Book of Odds: A Semantic Knowledgebase of Probability

When I think about quotes related to probability, I can't decide which one of these is more profound or I like more:
1) "Probability is the very guide of life" by Marcus Tullius Cicero
2) “Once you eliminate the impossible, whatever remains, no matter how improbable, must be the truth.” by Sherlock  Holmes
3) "Million to one odds happen eight times a day in New York" by Penn Jillette

I am always tempted to choose number three because it is more funny and  if you have lived, worked or visited NewYork city then you can relate to it. But this blog is not about quotes related to probability! It is about Book of Odds: World's first reference on the odds of everyday life or a Knowledgebase of Probability built using Semantic Technologies. It is not another search engine or "yet another knowledgbase" as I explained in one of my earlier blogs. It tells you odds about things which you care about like "odds of surviving in a plane crash" or  "odds of having a heart attack if you are of certain age and ethnicity". Odds, Probability and Chances are used interchangeably in this context -- it is basically the ratio of favourable outcomes to total outcomes. The world had  references like google, wikipedia, Webster Dictionary, encyclopedias and so many other for  almost everyhting but didn't have "Book Of Odds" to understand the probability of an event or an occurence - so the founders had a good business case to start this initiative.

It seems that the project started almost three years back and millions of data points were collected by the team of very talented researchers. Once data have been collected, analyzed, and the odds have been computed, every Odds Statement then undergoes a rigorous quality review, is modeled ontologically (or in simple words by using semantic technologies explained throughout this blog), and finds its place in underlying semantic database of probabilities. The Semantic middleware is provided by Cambridge Semantics, a Semantic Technology company, mentioned in one of my earlier blogs, which structures the data, adds metadata and other information that helps users find the relevant odds statements using simple search. I consider it a very creative use of applying semantic technologies to the problems which could not have been solved using traditional database management systems. This proves again that a typical DBMS is not the answer to all the storage and query related problems! The semantic technology also allows it to find interesting and unexpected connections. It is not only a fun site but aims to be a respected new reference source to increase the tolerance for uncertainity and help people to deal with tough decisions.

To start using Book of Odds, you can browse by topics like health and illness; accidents and death; relationship and society; and daily life and activities. It is understood that it will always remain work in progress as this planet is becoming more complex and eventful everyday. There will always be  new areas to add and grow this reference.

My limited intearction with the website tells me that content is of high quality. I will recommend readers to at least give it a try and encourage this team. The most important thing for "Book of Odds Inc." is to have more people registered and start using this knowledgebase. I will "not" keep on pressurizing the founders at this stage by asking about business models and revenue stream.What business model did google have for many years? I am sure that they know that these questions are going to come up repeatedly after an year of their launch.  If  they will keep on producing high quality content or probabilistic ratios in this case and keep it meaningful for people then good things will happen. They do need to think creatively about advertising and marketing campaigns. They shouldn't be perceived as another google killer! Atleast they were smart enough to not use "engine" in their name. A business model will follow if they can attract more users and keep them interested.  My only recommendation to the founders will be to start thinking about combining their work or knowledgebase with various forms of predictive modeling or business analytics in the business world. Who knows better than companies in this economy that  "By looking at the past we can come to a greater understanding of the world and what is forever an uncertain future!"

How Semantic Technology and Text Extraction can help in listening to "Voice of Customer!"

Businesses succeed by getting, keeping, and growing customers. Customers are the only reason you build factories, hire employees, schedule meetings, lay fibre-optic lines, stock inventory, write software  or engage in any kind of business activity. "Without customers, you don't have a business. You have a hobby." These are not my words but they are explained very nicely by Don Peppers and Martha Rogers in their famous book - Return on Customer. The book is basically about developing a business metric around your customers that can drive better management and higher stock price.

We know that it is becoming increasingly hard to keep this customer as they are no longer hungry for what a business has to offer. They have more choices than ever as far as developed and emerging economies are concerned! At the same time, Social media tools have made it possible for hundreds of millions of people on this planet to express their opinion. And everybody has an opinion about something! If that "something" is not a person or social/political/economic  issue than chances are that it is likely to be a product or a service! It is becoming very important for businesses to understand what their customers are thinking and saying about their products and services. They know that they will lose this customer as customers' shift loyalties very quickly if they are not heard. Businesses call it "Voice of Customer" and have been trying various ways in last few decades like polls, surveys, incentives and some other ways to understand that. There have been many issues with most of the earlier approaches as it is not an exact science and new regulation and privacy issues are making it harder. Marketing and support personnel have been trying hard to keep tabs on real-time conversations regarding their products, services and brands in blogs, Web forums, wikis, microblogging services including Twitter and Facebook. The advancement in text analytics is bringing  a complete new approach to hear VOC.

In a nutshell, "text analytics" is about processing unstructured information in text and transforming it into relevant information. It is a highly sophisticated technology which uses concepts like clustering, categorization, advanced pattern recognition, machine learning, AI,  language identification, semantics, linguistics and relationship extraction . The text processing can extract entities, concepts, events and sentiments from the text and marry it with structured data in databases and data warehouses to get useful business insights. Pharma and intelligence community have been the earliest adapters of this technology as they have compelling need and ROI is very high. In of my earlier blogs, I have also explained how Open Calais, using text analytics, is helping Semantic Technology.

Attensity Group, one of the remaining leaders, after Clearforest and Inxight were acquireed by Reuters and Business Objects respectively, text analytics space, has announced a very interesting product called Attensity Cloud. They built this product by combining their patented text analytic technology with Radian6 platform. Radian6 provides a platform to develop a dashboard, metrics, filtering and segmentation for social media data. According to Attensity Group, the Cloud plaform also uses sematic web technology along with text analytics technology. The Attentisty Cloud can tell things like:

• Which conversations are relevant and active
• Which products are most talked about e.g hamburger vs. salads in case of Mcdonald
• Sentiments behind each product
• Intentions to buy product
• Top issues, cries for help
• product suggestions
• Eearly warning of issues
• Actionable facts

Morever, you can integrate this platform with CRM and e-service to get value across all channels. I think that it is a very good move as it is a very smart idea to tie text analytics technology with semantic technology and applying it to solve a business problem. It can allow organizations to monitor, analyze and respond to social media in more effective and faster way.

Since, the platform is introduced very recently so there are no real customers. It will take a while to understand the power and usefulness of this plaform and how it is working out for Attensity's customers. Nonetheless, the concept is good because it will probably help in customer retention if businesses can integrate, configure and deploy Attensity Cloud successfully along with CRM systems. Because, In the end, any customer needs just few basic things:

• They want you to know who they are
• They want you to know what they need
• They want a reliable way to communicate with you
• They want you understand their definition of value
• They want you to know what they want next
• They want you to support them if they trust you and buy from you
• And they don't want you to ask the same questions

I believe, despite doing all of these things, it is still a challenge to retain a customer if you don't innovate. Do you know the number of customers Apple stole from its competitors, who thought they were doing everything right to hear VOC, by introducing iphone? But thats a different discussion for a future blog!

Do we need to define a "Category" for Semantic Technology in the IT Stack?

I started thinking about 'having a category for semantic technology' more after having a conversation with a friend of mine who is a Vice President of Product development for a software product company. His exact words were : "I believe in Semantic Technology and it is very promising. We will also see lots of innovation in next few years. But it is hard to sell product and services in this space" He really meant it more in context of an enterprise than the WWW. He is probably right somewhere despite his limited exposure to Semantic Technology world.

 Even though there are many semantic technology-based implementations that are happening in the IT industry but it is still a tiny fraction of what is going on with Databases, ERP, CRM, BI, Portals, ECM, SAAS, SOA, Collaboration, Applications Servers, Security, legacy application modernization and so many others disciplines. Semantic Technology is not among top ten priority for any CIO for 2009 or even 2010! Actually, it is not even perceived as a category.  When will it become a priority? Do we need to create a category for semantic technology for the enterprise? Maybe! Selling any product and services in this space can be hard sell if semantic technology industry doesn't do a good job in coming up with a nice and understandable category like ERP, BI, CRM etc.. which is already defined clearly in the minds of business and IT in any company. At least, they don't argue about what it really means. At the same time, we have seen the fate of "knowledge management" category which till this date means different things to different people. Selling it as "Semantic Technology" has the same risk as selling SOA (service oriented arhchitecture) which is always a hard sell by IT to business. It has to sound like a solution to the business who doesn't want to buy another piece of technology. The Semantic Technology community now has to wear their marketing hats.

I really admire at least four instances where defining a category played a huge role in success of one or more companies in that space and subsequently many innovative products and solutions were introduced in the marketplace.

• SAP hijacking MRP and rebranding it as ERP (Enterprise Resource Planning). Later on other disciplines like sales and distribution, warehousing, financial etc. became a part of ERP.
• Siebel, through acquistion, coalesces SFA (Sales Force automation), CCA (Contact Center Application) and marketing automation tools into CRM category.
• BI was coined by Gartner and they re-named catgeories like DSS (Decision Support System) or EIS (Executive Information System)
• The industry went through the confusion of defining a  category for many years for web content mangement, imaging, document management, records management, digital asset management, collaboration etc.. Finally, everybody has settled on ECM (Enterprise Content Management) which is broad and relevant enough. The ECM industry has been on a great growth trajectory since then.

Also,  these four categories are already multi-billion dollar industry now. Having a broader concept and having more players as part of it always helps when anyone is sizing up a category. In matter of months, from small player who is struggling to make few millions on its own, you suddenly look like a part of a growth industry which is already making hundreds of millions. It also makes a CIOs as well as VC community very comfortable.

Defining a right category which sticks and resonates with both business and IT is one of the most important steps for growth and to foster innovation. We really need better positioning for semantic technology! Having a right category doesn't guarantee success but it is the first step towards long-term success. It will definetely accelerate adaption! From timing perspective, the Semantic Technology Industry has to do it now. As Sir Tim Berners Lee says, "We have all the tools to make it happen" - he probably meant web in this context but we know that all the technologies are equally relevant and game changing in the enterprise context also.

Things can quickly change in theIT  industry if you take the examples of SAAS (software as a service model). It was nowhere among the priorties for CIOs 3-4 years back and has already made it into the top 10 list for many of them. There are already more than six hundred vendors who are offering SAAS. Virtualization and Cloud computing also comes to mind in this context as they have also made it into top priorties in a very short span of time. These examples should be enough inspiration for companies who are planning to build products or offer services as Semantic Technology discipline is based on eually good foundation of solid research and development as any of these disciplines mentioned above. Again, right categorization is the key here.

We really need an "Umbrella Term" for Semantic Technology so that companies in this space like Franz (database), Metatomix (platform), TopQudrant(tool), offering ontology services and many others can live peacefully under it. It will also make life so easy for sales, marketing, channels and future enterpreneurs  in the vendor community.

"Business Semantics Management" has been used earlier in a similar but smaller context. Though, personally I think that it has a very broad meaning and can be one of the candidates. It does resonate with business but this name can have its own limitations also. It can be perceived more as just in context of ontology and vocabulary whereas Semantic Tchnology can give you business insights also. It can multiply your ROI on your investments on BI, data management,integration, ECM, search etc..  There can be many meaningful and relevant names for this category which will require more dialogue in the industry.  Once we agree on a category name then it needs to be marketed and socialized relentlessly.
 
We really need a body in the semantic technology like what AIIM is to ECM industry or TDWI is to Business Intelligence to help in this.  W3C is doing great work but its focus is more web, research and standards. We need an organization who can work closely with corporatations, vendor communiy and maybe research to facilitate conversation and ideas.It will be lot of work for this organization if they have to follow the model of AIIM who has done great job in ECM market in education, industry advocacy, marketing, consulting, professional development, peer networking, benchmarking, market intelligence, developing local chapters, best practices, help in developing policies and procedures etc..

Is it GE or Google as proxy for US economy? The role of Search in being a leading indicator!

We know that the best way to understand the state of US economy is by looking at GDP . It wasn't long ago, probably two to three years back, when GE was unanimously considered as proxy for US economy. It was always most diversified company as its businesses spanned from entertainment, medical devices, energy, locomotives, equipment, infrastructure services to finance. It always remained among the leaders in revenue and market capitalization. Unfortunately, it has fallen out of favor in last two years because of its average performance across the board in most of its businesses. It is no longer a leader in market cap. also. The result of all of this is that GE is not considered as a proxy for US economy or their sales pipeline as one of the leading indicator of economy.
I will leave it to economists to debate what is a true economic indicator but recently, Google has started claiming that its search patterns are saying that economy is recovering.  Basically, Google's chief economist says that he can tell the direction of economy from American's search habits.

For e.g Google is seeing following trends:

• Decrease in searches for unemployment benefits
• Increase in searches for homes and real estate agents
• They also showed an early spike in government "cash for clunkers" program.

There can be million other search trends which they might have analyzed or modeled! It seems lot of people are already listening! Well, the part of the reason can be that it wasn't long ago when Google's market cap. became more than GE!
You can always disagree with Google's claim and argue that a  search for "property prices in a neighbourhood of Las Vegas" shouldn't be interpreted as recovery sign. Maybe, the person is trying to to sell the house! In the end, it is very difficult to interpet the true motive from few key words in the search. After all, economic forecast is a serious business and econometric modeling it is a very complex thing!

I think that we will continue to see this trend in future where "search on the web" will continue to indicate at least partially where the economy is heading. As search on the web moves from "keyword search" to more advanced "semantic search", we will have better  idea about the intention of the query.

If we can analyze the "query intent" then we can build better models also. We also have to take into consideration the search patterns from Yahoo, Bing and other search engines to have a complete picture. I will still not dismiss off leading indicators from GE as it remains a great company despite a plunge in its stock value.

Semantic Technology, Financial Reporting and the Toxic Assets!

Financial markets, traditionally the earliest adapter of any new technology relative to other industries, has been a laggard as far as Semantic Technology is concerned. It seems that the turmoil in the capital markets in last two years has managed to dampen enthusiasm for new technologies in capital markets and banking industry. All of it is about to change! The two obvious reasons are: we are coming out of recession and  new regulations regarding financial reporting in XBRL. I believe that the third reason is the inherent limitation of XBRL as far as Semantics is concerned!

As we know, XBRL, solves two significant problems for companies who prepare financial statements along with analysts, investors, regulators, financial publishers and data aggregators:

• The first problem is that preparing a financial statement for printing, for a Web site, and for filing today means that a company could typically enter information three times
• The second problem is that today (if the report is not in XBRL), extracting specified detailed information from a financial statement - for e.g we still can't ask questions like "Give me depreciation expense from 2003 a financial report."

The basic idea behind XBRL is to provide grammer and syntax behind financial reporting so that it can be extracted, analyzed and queried. Although XBRL has been around for 10 years now, the adoption and acceptance has only begun to significantly accelerate during 2007 with the support of  SEC. Since the year 2009, the filing has become mandatory for largest five hundred US corporations and other companies will follow in a phased manner from 2010 onwards. Market is already flooded with XBRL products , services and tools. Most of these products and tools help in one or more of following things : creation, viewing, analysis, taxonomy creation, custom document creation and various other automation features. XBRL can be stored in RDBMS as well as XML databases like Marklogic.


So what is the problem? Why do we need Semantic Technology in this context? While XBRL allows for more accurate consumption and interpretation of financial information, there is still a need to connect to the authentic source of the document and to recombine the XBRL content with other data sources. The fundamental issue here is that XBRL document working with other dat source doesn't understand anything about the semantics of data. There is just no meaning associated with the nesting of tags. The limitation of XBRL becomes more obvious when you have to use/analyze/query XBRL reports along with other sources of data which is not XBRL compliant.

If you read this  article in Wall Street Jornal on Toxic assets then it will make you think more clearly about importance of "semantics" in reporting in the world of derivatives.. The key points are:

1. Ever since humans started trading, lending and investing beyond the confines of the family and the tribe, we have depended on legally authenticated written statements to get the facts about things of value
2. Derivatives are the root of the credit crunch. Why? Unlike all other property paper, derivatives are not required by law to be recorded, continually tracked and tied to the assets they represent. Nobody knows precisely how many there are, where they are, and who is finally accountable for them.
3. Every financial deal must be firmly tethered to the real performance of the asset from which it originated.
4. All documents and the assets and transactions they represent or are derived from must be recorded in publicly accessible registries
5. Governments can encourage assets to be leveraged, transformed, combined, recombined and repackaged into any number of tranches, provided the process intends to improve the value of the original asset
6. Financial institutions will have to serve society and fully report what they own and what they owe -- just like the rest of us -- so that we get the facts necessary to find our way out of the current maze
7. Governments can no longer tolerate the use of opaque and confusing language in drafting financial instruments. Clarity and precision are indispensable for the creation of credit and capital through paper.

XBRL, by itself can't fulfill all of these requirements as we need to corelate/link/resolve various reports to the source data - a very important thing in the world of derivatives reporting. You need Semantic Technology for that! We need to represent XBRL in RDF or OWL representation. I will recommend my readers to read another rebuilding public trust - a nice article on the same topic. The author is also talking about services which can allow the financial data in XBRL to be combined with data from other industry and government sectors — basically, transforming the way we explore information.

There are various techniques to convert an XBRL document to RDF. I will not go into those details in this blog. One example -  GoodMorningResearch.com machine automates XBRL tagging of Excel data in RDF format with one-click Save As XBRL functionality.

I believe that  long-term (probably very long-term) vision of XBRL reports should be to publish it as RDF triples and make it a part of Linkedata cloud. This will help in achieving all linkages, transparency and verification as far as financial reporting is concened. I would like sceptics to know that by April 2009, more than 600 XBRL reports, approx. 1,3 million RDF triplets,  were already part of Linked data cloud.  But at the same time, you need lot more governance, regulations and process behind this effort to get real value. Also, there has to be some kind of incentives for financial organizations to do this.

Yet Another Knowledgebase Knowledgebase?

Well, those who know yet another hacker jargon or have used yacc, will probably guess where I am going with this! Microsoft recently announced that Bing will have its own reference page - http://www.bing.com/reference. Microsoft, with the help of Powerset team behind Bing, is basically positioning Bing reference as nothing short of key source on the web for all reference needs. I am still struggling to understand the purpose/value behind it as if you try to use it then you can see that most of the articles are from wikipedia or freebase. So, basically, it seems that it is not really a knowledgebase. In that case, it can be more of a branding strategy.

The world of web knowledgebases has become more interesting lately. The vision was really set by cyc - started as an artificial intelligence project back in 1984 - which attempted to assemble a comprehensive knowledge base with the goal of enabling AI applications to perform human-like reasoning.   But it ended up being criticized by one of the most controversial endeavors of artifical intelligence history. Wikipedia successfully completed the vision by keeping it simple and used the power of collaboration. Freebase, the effort from metaweb technologies, which is combination of semanticlly enriched wikipedia articles and many other sources like musicbrainz etc.. is already sixty percent more than English wikipedia articles. Despite its size, freebase is still criticized that there are not enough strong use cases to leverage the rich semantic knoweldgebase. Also, I suspect that despite its large size, number of visitors to freebase will be significanlty lower than wikipedia.

Things got more interesting after the release of dbpedia, which is a comunity effort to convert wikipedia into semantic web representation. Now, many folks are saying that dbpedia and freebase should combine their efforts. So after all of this, how can Google, "the database of intentions" as described by John Battele in his famous book - "The Search: Business and Culture in the Age of Google", be far behind! Their knowledge base effort, called Knol - a unit of knoweldge, though still in Beta, is still a mystery to many. It is often compared with wikipedia and some suspect that Knol results will rank higher in search results. I haven't see that though. Many people like Knol as as you are not credited with anything when you contribute something to wikipedia. Whereas, Knol is an open knowledge bank, where you can maintain an account and write what you want. You can edit yourself or allow somebody else to edit the pages there. What's more, you can earn money there by the google adsense program. Money talks!

I haven't even started to talk about WolframAlpha which adds new dimension of knowledgbase computing to the world of knowledgebases. I will save it for another blog.

Only time will tell which knowledgebase(s) will survive.

The Anatomy of an Enterpreneur!

Enterpreneurs are among the most celebrated people in our culture. I came across this interesting study by KAUFMANN - The foundation of enterpreneurship. A lot is known about the celebrity enterpreneurs like Bill Gates, Steve Jobs, Larry Ellison etc. but very little is known about the rest. It is a very interesting study across various industries. It can be a great source of motivation for future enterpreneurs who are reading this. Basically, its never too late for anyone. Some of the interesting findings are:

• Company founders tend to be middle-aged and well-educated, and did better in high school than in college
• 67 percent ranked their academic performance among the top 30 percent of theri undergraduate class, but a smaler percentage (37.5 percent) ranked their performance among the top 10 percent
• The majority of respondents (75.4%) had worked as employees as other companies for more than six years
• Significant percentages of respondents started their first companies after working eleven to fifteen years (23.3 percent), sixteen to twenty years (14.3 percent), or greater than twenty years (10.3 percent) for someone else.

You can get the full report here.

Semantic Search, Lexis-Nexis and the Issue of Trust!

It really caught me by surprise when I heard the news that Lexis-Nexis, a leader in legal publishing, chose Pure Discovery, for its patent research search. Again, I want to remind the readers of this blog that semantic search in this context has to do with 'semantics' only - it has nothing to do with semantic web technology as described by W3 standards. Actually, the business need here is nothing more than what you will typically expect an 'enterprise search' use case to be - Basically, getting the right piece of content or document from a huge corpus. In this case, it is approximately 10 million. I am pretty sure that decision makers and decision influencers in this context were well aware of the enterprise search engines like Fast, Autonomy, Endeca and many others in the crowded category of search engines. All three search engines mentioned above have unique algorithms and can handle the scalability aspect easily. So why did they do this effort collaboratively with a relatively unknown company? The main reason cited here is the issue of transparency! Most search engines expect the end user to blindly trust the relevant results - simply trust the algorithm. But in case of PureDiscovery, each query is enhanced by the machine intelligence and "shown to the user" for their complete understanding and engagement. The user can also control the weightage of the query words in a visual manner called querycloud. It seems that search vendors' saying that "its just magic" didn't work this time

Applying Semantic Technology to cure cancer!

All of us know that in the world of molecular biology, significant research efforts are moving from wet labs to the dry labs. And by dry labs, I mean bioinformatics. It is a relatively a new discipline but a lot of work has been done in last few years.  In a nutshell, bioinformatics is about building an information system using biological data. Semantic Technology is playing a big role in transforming this biological data into connectable information which is giving scientists completely new insight about biological mysteries.

Recently, scientists at The University of Texas, M. D. Anderson Cancer Center got funding to derive meaningful information from an ocean of data about the aberrant genetics that drive human cancers. The work will be primariy focused on parsing the multiple genetic pathways that fuel more than 20 types of cancer. Medical Scientists are going to use semantic technology to solve this puzzle.


For full news:
 http://www.eurekalert.org/pub_releases/2009-10/uotm-mda100709.php

How Semantic Technology is revolutionizing the massive e-Science project!

The researchers at Rensselaer Polytechnic Institute are planning to leverage the semantic technology to solve knowledge acquisition problems for scientists in the scientific domain. Scientists can ask questions outside their domain and get the relevant answers by collaborating with data from various sources. The effort is very similar to the wikipedia except that the data will not reside at one place and all data will be semantically encoded. Each website will semantically represent the scientific data i.e various ontologies will be built for each area.

There are two things which I like about this initiative:
- Raw data is available for everyone, scientists, researchers or a school teacher to access
- It won't be confined to peer review only which is customary in scientific publishing. Even an elementary school teaacher can review the work of a scientist and comment on it.

For full news, please go to: http://news.rpi.edu/update.do

When Cloud Computing meets Semantic Technology!

There is no doubt that Cloud Computing is one of the most talked about topic in the technology world. While the industry is still working on defining it in a better way, we are begining to see examples where cash strapped smaller companies are actually using it to their advantage. Franz, Inc, a company offering semantic database product, called Allegrograph, is the one who leveraged the concept in very cost effective way. They did series of QA tests for their product for $200 by staging its service on Amazon S3 instead of spending 100k on capital expenditure. As a database company, Franz is under constant pressure to improve its technology and maintain a competitive edge over alternatives like open source or RDBMS vendors.

AllegroGraph is suitable for the purpose of storing very large quantities of Semantic data and supporting complex queries. Franz’s unique “Activity Recognition” package combines Semantic data with Geospatial data, Temporal Logic, Social Networking Analytics to allow users to easily combine this information and perform queries that won't work with tradional RDBMS.

For example : “Find all events that happened in last two months within 10 miles of Newyork that were attended by you and your social network.”

Overall, I perceive it as a great news for future enterpreneurs as the cost of doing business is going down dramatically at least for the software startups!


For full news, please go to:

http://www.ddj.com/web-development/220300736

Where is the scope for innovation in Semantic Technology?

The research in the area of semantic technology has been going on for more than a decade. W3C has done a great job in finalizing various standards. So what is the next step? It is of course innovation. We will continue to see new innovative software companies cropping up who will build verticalized semantic solutions for various domains leveraging existing data. There is already so much of data out there that the time has come to develop some new algorithms in context of semantic technology. Who says that software is dead now!

In this context, the company which comes to my mind is Metatomix. The Metatomix platform is combination of rules/policy engine, semantic technologies and enteprise-class data integration. They use semantic technologies for informaition modeling and facilitating data integration; use rules engine to drive behaviour; and use integration platform to work with data, connect to data sources, recevie events etc.. The company has developed a very good solution for law enforcement systems. They have developed actionable insights for justice systems personnel - basically finding the right information and then bringing to the right people at the right time can be life saving in the world of law enforcement. Please go to metatomix website to see video which tells you about a real case. It will take only few minutes. The company also has secured a new round of funding lately.