goldfire up a notch. The write up states:

Unlike today’s textual, question-answering technologies, which work as meta-search engines to search for text fragments by keyword and then try to extract answers similar to the text fragment, the IHS Goldfire approach is entirely unique – providing relevant answers, not lists of largely irrelevant documents. With IHS Goldfire, hundreds of different document types can be parsed by a semantic processor to extract semantic relationships like subject-action-object, cause-and-effect and dozens more. Answer-extraction patterns are then applied on top of the semantic data extracted from documents and answers are saved to a searchable database.

According to Igor Sovpel, IHS Goldfire:

“Today’s engineers and technical professionals are underserved by traditional Internet and enterprise search applications, which help them find only the documents they already know exist,” said Igor Sovpel, chief scientist for IHS Goldfire. “With this patent, only IHS Goldfire gives users the ability to quickly synthesize optimal answers to a variety of complex challenges.”

Is IHS’ new marketing push in “knowledge” and related fields likely to have an immediate and direct impact on the enterprise search market? Perhaps.

There are several observations that occurred to me as I flipped through my archive of IHS, Thyssen, and Invention Machine information.

First, IHS has strong brand recognition in what I would call the librarian and technical analyst for engineering demographic. Outside of lucrative but quite niche markets for petrochemical information or silhouettes and specifications for the SU 35, IHS suffers the same problem of Thomson Reuters and Wolters Kluwer. Most senior managers are not familiar with the company or its many brands. Positioning Goldfire as an enterprise search or enterprise technical documentation/data analysis tool will require a heck of a lot of effective marketing. Will positioning IHS cheek by jowl with IBM and a consulting firm that teaches indexing address this visibility problem? The odds could be long.

Second, search engine optimization folks can seize on the name Goldfire and create some dissonance for IHS in the public Web search indexes. I know that companies like Attivio and Microsoft use the phrase “beyond search” to attract traffic to their Web sites. I can see the same thing happening. IHS competes with other professional publishing companies looking for a way to address their own marketing problems. A good SEO name like “Goldfire” could come under attack and quickly. I can envision lesser competitors usurping IHS’ value claims which may delay some sales or further confuse an already uncertain prospect.

Third, enterprise search and enterprise content analytics is proving to be a difficult market from which to wring profitable, sustainable revenue. If IHS is successful, the third party licensees of IHS data who resell that information to their online customers might take steps to renegotiate contracts for revenue sharing. IHS will then have to ramp up its enterprise search revenues to keep or outpace revenues from third party licensees. Addressing this problem can be interesting for those managers responsible for the negotiations.

Finally, enterprise search has a lot of companies planning on generating millions or billions from search. There can be only one prom queen and a small number of “close but no cigar” runner ups. Which company will snatch the crown?

This IHS search initiative will be interesting to watch.

Stephen E Arnold, July 29, 2014

Color Changing Ice Cream: The Metaphor for Search Marketing

July 29, 2014

I read “Scientist Invents Ice Cream That Changes Colour As You Lick It.” The write up struck me as a nearly perfect metaphor for enterprise search and retrieval. First, let’s spoon the good stuff from the innovation tub:

Science might be busy working on interstellar travel and curing disease but that doesn’t mean it can’t give some time to ice cream. Specifically making it better visually.

The idea is that ice cream has an unsatisfactory user interface. (Please, do not tell that to the neighbor’s six year old.)

Spanish physicist and electronic engineer Manuel Linares has done exactly that. He’s managed to invent an ice cream that changes colour as you lick it. The secret formula is made entirely from natural ingredients…Before being served, the ice cream is a baby blue colour. The vendor serves and adds a spray of “love elixir”…Then as you lick the ice cream it will change into other colours.

My Eureka! moment took place almost instantly. As enterprise search vendors whip up ever more fantastic capabilities for key word matching and synonym expansion, basic search gets sprayed with “love elixir.” As the organization interacts with the search box, the search results are superficially changed.

The same logic that improves the user experience with ice cream has been for decades the standard method of information retrieval vendors.

But it is still ice cream, right?

Isn’t search still search with the same characteristics persistent for the last four or five decades?

Innovation defines modern life and search marketing.

Stephen E Arnold, July 29, 2014

Google and Findability without the Complexity

July 28, 2014

Shortly after writing the first draft of Google: The Digital Gutenberg, “Enterprise Findability without the Complexity” became available on the Google Web site. You can find this eight page polemic at or you can search for the title on—what else?—

Six years after the document became available, Google’s anonymous marketer/writer raised several interesting points about enterprise search. The document appeared just as the enterprise search sector was undergoing another major transformation. Fast Search & Transfer struggled to deliver robust revenues and a few months before the Google document became available, Microsoft paid $1.2 billion for what was another enterprise search flame out. As you may recall, in 2008, Convera was essentially non operational as an enterprise search vendor. In 2005, Autonomy bought the once high flying Verity and was exerting its considerable management talent to become the first enterprise search vendor to top $500 million in revenues. Endeca was flush with Intel and SAP cash, passing on other types of financial instruments due to the economic downturn. Endeca lagged behind Autonomy in revenues and there was little hope that Endeca could close the gap between it and Autonomy.

Secondary enterprise search companies were struggling to generate robust top line revenues. Enterprise search was not a popular term. Companies from Coveo to Sphinx sought to describe their information retrieval systems in terms of functions like customer support or database access to content stored in MySQL. Vivisimo donned a variety of descriptions, culminating in its “reinvention” as a Big Data tool, not a metasearch system with a nifty on the fly clustering algorithm. IBM was becoming more infatuated with open source search as a way to shift development an bug fixes to a “community” working for the benefit of other like minded developers.


Google’s depiction of the complexity of traditional enterprise search solutions. The GSA is, of course, less complex—at least on the surface exposed to an administrator.

Google’s Findability document identified a number of important problems associated with traditional enterprise search solutions. To Google’s credit, the company did not point out that the majority of enterprise search vendors (regardless of the verbal plumage used to describe information retrieval) were either losing money or engaged in a somewhat frantic quest for financing and sales).

Here are the issues Google highlighted:

  • User of search systems are frustrated
  • Enterprise search is complex. Google used the word “daunting”, which was and still is accurate
  • Few systems handle file shares, Intranets, databases, content management systems, and real time business applications with aplomb. Of course, the Google enterprise search solution does deliver on these points, asserted Google.

Furthermore, Google provides integrated search results. The idea is that structured and unstructured information from different sources are presented in a form that Google called “integrated search results.”

Google also emphasized a personalized experience. Due to the marketing nature of the Findability document, Google did not point out that personalization was a feature of information retrieval systems lashed to an alert and work flow component. Fulcrum Technologies offered a clumsy option for personalization. iPhrase improved on the approach. Even Endeca supported roles, important for the company’s work at Fidelity Investments in the UK. But for Google, most enterprise search systems were not personalizing with Google aplomb.

Google then trotted out the old chestnuts gleaned from a lunch discussion with other Googlers and sifting competitors’ assertions, consultants’ pronouncements, and beliefs about search that seemed to be self-evident truths; for example:

  • Improved customer service
  • Speeding innovation
  • Reducing information technology costs
  • Accelerating adoption of search by employees who don’t get with the program.

Google concluded the Findability document with what has become a touchstone for the value of the Google Search Appliance. Kimberly Clark, “a global health and hygiene company,” reduced administrative costs for indexing 22 million documents. The costs of the Google Search Appliance, the consultant fees, and the extras like GSA fail over provisions were not mentioned. Hard numbers, even for Google, are not part of the important stuff about enterprise search.

One interesting semantic feature caught my attention. Google does not use the word knowledge in this 2008 document.

Several questions:

  1. Was Google unaware of the fusion of information retrieval and knowledge?
  2. Does the Google Search Appliance deliver a laundry list of results, not knowledge? (A GSA user has to scan the results, click on links, and figure out what’s important to the matter at hand, so the word “knowledge” is inappropriate.)
  3. Why did Google sidestep providing concrete information about costs, productivity, and the value of indexing more content that is allegedly germane to a “personalized” search experience? Are there data to support the implicit assertion “more is better.” Returning more results may mean that the poor user has to do more digging to find useful information. What about a few, on point results? Well, that’s not what today’s technology delivers. It is a fiction about which vendors and customers seem to suspend disbelief.

With a few minor edits—for example, a genuflection to “knowledge—this 2008 Findability essay is as fresh today as it was when Google output its PDF version.

Several observations:

First, the freshness of the Findability paper underscores the staleness and stasis of enterprise search in the past six years. If you scan the free search vendor profiles at, explanations of the benefits and functions of search from the 1980s are also applicable today. Search, the enterprise variety, seems to be like a Grecian urn which “time cannot wither.”

Second, the assertions about the strengths and weaknesses of search were and still are presented without supporting facts. Everyone in the enterprise search business recycles the same cant. The approach reminds me of my experience questioning a member of a sect. The answer “It just is…” is simply not good enough.

Third, the Google Search Appliance has become a solution that costs as much, if not more, than other big dollar systems. Just run a query for the Google Search Appliance on and check out the options and pricing. Little wonder than low cost solutions—whether they are better or worse than expensive systems—are in vogue. Elasticsearch and Searchdaimon can be downloaded without charge. A hosted version is available from and is relatively free of headaches and seven figure charges.

Net net: Enterprise search is going to have to come up with some compelling arguments to gain momentum in a world of Big Data, open source, and once burned twice shy buyers. I wonder why venture / investment firms continue to pump money into what is same old search packaged with decades old lingo.

I suppose the idea that a venture funded operation like Attivio, BA Insight, Coveo, or any other company pitching information access will become the next Google is powerful. The problem is that Google does not seem capable of making its own enterprise search solution into another Google.

This is indeed interesting.

Stephen E Arnold, July 28, 2014

Pre Oracle InQuira: A Leader in Knowledge Assessment?

July 28, 2014

Oracle purchased InQuira in 2011. One of the writers for Beyond Search reminded me that Beyond Search covered the InQuira knowledge assessment marketing ploy in 2009. You can find that original article at

InQuira’s technology is an option in the Oracle RightNow customer support system. RightNow was purchased by Oracle in 2001. For those who are the baseball card collectors of enterprise search, you know that RightNow purchased Q-Go technology to make its customer support system more intuitive, intelligent, and easier to use. (Information about Q-Go is at

InQuira’s technology is not cut from a single chunk of Styrofoam. InQuira was formed in 2002 by fusing the Answerfriend, Inc. and Electric Knowledge, Inc. systems. InQuira was positioned as a question answering system. For years, Yahoo relied on InQuira to deliver answers to Yahooligans seeking help with Yahoo’s services. InQuira also provided the plumbing to InQuira hopped on the natural language processing bandwagon and beat the drum until it layered on “knowledge” as a core functionality. The InQuira technology was packaged as a “semantic processing engine.”

InQuira used its somewhat ponderous technology along with AskJeeves’ type short cuts to improve the performance of its system. The company narrowed its focus from “boil the ocean search” to a niche focus. InQuira wanted to become the go to system for help desk applications.

InQuira’s approach involved vocabularies. These were similar to the “knowledge bases” included with some versions of Convera. InQuira, according to my files, used the phrase “loop of incompetence.” I think the idea was that traditional search systems did not allow a customer support professional to provide an answer that would make customers happy the majority of the time. InQuira before Oracle emphasized that its system would provide answers, not a list of Google style hits.

The InQuira system can be set up to display a page of answers in the form of sentences snipped from relevant documents. The idea is that the InQuira system eliminates the need for a user to review a laundry list of links.

The word lists and knowledge bases require maintenance. Some tasks can be turned over to scripts, but other tasks require the ministrations of a human who is a subject matter expert or a trained indexer. The InQuira concept knowledge bases also requires care and feeding to deliver on point results. I would point out that this type of knowledge care is more expensive than a nursing home for a 90 year old parent. A failure to maintain the knowledge bases usually results in indexing drift and frustrated users. In short, the systems are perceived as not working “like Google.”

Why is this nitty gritty important? InQuira shifted from fancy buzzwords as the sharp end of its marketing spear to the more fuzzy notion of knowledge. The company, beginning in late 2008, put knowledge first and the complex, somewhat baffling technology second. To generate sales leads, InQuira’s marketers hit on the idea of a “knowledge assessment.”

The outcome of the knowledge marketing effort was the sale of the company to Oracle in mid 2011. At the time of the sale, InQuira had an adaptor for Oracle Siebel. Oracle appears to have had a grand plan to acquire key customer support search and retrieval functionality. Armed with technology that was arguably better than the ageing Oracle SES system, Oracle could create a slam dunk solution for customer support applications.

Since the application, many search vendors have realized that some companies were not ready to write a Moby Dick sized check for customer support search. Search vendors adopted the lingo of InQuira and set out to make sales to organizations eager to reduce the cost of customer support and avoid the hefty license fees some vendors levied.

What I find important about InQuira are:

  1. It is one of the first search engines to be created by fusing two companies that were individually not able to generate sustainable revenue
  2. InQuira’s tactic to focus on customer support and then add other niche markets brought more discipline to the company’s message than the “one size fits all” that was popular with Autonomy and Fast Search.
  3. InQuira figured out that search was not a magnetic concept. The company was one of the first to explain its technology, benefits, and approach in terms of a nebulous concept; that is, knowledge. Who knows what knowledge is, but it does seem important, right?
  4. The outcome of InQuira’s efforts made it possible for stakeholders to sell the company to Oracle. Presumably this exist was a “success” for those who divided up Oracle’s money.

Net net: Shifting search and content processing to knowledge is a marketing tactic. Will it work in 2014 when search means Google? Some search vendors who have sold their soul to venture capitalists in exchange for millions of jump start dollars hope so.

My thought is that knowledge won’t sell information retrieval. Once a company installs a search systems, users can find what they need or not. Fuzzy does not cut it when users refuse to use a system, scream for a Google Search Appliance, or create a work around for a doggy system.

Stephen E Arnold, July 28, 2014

From Search to Sentiment

July 28, 2014

Attivio has placed itself in the news again, this time for scoring a new patent. Virtual-Strategy Magazine declares, “Attivio Awarded Breakthrough Patent for Big Data Sentiment Analysis.” I’m not sure “breakthrough” is completely accurate, but that’s the language of press releases for you. Still, any advance can provide an advantage. The write-up explains that the company:

“… announced it was awarded U.S. Patent No. 8725494 for entity-level sentiment analysis. The patent addresses the market’s need to more accurately analyze, assign and understand customer sentiment within unstructured content where multiple brands and people are referenced and discussed. Most sentiment analysis today is conducted on a broad level to determine, for example, if a review is positive, negative or neutral. The entire entry or document is assigned sentiment uniformly, regardless of whether the feedback contains multiple comments that express a combination of brand and product sentiment.”

I can see how picking up on nuances can lead to a more accurate measurement of market sentiment, though it does seem more like an incremental step than a leap forward. Still, the patent is evidence of Attivio’s continued ascent. Founded in 2007 and headquartered in Massachusetts, Attivio maintains offices around the world. The company’s award-winning Active Intelligence Engine integrates structured and unstructured data, facilitating the translation of that data into useful business insights.

Cynthia Murrell, July 28, 2014

Sponsored by, developer of Augmentext

Searchcode Is a Valuable Resource for Developers

July 28, 2014

Here is a useful tool that developers will want to bookmark: searchcode does just what its name suggests—paste in a snippet of code, and it returns real-world examples of its use in context. Great for programming in an unfamiliar language, working to streamline code, or just seeing how other coders have approached a certain function. The site’s About page explains:

“Searchcode is a free source code and documentation search engine. API documentation, code snippets and open source (free software) repositories are indexed and searchable. Most information is presented in such a way that you shouldn’t need to click through, but can if required.”

Searchcode pulls its samples from Github, Bitbucket, Google Code, Codeplex, Sourceforge, and the Fedora Project. There is a way to search using special characters, and users can filter by programming language, repository, or source. The tool is the product of one Sydney-based developer, Ben E. Boyter, and is powered by open-source indexer Sphinx Search. Many, many more technical details about searchcode can be found at Boyter’s blog.

Cynthia Murrell, July 28, 2014

Sponsored by, developer of Augmentext

Snowden Effect on Web Search

July 27, 2014

If you are curious about the alleged impact of intercepts and monitoring on search, you will want to read “Government Surveillance and Internet Search Behavior.” You may have to pay to access the document. Here’s a passage I noted:

In the U. S., this was the main subset of search terms that were affected. However, internationally there was also a drop in traffic for search terms that were rated as personally sensitive.

Stephen E Arnold, July 27, 2014

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