The summit series was the first business-focused conference dedicated to BI on text, to techniques that turn text into data in the service of diverse applications. It remains the best, a testimony to outstanding speakers, great networking opportunities, and the unparalleled importance text plays in the Social, Big Data era.

As chair, I can extend to you a special $300 registration discount, via the code SG12. Use it and hear speakers on customer experience, marketing, e-discovery, financial services, and social-media analytics — from organizations that include American Express, eBay, Fidelity Investments, Maritz Research, Monster.com, NASA, and Walt Disney. Visit www.textanalyticsnews.com for information, and follow the Registration link to register now.

Whether you’re a veteran user or just getting started with text analytics, I hope you’ll join us next month in Boston!

People tell me things. Businesses tell me about needs, budgets, and procurements. Solution providers tell me about product plans, pricing, customers, financials, merger & acquisition activities, pending investments. They suppose, sometimes justifiably, that these matters interest their competitors. The information disclosed in the course of discussions is sensitive and proprietary. Naturally, a company wishes to keep it from others who can use it to competitive advantage, whether a rival, supplier, or customer.

When information is currency, you seek safeguards. NDAs are a mechanism of choice. (I’ll add that unless you’re in broadcast mode (all messaging, no listening), an NDA should be mutual. As a consultant and analyst, I have my own market and technology views and reactions to information presented to me. My views, reactions, and advice have commercial value although it never ceases to amaze me that they do!)

Some believe that lengthy NDAs, replete with legal arabesques, somehow offer the greatest (and minimal acceptable level of) protection. My view is that they not only constitute unnecessary overhead, they may even degrade information security. If they are closely read and fully understood only by the lawyers who drafted them, they are a no-value-added imposition on the folks who actually touch to-be-protected information.

Here’s what I propose, a short, sensible, understandable, and comprehensive(-enough) NDA that responds to the need with the type of protection that really counts, personal commitment –

As a condition of their interaction, the parties recognize that certain information exchanged will be proprietary and sensitive and agree to respect the confidentiality of all such information disclosed in the course of the interaction.

That’s 237 characters, not even two tweets long, but it works for me if it works for you!

Now just don’t get me started on over-reaching contractor agreements…

Drew Robb> Why is visualization becoming so important in BI?

Data visualization helps us quickly grasp both patterns and outliers, via both general-purpose techniques and tools that are specialized for particular information types and business purposes. Visualization has always been important in BI — although never more than now — because it helps us tease business insights from data and effectively communicate what we see.

Drew> What trends are unfolding in this area?

The big news in visualization involves mash-ups, online tools, visual interfaces and immersive experiences, and enhanced reality. Mash-ups join diverse information from multiple sources, typically involving geographic and graphical presentations. Mash-up visualizations are often produced and published online; another class of online visualization tools, pioneered by IBM’s Many Eyes, with Tableau Public a compelling commercialized example and Weave, the Web-based Analysis and Visualization Environment, a latest open-data oriented solution, help a spectrum of data users create graphical magic.

Visual interfaces allow us to guide analyses via clicks and gestures rather than (just) via commands and keystrokes, providing more-natural means of exploring data. Visual interfaces are trending more and more toward immersive. And enhanced reality is the future, already possible to an extent via our mobile devices, where we’re presented with situationally relevant information that enables better-informed decision making.

Drew> What are the various technologies used to facilitate visualization and how do they compare?

There are too many visualization technologies to name, ranging from one-off infographics, to interfaces from exploratory data analysis, to data animations that facilitate telling a story with data. Technologies range from long-standard basic charts to sophisticated network-flow diagrams that allow us to see social behaviors. The best of them are both innovative and understandable.

Visual Complexity is a great place to get an idea of the variety of visualization technologies and approaches out there.

Drew> What about on the vendor front: who seems to be ahead and why and who is lagging behind?

Professionally, I focus on business intelligence and text analytics, so I’ll trot out the usual (and still leading) names, solution providers that include QlikTech, the open-source R project, Tableau, TIBCO Spotfire, and I’ll also cite newer and more-focused entrants that include Advizor Solutions, the spectrum of Google API-fronted services, Miner3D, NodeXL, Palatir, Panopticon, and Tom Sawyer Software. There are many, many choices. Who’s ahead? Visualization developers and users.

Drew> What can we expect to see in the future in visualization?

Immersion and interaction, visualization as a key means of both (and simultaneously) controlling computing devices and enhancing our everyday experiences.

Yet there are too many low-grade tools out there. Sentiment analysis done right is about much, much more than simply scoring tweets and reviews. Sentiment analysis done right discovers business value in customer, consumer, and constituent content and behaviors, whether online, on-social, or in enterprise feedback.

The Sentiment Analysis Symposium, May 8 in New York, is the place to learn more.  This is an authoritative conference that brings together experts and practitioners from research and industry. You’ll have an unmatched opportunity to learn about state-of-the-art technologies, how they are applied, return on investment, and how to choose from among the many available options.

Options help you understand social conversations and also direct and indirect feedback (such as surveys, contact-center notes, and warranty and insurance claims), online news, presentations, even scientific papers: Any information source that captures subjective information.

Advanced analyses monitor and measure sentiment and often much more, linking sentiment to demographics, customer profiles, behaviors, and transactional records. They help business analysts (and marketers, market researchers, customer service and support staff, product managers, and other users) get at root causes.  These are the explanations of behaviors captured in transaction and tracking records. Sentiment analysis means better targeted marketing, faster detection of opportunities and threats, brand-reputation protection, and the ultimate aim, profit.

Social Media revolve around feelings, attitudes, and emotions. Facebook and Twitter are major sources of sentiment (and also of complementary social connectedness data). Facebook and Twitter accounts have profile data attached to them, but nothing that matches the detailed, usably-structured information you can find on LinkedIn. Google is the ultimate information-access engine, capable of bringing together information from a huge variety of disparate sources, including sentiment information such as product, restaurant, and hotel ratings, although when corporations wish to find, mine, and exploit sentiment they need to turn to deeper BI and analytics tools.

There’s no one-size-fits-all sentiment solution, not Google or one of the several as-a-service solutions out there or any of the capable analysis workbenches or social-media analytics tools. Instead, there’s a whole spectrum of sentiment sources and analysis possibilities.

These are a sampling of the topics that will be covered at the May 8 Sentiment Analysis Symposium. You will meet and learn from experts, strategists, practitioners, researchers, and solution providers – experienced and new users and those evaluating solutions. A sample of speakers for the event includes the American Red Cross, Fidelity Investments, Thomson Reuters, American Express, Kraft Foods and Yahoo.

For a crash course on technology concepts, you should also attend the May 7, half-day Practical Sentiment Analysis tutorial, taught by Prof. Bing Liu. (Check out this profile of Bing that appeared in the January 27, 2012 New York Times.)

To register please visit sentimentsymposium.com/registration.html today. See you there!

Yet Pinterest has to be the least-social social platform I’ve encountered in a while, with the business impact of a pin prick, useless except to marketers who are looking for yet another billboard for their brands.

Pinterest is an image-sharing platform. It supports comments and re-sharing but lacks almost every other major social and business-friendly feature I’d seek such as –

• “Find friends” that extends to my LinkedIn and Twitter networks and not just to Facebook and Gmail. My social network and yours span these platforms and more, yet I’d guess the barrier here is business-rooted rather than technical. We socializers (and who isn’t one?) abhor boundaries that block our connectedness, and we very, very often mix our work and play lives. Share and share around.
• Collaborative pinning. Wikis do collaboration. Why not pinboards? Wikipedia shows us that collaborative information-sharing models work. Pinnable media — photos and videos — are, after all, information.
• Tagging. Flickr, Vimeo, YouTube: They all let you apply descriptive labels that will you categorize your content and help others find it.
• Meaningful search, by which I mean search on image content, or at least on the tags that Pinterest doesn’t have, rather than just on descriptive text.
• An indication of license and rights and some form of content-use controls. (This Pinterest gap has drawn a fair amount of attention.)
• Mobility. Pinterest is im-mobile. (The one free, third-party, Android app I tried, Pin’d, wouldn’t even run on my phone.)

And you can’t pin Web pages, docs, or other objects?!

Let’s hope that the company is putting that $37.5 million in funding to good use. Pinning and sharing is a good start, but let’s get social! Pinterest the company would do well to break down platform barriers, support social tagging and collaboration, diversify content, expand search possibilities, and make the site safe for business. But until that good stuff happens, for me, it’s Pinterest Uninterest.

The Oscar Senti-meter rates tweets that cite Academy Awards nominees in the Best Actress, Best Actor, and Best Picture categories and scores them on a more-positive/more-negative scale.

What do I like?

1. The timeline control! Move left (earlier) or right (later) to see tweet volume and sentiment scoring for different dates.
2. The listing of selected external events as part of the time control. Some of are quite relevant, for instance, the January 15 Golden Globe awards. Scroll to that date to see the effect on stats for Meryl Streep, for instance.
3. There are at least some linguistic smarts built into the system (although it’s impossible to determine, by looking, how much). “Merryl Streep” and “dragon tattoo” are resolved to the correct, full names.

And what’s lacking, items for the To Do list for next go-around?

4. Provide click-through to underlying tweets, and not just three, all of them or at least a large sample. The current tool is a tease, and it’s not as if the tweets are private or proprietary. But click-through isn’t, alone, quite enough…
5. Show the sentiment classification of the underlying tweets. The LA Times tells us only that “the Senti-meter combs through a high volume of tweets daily and uses language-recognition technology… to gauge positive, negative and neutral opinions shared in the messages.” I want to see for myself how good the technology is.
6. Animate the timeline control. A simple play button would march the display through the range of dates.
7. Let me trace opinion across dates. All that’s needed is a trail attached to what’s now a bouncing dot although to be done effectively, it would be best to focus on a particular nominee and replace the current horizontal axis with a time axis.
8. Let me filter tweets — for instance, to exclude tweets with the #GoldenGlobe hashtag — and dynamically recalculate based on my filtering.
9. Let me explore how sentiment about Actors, Actresses, and Movies is linked. It would be interesting, for instance, to see if and how tweet volume and sentiment about Meryl Streep and the Iron Lady are linked.
10. I want Sorting, Ranking, and Thresholds. Let me sort nominees, ascending and descending, within the Actors, Actresses, and Movies categories, by number of tweets in a given day and over all days. Let me restrict nominees to the Top N or Bottom M based on number of tweets and on positive or negative sentiment. Let me apply a threshold, for instance, show only nominees who have been mentioned in at least 100 tweets.
11. Content enrichment. I’ve never heard of the movie Carnage, nor of actor Joseph Gordon-Levitt. How about a hyperlink, behind each name, for more information?
12. Explain how the system works!

Frankly, I suspect that Senti-meter is classifying tweets at the message rather than at the feature level, that is, for individual Actresses, Actors, and Movies. I’d be happy to learn otherwise, but there’s no indication about method on the Senti-meter site.

I have one, last ask:

13. Find a way to make Senti-meter useful, more than eye candy.

My last point is seemingly a tough one. The best way to make any information-delivery site or utility more useful is by designing it, from the start, to respond to business needs. If you don’t, the majority reaction will be a yawn, So What? (and I don’t mean the Miles Davis version), a few moments on-page dwell time and then the site visitor is on to the next thing. Is there a business need met by the Oscar Senti-meter? There could be, for instance, in predicting box-office and rental demand. But of course we assume that the Oscar-scoping interface is just a demonstration of the possibilities afforded by the technology and the implementation.

The types of interactivity I’ve described would boost Senti-meter’s business usefulness. So would provision of goal-aligned, beyond-polarity sentiment classification. A plug: We’ll discuss these topics and much more at the up-coming Sentiment Analysis Symposium, a conference I organize, May 7-8 in New York. I do come back to these topics frequently. I wrote about them in a recent review of Neurolingo’s beta Sentibet system. Sports betting, Oscar sentiment: Two peas in a pod. Sentibet differentiates Predictions, Feelings, and Wishes. I may think that Tilda Swinton is a great actress but hope that Michelle Williams wins the Oscar and expect Viola Davis to take the prize this year. Surely this granular, focused level of analysis isn’t beyond IBM’s capabilities, USC Annenberg’s research interests, or the L.A. Times‘s information-delivery mandate.

In sum, my review scores the Oscar Senti-meter moderately negative, with lacking outpolling like by 10 to 3. The IBM-USC-L.A. Times system is a worthy nominee, but not a winner. The players have the tools and resources to create something great, both technically strong and usable for a mass audience. I’m looking forward to the sequel.

I offer observations and a bit of analytics history in response to Gary’s question, “Could the great classical music composer Ludwig Beethoven successfully implement business analytics in an organization?,” followed by my answer. Oh, in case you’re wondering: Business analytics is a collection of techniques and tools that model and extract insight from data, applied for business purposes.

Beethoven (1770-1826) was a near contemporary of the founder of analytics, Carl Friedrich Gauss (1777-1855). Gauss invented key methods that underlie analytics although of course he didn’t write software or design business solutions. Gauss’s work, nonetheless, had contemporaneous applications. Foremost of them was gambling, but his larger mathematical legacy comprises the longest of long tails.

Check out text I found, searching on the two, on Beethoven and Gauss, from a blurb describing a 1970 biography of Gauss by Tord Hall:

“Carl Friedrich Gauss (1777-1855) is generally ranked with Archimedes and Newton as one of the three greatest mathematicians that ever lived. His work, in terms of its all-pervasive importance, its painstaking attention to detail, and its completely developed beauty, somehow reminds one of the work of Beethoven, his contemporary and compatriot.”

Gauss himself built on mathematical methods created, before Beethoven’s birth, by figures such as the Bernoullis (Johann, Jakob, and Daniel) and Leonhard Euler. Society in Beethoven’s time was, in a sense, their heritor, just as we are today. If you do any form of (social-)network analysis, you owe Euler. Take a look at slide 11 of my presentation Text, Content, and Social Analytics: BI for the New World. I illustrate analytical modeling with Euler’s famous Bridges of Konigsberg problem. Daniel Bernoulli’s Fluid Equation, in its elegant simplicity, is as monumental as Beethoven’s tortured Grosse Fuge, but unlike a Beethoven string quartet, it’ll keep you in the air next time you fly the friendly skies.

Beethoven’s life also overlapped those of Joseph-Louis Lagrange (1736-1813) and Siméon Denis Poisson (1781–1840), who each invented techniques that are of central importance in aspects of analytics. I would be stunned to learn that Beethoven gave more than a moment’s passing thought to any of these towering figures, to Lagrange or Poisson, to Euler, the Bernoullis, or Gauss, whose stature in mathematics equals Beethoven’s in music.

So, while I like Gary’s thinking and his ability to see something bigger in the work we do every day, I believe his invocation of Beethoven is fundamentally off track. Beethoven admired revolutionaries, but his admiration was that of a Romantic, for the political and literary. He was not an Analyticist (sorry for that word). I see only the faintest ghost of his compositional methods in business analytics processes.

Beethoven’s affinity for the liberator Napoleon, and for literary figures such as Johann Wolfgang von Goethe and Friedrich Schiller (author of the “Ode to Joy,” set by Beethoven in his Ninth Symphony), is telling. So happens that I read Goethe’s Young Werther just a couple of months ago. It was truly hard for analytically-minded me to identify with Goethe’s emotionally overwrought Sturm und Drang hero, to see any redeeming quality in the character’s self-absorbed obsession. Yet Young Werther and similar works were the rage of Beethoven’s age, even while the Analyticists were creating tools that rationalized the world, enabling the Industrial Revolution and modern business methods.

Simply put –

Beethoven lived in analytics’ seminal period, but he wouldn’t have cared a rat’s ass about business analytics. There is very, very little I see in today’s business-analytics world, or that Beethoven would have seen, that truly fits the Romantic’s conception of revolution or of himself.

By contrast –

Occupy Analytics!

Turn business analytics to public good. It can be done. That would be the movement for Ludwig van Beethoven. Dance like a spinning top.

Our tag line remains unchanged:

The symposium bridges technology and business in one of the most exciting applications to emerge in recent years: software that discovers business value in opinions and attitudes in social media, news, and enterprise feedback.

But our program is better than ever. Keynotes kick off the morning sessions:

• Prof. Jan Wiebe of the Univ of Pittsburgh is a sentiment-analysis pioneer. She will speak on Sentiment, Subjectivity, and Sense, providing a leading researcher’s view of the state of sentiment, and where we’re heading.
• Drinking from the Fire Hose authors Chris Frank (American Express) and Paul Magnone (Openet Telecom) are slated to tackle (working title) Emotional Versus Rational in Customer Decision Making.

If you’re sold, click here to visit the registration page.

The Sentiment Analysis Symposium series has drawn steadily growing numbers, from 95 attendees in April 2010 to 120 (capacity) in April 2011 to 154 in San Francisco last November. (The linked text will take you to pages with videos of the last two symposiums’ presentations.) We’re both building on past success and aiming to keep the program fresh, on the leading edge of sentiment technologies and applications. The symposium is not just another Social Media Blah, Blah, Blah show. Instead, it showcases ground-breaking sentiment-analysis applications and provides unique learning opportunities.

Learning is why, in addition to talks by Jan Wiebe and a new presentation by Prof. Ronen Feldman of Hebrew University, who co-founded solution provider Digital Trowel — Feldman will speak on Unsupervised Learning: Insight in Business News, Medical Forums, and Public Filings — we’ll once again have a half-day, day-before Practical Sentiment Analysis tutorial, on May 7, this year taught by Prof. Bing Liu, another sentiment/opinion-mining expert. Liu was featured in a January 26 New York Times article, For $2 a Star, an Online Retailer Gets 5-Star Product Reviews.

Other May 8 speakers include two of our San Francisco symposium panelists, Banafsheh Ghassemi of the American Red Cross (A Multi-Channel Proposition: Customer Sentiment and (Much) More) and Carol Haney, now with Toluna (Tween Pants Cut Too Low!! (or, Combine Survey Research & Social Monitoring to Discover the Unknown)). (Carol’s original title was Tween Butt-Crack: Finding new trends of dissatisfaction by integrating social media data with open-end data, but I asked her to make it both more and less descriptive.) Rich Brown from Thomson Reuters is another repeat, a highly rated presenter at the April 2010 symposium. Brown’s title this go-around is Forecasting Financial Market Response from News and Social Media Sentiment.

New to the symposium, we have Andera Gadeib, CEO of German-based Dialego AG, speaking on Market Research Beyond Sentiment: Differentiating the Engaged and Pleased.

You may have noticed an interest in talks that tell you how to use sentiment analysis and how to combine it with data from diverse online and enterprise sources, and on beyond positive/negative analysis that help you understand and exploit emotion, mood, and opinion in the spectrum of sources. We also focus on User Perspectives in a series of three shorter presentations followed by a panel discussion:

• Can automated sentiment analysis be less accurate than flipping a coin? — Dana Jacob, Yahoo
• Who are the most loyal customers? — Sobhan Hota, Fidelity Investments
• “How Can I Listen If I’m Talking?”: The Power Of Social Media Listening — Frank Cotignola, Kraft Foods

As usual, we’ll have a series of 5-minute lightning talks just before the lunch break:

• Sentiment As A Service — Michael Tupanjanin, Metavana
• Political Sentiment Analysis — Dr. Stuart W. Shulman, Texifter
• Sentiment Visualization — Vsevolod Gavrilyuk, SemanticForce (tentative)
• Data-driven Sentiment Analysis of Financial News — Matt Sommer, MarketChorus Inc.

And we have a new Innovators & Innovation panel to close this year’s program, with Leslie Barrett (The Ladders), Prof. Bing Liu (University of Illinois at Chicago), and Romi Mahajan (Metavana).

Lastly, in order to maximize networking opportunities, we’re once again planning evening-before and post-symposium receptions.

I’m really looking forward to the May 8 symposium. I hope you’ll consider joining us. If you have questions do get in touch, and I hope to see you there.

P.S. Thank you to sponsors Attensity, Lexalytics, and NetBase (there are others pending), whose support makes the symposium possible.

Here are my planned panel intro and starter discussion questions, shared in the hope that they, on their own, will provide insights into questions.

Panel intro

Our brief is to look at “Analyzing the vast amount of readily accessible data that flows constantly across the internet uncovers details, information and relationships that were unavailable a few years ago. This panel will examine methods and practices to glean sentiment from words and text, look at using this data to predict the future and discuss what information social networks can reveal – all accomplished with no limitation on language and on a real-time or near real-time basis.”

There’s a huge amount in that assignment. I count a dozen notions that are worth exploring. Start with “vast amount,” “readily accessible,” “data”,” “flows,” “constantly,” “analyzing… to uncover details, information, and relationships,” “unavailable a few years ago.” Then there’s “sentiment,” “predict the future,” “information social networks can reveal,” “no limitation on language,” “real-time or near real-time.”

That makes twelve notions (putting aside that some of them aren’t even atomic), or perhaps the count is muliplied given the interplay among individual notions. How do we detect “events” in “flows” and use them to “predict the future”? How is “sentiment” “data”? Is it truly “readily accessible,” and is there really “no limitation on language,” particularly when seeking to understand subjective information such as sentiment?

Let’s hear what our panelists have to say on the these points such as these, in particular as relates to today’s theme, Real Time Awareness.

… and Questions

It’s my job as moderator to prompt an interesting conversation. These questions will, I hope, serve the purpose. My expectation and hope, by the way, is that we’ll get through only a few of them. Here they are:

1. Let’s start with sentiment. What role do sentiment, opinion, emotions, attitudes — various forms of subjectivity — play in analyses of the online and social worlds?
2. Say you’re an analyst tasked with some business or research function (and I do include here study and formulation of policy, program analysis, intelligence, political strategy, and so on). There’s lots of information in text: “named entities” (people, place, organizations, and so on), geolocation, events, sentiment and opinion, identity clues, and so on. And then there are those imperatives: “real time,” prediction, flows. Where do you start, that is, what are the most important elements to understand, and the most important technical capabilities to have?
3. We’re interested in social networks. Well, myself, I don’t view Facebook or Twitter as a social *network*. Instead, they’re platforms where networks consist of connected individuals and organizations whose links are rarely limited to any single platform. Certain technologies provide the ability to track individuals across platforms although they’re as-yet controversial. Anyway, to my question for you: How do analysis of content and of networks mesh up? Analytically, how do you match what people say to their actions and interactions? What can be learned from this sort or analysis?
4. Has there been anything really cool, on the language technology front, that has emerged recently? IBM Watson, Siri, Wolfram Alpha? Something else? Only one rule for responses to this question: Please tell us about something other than what you’ve been working on yourself.
5. How important, and how doable, are cross-lingual or multi-lingual analyses?
6. There’s a temporal dimension to our analyses. Information sources capture, and are themselves, events. Patterns both simple and complex emerge from studying sources over time. Even the meaning of information evolves, both because today’s observer has different concerns from yesterday’s and because language changes over time. What’s your view on temporality and temporal analyses?
7. In our discussion before today, some of you wanted to talk about the interplay between technical approaches and social-science techniques. Please tell us about that interplay.
8. How is our field evolving? Where have we been and where are we heading? How do we make our tools more relevant now and more adaptable to emerging needs?

There is a difference. Let’s explore it via the definitions that follow, first of text analytics, then content analytics, and finally content enrichment and where the ensemble takes us. First definition –

Text analytics is a set of software and transformational steps that discover business value in “unstructured” text. (Analytics in general is a process, not just algorithms and software.) The aim is to improve automated text processing, whether for search, classification, data and opinion extraction, business intelligence, or other purposes.

To expand on this definition a bit, to bridge from text to the wider content world:

Text analytics draws on data mining and visualization and also on natural-language processing (NLP). Supplement NLP with technologies that recognize patterns and extract information from images, audio, video, and composites and you have content analytics.

The concept of content enrichment is easy to grasp: Every link in this article — Web links are accomplished via the HTML “a” anchor tag — is a bit of content enrichment. And semantic content enrichment? Marie Wallace puts it this way, focusing on text but with concepts that extend to the broad set of content types:

When I think about semantic enrichment, I see it as transforming a piece of content into a linked data source. In order to do this you do indeed need text analytics for entity and relationship extraction, but you need more than that…. A text analytics engine might recognize that [Marie Wallace] is a person, [Ireland] is a place, and Marie comes from Ireland and annotate the entities/relationships found. However when doing semantic enrichment, I would want to convert those annotations to openly addressable URIs that contribute to the linked data cloud.

URIs are uniform resource identifiers, Semantic Web terminology for IDs, unique within a namespace, that name or locate things. Web URLs (e.g., http://whitehouse.gov/) are a type of URI.

Rather than write my own annotation elaboration, I’ll reuse one from the Web site of Ontotext, a semantic-technology developer:

Annotation, or tagging, is about attaching names, attributes, comments, descriptions, etc. to a document or to a selected part in a text. It provides additional information (metadata) about an existing piece of data.

Semantic Annotation goes one level deeper:

• It enriches the unstructured or semi-structured data with a context that is further linked to the structured knowledge of a domain.
• It allows results that are not explicitly related to the original search.

The earliest specific semantic content enrichment reference I’ve encountered is in an Ontotext paper, Towards Semantic Web Information Extraction, presented at the 2003 International Semantic Web Conference (ISWC). The paper covers work based on Ontotext’s Knowledge and Information Management (KIM) platform, which in turn relies on GATE, the General Architecture for Text Engineering, an open-source text-analysis framework and toolkit, Apache Lucene, and other technologies. The Ontotext folks have other, related papers posted on the company Web site.

The Ontotext materials help explain the role text/content analytics can and should (but doesn’t often enough) play as a Semantic Web generator. The entities, concepts, events, and other features discerned, via content analytics, in text and rich media not only enable smart content; they can also be loaded to knowledge bases (which I won’t get into here, other than to say that systems such as IBM Watson and Wolfram Alpha use them) and Semantic Web triple stores.

There are other solution providers in the content analytics meets semantic annotation/enrichment game. In addition to IBM and Ontotext, they include HP Autonomy, MarkLogic, OpenText, Temis, and the nascent, open-source IKS project. Other vendors offer enterprise-strength building blocks, for instance, SAS via the various SAS Text Analytics components.

I’m sold on this stuff given the business benefits for content producers and content consumers alike. These technologies — and the interplay between analytics and semantics — are key in making sense of the digital universe.