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Category: Semantic technologies (Page 23 of 72)

Our coverage of semantic technologies goes back to the early 90s when search engines focused on searching structured data in databases were looking to provide support for searching unstructured or semi-structured data. This early Gilbane Report, Document Query Languages – Why is it so Hard to Ask a Simple Question?, analyses the challenge back then.

Semantic technology is a broad topic that includes all natural language processing, as well as the semantic web, linked data processing, and knowledge graphs.


Classifying Searchers – What Really Counts?

I continue to be impressed by the new ways in which enterprise search companies differentiate and package their software for specialized uses. This is a good thing because it underscores their understanding of different search audiences. Just as important is recognition that search happens in a context, for example:

  • Personal interest (enlightenment or entertainment)
  • Product selection (evaluations by independent analysts vs. direct purchasing information)
  • Work enhancement (finding data or learning a new system, process or product)
  • High-level professional activities (e-discovery to strategic planning)

Vendors understand that there is a limited market for a product or suite of products that will satisfy every budget, search context and the enterprise’s hierarchy of search requirements. Those who are the best focus on the technological strengths of their search tools to deliver products packaged for a niche in which they can excel.

However, for any market niche excellence begins with six basics:

  • Customer relationship cultivation, including good listening
  • Professional customer support and services
  • Ease of system installation, implementation, tuning and administration
  • Out-of-the box integration with complementary technologies that will improve search
  • Simple pricing for licensing and support packages
  • Ease of doing business, contracting and licensing, deliveries and upgrades

While any mature and worthy company will have continually improved on these attributes, there are contextual differentiators that you should seek in your vertical market:

  • Vendor subject matter expertise
  • Vendor industry expertise
  • Vendor knowledge of how professional specialists perform their work functions
  • Vendor understanding of retrieval and content types that contribute the highest value

At a recent client discussion the application of a highly specialized taxonomy was the topic. Their target content will be made available on a public facing web site and also to internal staff. We began by discussing the various categories of terminology already extracted from a pre-existing system.

As we differentiated how internal staff needed to access content for research purposes and how the public is expected to search, patterns emerged for how differently content needs to be packaged for each constituency. For you who have specialized collections to be used by highly diverse audiences, this is no surprise. Before proceeding with decisions about term curation and determining the granularity of their metadata vocabulary, what has become a high priority is how the search mechanisms will work for different audiences.

For this institution, internal users must have pinpoint precision in retrieval on multiple facets of content to get to exactly the right record. They will be coming to search with knowledge of the collection and more certainty about what they can expect to find. They will also want to find their target(s) quickly. On the other hand, the public facing audience needs to be guided in a way that leads them on a path of discovery, navigating through a map of terms that takes them from their “key term” query through related possibilities without demanding arcane Boolean operations or lengthy explanations for advanced searching.

There is a clear lesson here for seeking enterprise search solutions. Systems that favor one audience over another will always be problematic. Therefore, establishing who needs what and how each goes about searching needs to be answered, and then matched to the product that can provide for all target groups.

We are in the season for conferences; there are a few next month that will be featuring various search and content technologies. After many years of walking exhibit halls and formulating strategies for systematic research and avoiding a swamp of technology overload, I try now to have specific questions formulated that will discover the “must have” functions and features for any particular client requirement. If you do the same, describing a search user scenario to each candidate vendor, you can then proceed to ask: Is this a search problem your product will handle? What other technologies (e.g. CMS, vocabulary management) need to be in place to ensure quality search results? Can you demonstrate something similar? What would you estimate the implementation schedule to look like? What integration services are recommended?

These are starting points for a discussion and will enable you to begin to know whether this vendor meets the fundamental criteria laid out earlier in this post. It will also give you a sense of whether the vendor views all searchers and their searches as generic equivalents or knows that different functions and features are needed for special groups.

Look for vendors for enterprise search and search related technologies to interview at the following upcoming meetings:

Enterprise Search Summit, New York, May 10 – 11 […where you will learn strategies and build the skill sets you need to make your organization’s content not only searchable but “findable” and actionable so that it delivers value to the bottom line.] This is the largest seasonal conference dedicated to enterprise search. The sessions are preceded by separate workshops with in-depth tutorials related to search. During the conference, focus on case studies of enterprises similar to yours for better understanding of issues, which you may need to address.

Text Analytics Summit, Boston, May 18 – 19 I spoke with Seth Grimes, who kicks off the meeting with a keynote, asking whether he sees a change in emphasis this year from straight text mining and text analytics. You’ll have to attend to get his full speech but Seth shared that he see a newfound recognition that “Big Data” is coming to grips with text source information as an asset that has special requirements (and value). He also noted that unstructured document complexities can benefit from text analytics to create semantic understanding that improves search, and that text analytics products are rising to challenge for providing dynamic semantic analysis, particularly around massive amounts of social textual content.

Lucene Revolution, San Francisco, May 23 – 24 […hear from … the foremost experts on open source search technology to a broad cross-section of users that have implemented Lucene, Solr, or LucidWorks Enterprise to improve search application performance, scalability, flexibility, and relevance, while lowering their costs.] I attended this new meeting last year when it was in Boston. For any enterprise considering or leaning toward implementing open source search, particularly Lucene or Solr, this meeting will set you on a path for understanding what that journey entails.

ETL and Building Intelligence Behind Semantic Search

A recent inquiry about a position requiring ETL (Extraction/Transformation/Loading) experience prompted me to survey the job market in this area. It was quite a surprise to see that there are many technical positions seeking this expertise, plus experience with SQL databases, and XML, mostly in healthcare, finance or with data warehouses. I am also observing an uptick in contract positions for metadata and taxonomy development.

My research on Semantic Software Technologies placed me on a path for reporters and bloggers to seek my thoughts on the Watson-Jeopardy story. Much has been written on the story but I wanted to try a fresh take on the meaning of it all. There is a connection to be made between the ETL field and building a knowledgebase with the smarts of Watson. Inspiration for innovation can be drawn from the Watson technology but there is a caveat; it involves the expenditure of serious mental and computing perspiration.

Besides baked-in intelligence for answering human questions using natural language processing (NLP) to search, an answer-platform like Watson requires tons of data. Also, data must be assembled in conceptually and contextually relevant databases for good answers to occur. When documents and other forms of electronic content are fed to a knowledgebase for semantic retrieval, finely crafted metadata (data describing the content) and excellent vocabulary control add enormous value. These two content enhancers, metadata and controlled vocabularies, can transform good search into excellent search.

The irony of current enterprise search is that information is in such abundance that it overwhelms rather than helps findability. Content and knowledge managers can’t possibly contribute the human resources needed to generate high quality metadata for everything in sight. But there are numerous techniques and technologies to supplement their work by explicitly exploiting the mountain of information.

Good content and knowledge managers know where to find top quality content but may not know that, for all common content formats, there are tools to extract key metadata embedded (but hidden) in it. Some of these tools can also text mine and analyze the content for additional intelligent descriptive data. When content collections are very large but too small to justify (under a million documents) the most sophisticated and complex semantic search engines, ETL tools can relieve pressure on metadata managers by automating a lot of mining, extracting entities and concepts needed for good categorization.

The ETL tool array is large and varied. Platform tools from Microsoft (SSIS) and IBM (DataStage) may be employed to extract, transform and load existing metadata. Other independent products such as those from Pervasive and SEAL may contribute value across a variety of platforms or functional areas from which content can be dramatically enhanced for better tagging and indexing. The call for ETL experts is usually expressed in terms of engineering functions who would be selecting, installing and implementing these products. However, it has to be stressed that subject and content experts are required to work with engineers. The role of the latter is to help tune and validate the extraction and transformation outcomes, making sure terminology fits function.

Entity extraction is one major outcome of text mining to support business analytics, but tools can do a lot more to put intelligence into play for semantic applications. Tools that act as filters and statistical analyzers of text data warehouses will help reveal terminology for use in building specialized controlled vocabularies for use in auto-categorization. A few vendors that are currently on my radar to help enterprises understand and leverage their content landscape include EntropySoft Content ETL, Information Extraction Systems, Intelligenx, ISYS Document Filters, RAMP, and XBS, something here for everyone.

The diversity of emerging applications is a leading indicator that there is a lot of innovation to come with all aspects of ETL. While RAMP is making headway with video, another firm with a local connection is Inforbix. I spoke with a co-founder, Oleg Shilovitsky for my semantic technology research last year before they launched. As he then asserted, it is critical to preserve, mine and leverage the data associated with design and manufacturing operations. This area has huge growth potential and Inforbix is now ready to address that market.

Readers who seek to leverage ETL and text mining will gain know-how from the cases presented at the 2011 Text Analytics Summit, May 18-19 in Boston. As well, the exhibits will feature products to consider for making piles of data a valuable knowledge asset. I’ll be interviewing experts who are speaking and exhibiting at that conference for a future piece. I hope readers will attend and seek me out to talk about your metadata management and text mining challenges. This will feed ideas for future posts.

Finally, I’m not the only one thinking along these lines. You will find other ideas and a nudge to action in these articles.

Boeri, Bob. Improving Findability Behind the Firewall, 28 slides. Enterprise Search Summit 2010, NY, 05/2010.
Farrell, Vickie. The Need for Active Metadata Integration: The Hard Boiled Truth. DM Direct Newsletter, 09/09/2005, 3p
McCreary, Dan. Entity Extraction and the Semantic Web, Semantic Universe, 01/01/2009
White, David. BI or bust? KMWorld, 10/28/2009, 3p.

How Far Does Semantic Software Really Go?

A discussion that began with a graduate scholar at George Washington University in November, 2010 about semantic software technologies prompted him to follow up with some questions for clarification from me. With his permission, I am sharing three questions from Evan Faber and the gist of my comments to him. At the heart of the conversation we all need to keep having is, how far does this technology go and does it really bring us any gains in retrieving information?

1. Have AI or semantic software demonstrated any capability to ask new and interesting questions about the relationships among information that they process?

In several recent presentations and the Gilbane Group study on Semantic Software Technologies, I share a simple diagram of the nominal setup for the relationship of content to search and the semantic core, namely a set of terminology rules or terminology with relationships. Semantic search operates best when it focuses on a topical domain of knowledge. The language that defines that domain may range from simple to complex, broad or narrow, deep or shallow. The language may be applied to the task of semantic search from a taxonomy (usually shallow and simple), a set of language rules (numbering thousands to millions) or from an ontology of concepts to a semantic net with millions of terms and relationships among concepts.

The question Evan asks is a good one with a simple answer, “Not without configuration.” The configuration needs human work in two regions:

  • Management of the linguistic rules or ontology
  • Design of search engine indexing and retrieval mechanisms

When a semantic search engine indexes content for natural language retrieval, it looks to the rules or semantic nets to find concepts that match those in the content. When it finds concepts in the content with no equivalent language in the semantic net, it must find a way to understand where the concepts belong in the ontological framework. This discovery process for clarification, disambiguation, contextual relevance, perspective, meaning or tone is best accompanied with an interface making it easy for a human curator or editor to update or expand the ontology. A subject matter expert is required for specialized topics. Through a process of automated indexing that both categorizes and exposes problem areas, the semantic engine becomes a search engine and a questioning engine.

The entire process is highly iterative. In a sense, the software is asking the questions: “What is this?”, “How does it relate to the things we already know about?”, “How is the language being used in this context?” and so on.

2. In other words, once they [the software] have established relationships among data, can they use that finding to proceed – without human intervention- to seek new relationships?

Yes, in the manner described for the previous question. It is important to recognize that the original set of rules, ontologies, or semantic nets that are being applied were crafted by human beings with subject matter expertise. It is unrealistic to think that any team of experts would be able to know or anticipate every use of the human language to codify it in advance for total accuracy. The term AI is, for this reason, a misnomer because the algorithms are not thinking; they are only looking up “known-knowns” and applying them. The art of the software is in recognizing when something cannot be discerned or clearly understood; then the concept (in context) is presented for the expert to “teach” the software what to do with the information.

State-of-the-art software will have a back-end process for enabling implementer/administrators to use the results of search (direct commentary from users or indirectly by analyzing search logs) to discover where language has been misunderstood as evidenced by invalid results. Over time, more passes to update linguistic definitions, grammar rules, and concept relationships will continue to refine and improve the accuracy and comprehensiveness of search results.

3. It occurs to me that the key value added of semantic technologies to decision-making is their capacity to link sources by context and meaning, which increases situational awareness and decision space. But can they probe further on their own?

Good point on the value and in a sense, yes, they can. Through extensive algorithmic operations, instructions can be embedded (and probably are for high-value situations like intelligence work), instructing the software what to do with newly discovered concepts. Instructions might then place these new discoveries into categories of relevance, importance, or associations. It would not be unreasonable to then pass documents with confounding information off to other semantic tools for further examination. Again, without human analysis along the continuum and at the end point, no certainty about the validity of the software’s decision-making can be asserted.

I can hypothesize a case in which a corpus of content contains random documents in foreign languages. From my research, I know that some of the semantic packages have semantic nets in multiple languages. If the corpus contains material in English, French, German and Arabic, these materials might be sorted and routed off to four different software applications. Each batch would be subject to further linguistic analysis, followed by indexing with some middleware applied to the returned results for normalization, and final consolidation into a unified index. Does this exist in the real world now? Probably there are variants but it would take more research to find the cases, and they may be subject to restrictions that would require the correct clearances.

Discussions with experts who have actually employed enterprise specific semantic software, underscores the need for subject expertise, and some computational linguistics training coupled with an aptitude for creative inquiry. These scientists informed me that individuals, who are highly multi-disciplinary and facile with electronic games and tools, did the best job of interacting with the software and getting excellent results. Tuning and configuration over time by the right human players is still a fundamental requirement.

Sophia Launches Sophia Search for Intelligent Enterprise Search and Contextual Discovery

Sophia, the provider of contextually aware enterprise search solutions, announced Sophia Search, a new search solution which uses a Semiotic-based linguistic model to identify intrinsic terms, phrases and relationships within unstructured content so that it can be recovered, consolidated and leveraged. Use of Sophia Search is designed to minimize compliance risk and reduce the cost of storing and managing enterprise information. Sophia Search is able to deliver a “three-dimensional” solution to discover, consolidate and optimize enterprise data, regardless of its data type or domain. Sophia Search helps organizations manage and analyze critical information by discovering the themes and intrinsic relationships behind their information, without taxonomies or ontologies, so that more relevant information may be discovered. By identifying both duplicates and near duplicates, Sophia Search allows organizations to effectively consolidate information and minimizing storage and management costs. Sophia Search features a patented Contextual Discovery Engine (CDE) which is based on the linguistic model of Semiotics, the science behind how humans understand the meaning of information in context. Sophia Search is available now to both customers and partners. Pricing starts at $30,000. http://www.sophiasearch.com/

Leveraging Two Decades of Computational Linguistics for Semantic Search

Over the past three months I have had the pleasure of speaking with Kathleen Dahlgren, founder of Cognition, several times. I first learned about Cognition at the Boston Infonortics Search Engines meeting in 2009. That introduction led me to a closer look several months later when researching auto-categorization software. I was impressed with the comprehensive English language semantic net they had doggedly built over a 20+ year period.

A semantic net is a map of language that explicitly defines the many relationships among words and phrases. It might be very simple to illustrate something as fundamental as a small geographical locale and all named entities within it, or as complex as the entire base language of English with every concept mapped to illustrate all the ways that any one term is related to other terms, as illustrated in this tiny subset. Dr. Dahlgren and her team are among the few companies that have created a comprehensive semantic net for English.

In 2003, Dr. Dahlgren established Cognition as a software company to commercialize its semantic net, designing software to apply it to semantic search applications. As the Gilbane Group launched its new research on Semantic Software Technologies, Cognition signed on as a study co-sponsor and we engaged in several discussions with them that rounded out their history in this new marketplace. It was illustrative of pioneering in any new software domain.

Early adopters are key contributors to any software development. It is notable that Cognition has attracted experts in fields as diverse as medical research, legal e-discovery and Web semantic search. This gives the company valuable feedback for their commercial development. In any highly technical discipline, it is challenging and exciting to finding subject experts knowledgeable enough to contribute to product evolution and Cognition is learning from client experts where the best opportunities for growth lie.

Recent interviews with Cognition executives, and those of other sponsors, gave me the opportunity to get their reactions to my conclusions about this industry. These were the more interesting thoughts that came from Cognition after they had reviewed the Gilbane report:

  • Feedback from current clients and attendees at 2010 conferences, where Dr. Dahlgren was a featured speaker, confirms escalating awareness of the field; she feels that “This is the year of Semantics.” It is catching the imagination of IT folks who understand the diverse and important business problems to which semantic technology can be applied.
  • In addition to a significant upswing in semantics applied in life sciences, publishing, law and energy, Cognition sees specific opportunities for growth in risk assessment and risk management. Using semantics to detect signals, content salience, and measures of relevance are critical where the quantity of data and textual content is too voluminous for human filtering. There is not much evidence that financial services, banking and insurance are embracing semantic technologies yet, but it could dramatically improve their business intelligence and Cognition is well positioned to give support to leverage their already tested tools.
  • Enterprise semantic search will begin to overcome the poor reputation that traditional “string search” has suffered. There is growing recognition among IT professionals that in the enterprise 80% of the queries are unique; these cannot be interpreted based on popularity or social commentary. Determining relevance or accuracy of retrieved results depends on the types of software algorithms that apply computational linguistics, not pattern matching or statistical models.

In Dr. Dahlgren’s view, there is no question that a team approach to deploying semantic enterprise search is required. This means that IT professionals will work side-by-side with subject matter experts, search experts and vocabulary specialists to gain the best advantage from semantic search engines.

The unique language aspects of an enterprise content domain are as important as the software a company employs. The Cognition baseline semantic net, out-of-the-box, will always give reliable and better results than traditional string search engines. However, it gives top performance when enhanced with enterprise language, embedding all the ways that subject experts talk about their topical domain, jargon, acronyms, code phrases, etc.

With elements of its software already embedded in some notable commercial applications like Bing, Cognition is positioned for delivering excellent semantic search for an enterprise. They are taking on opportunities in areas like risk management that have been slow to adopt semantic tools. They will deliver software to these customers together with services and expertise to coach their clients through the implementation, deployment and maintenance essential to successful use. The enthusiasm expressed to me by Kathleen Dahlgren about semantics confirms what I also heard from Cognition clients. They are confident that the technology coupled with thoughtful guidance from their support services will be the true value-added for any enterprise semantic search application using Cognition.

The free download of the Gilbane study and deep-dive on Cognition was announced on their Web site at this page.

Semantically Focused and Building on a Successful Customer Base

Dr. Phil Hastings and Dr. David Milward spoke with me in June, 2010, as I was completing the Gilbane report, Semantic Software Technologies: A Landscape of High Value Applications for the Enterprise. My interest in a conversation was stimulated by several months of discussions with customers of numerous semantic software companies. Having heard perspectives from early adopters of Linguamatics’ I2E and other semantic software applications, I wanted to get some comments from two key officers of Linguamatics about what I heard from the field. Dr. Milward is a founder and CTO, and Dr. Hastings is the Director of Business Development.

A company with sustained profitability for nearly ten years in the enterprise semantic market space has credibility. Reactions from a maturing company to what users have to say are interesting and carry weight in any industry. My lines of inquiry and the commentary from the Linguamatics officers centered around their own view of the market and adoption experiences.

When asked about growth potential for the company outside of pharmaceuticals where Linguamatics already has high adoption and very enthusiastic users, Drs. Milward and Hastings asserted their ongoing principal focus in life sciences. They see a lot more potential in this market space, largely because of the vast amounts of unstructured content being generated, coupled with the very high-value problems that can be solved by text mining and semantically analyzing the data from those documents. Expanding their business further in the life sciences means that they will continue engaging in research projects with the academic community. It also means that Linguamatics semantic technology will be helping organizations solve problems related to healthcare and homeland security.

The wisdom of a measured and consistent approach comes through strongly when speaking with Linguamatics executives. They are highly focused and cite the pitfalls of trying to “do everything at once,” which would be the case if they were to pursue all markets overburdened with tons of unstructured content. While pharmaceutical terminology, a critical component of I2E, is complex and extensive, there are many aids to support it. The language of life sciences is in a constant state of being enriched through refinements to published thesauri and ontologies. However, in other industries with less technical language, Linguamatics can still provide important support to analyze content in the detection of signals and patterns of importance to intelligence and planning.

Much of the remainder of the interview centered on what I refer to as the “team competencies” of individuals who identify the need for any semantic software application; those are the people who select, implement and maintain it. When asked if this presents a challenge for Linguamatics or the market in general, Milward and Hastings acknowledged a learning curve and the need for a larger pool of experts for adoption. This is a professional growth opportunity for informatics and library science people. These professionals are often the first group to identify Linguamatics as a potential solutions provider for semantically challenging problems, leading business stakeholders to the company. They are also good advocates for selling the concept to management and explaining the strong benefits of semantic technology when it is applied to elicit value from otherwise under-leveraged content.

One Linguamatics core operating principal came through clearly when talking about the personnel issues of using I2E, which is the necessity of working closely with their customers. This means making sure that expectations about system requirements are correct, examples of deployments and “what the footprint might look like” are given, and best practices for implementations are shared. They want to be sure that their customers have a sense of being in a community of adopters and are not alone in the use of this pioneering technology. Building and sustaining close customer relationships is very important to Linguamatics, and that means an emphasis on services co-equally with selling licenses.

Linguamatics has come a long way since 2001. Besides a steady effort to improve and enhance their technology through regular product releases of I2E, there have been a lot of “show me” and “prove it” moments to which they have responded. Now, as confidence in and understanding of the technology ramps up, they are getting more complex and sophisticated questions from their customers and prospects. This is the exciting part as they are able to sell I2E’s ability to “synthesize new information from millions of sources in ways that humans cannot.” This is done by using the technology to keep track of and processing the voluminous connections among information resources that exceed human mental limits.

At this stage of growth, with early successes and excellent customer adoption, it was encouraging to hear the enthusiasm of two executives for the evolution of the industry and their opportunities in it.

The Gilbane report and a deep dive on Linguamatics are available through this Press Release on their Web site.

Semantic Technology: Sharing a Large Market Space

It is always interesting to talk shop with the experts in a new technology arena. My interview with Luca Scagliarini, VP of Strategy and Business Development for Expert System, and Brooke Aker, CEO of Expert System USA was no exception. They had been digesting my research on Semantic Software Technologies and last week we had a discussion about what is in the Gilbane report.

When asked if they were surprised by anything in my coverage of the market, the simple answer was “not really, nothing we did not already know.” The longer answer related to the presentation of our research illustrating the scope and depth of the marketplace. These two veterans of the semantic industry admitted that the number of players, applications and breadth of semantic software categories is impressive when viewed in one report. Mr. Scagliarini commented on the huge amount of potential still to be explored by vendors and users.

Our conversation then focused on where we think the industry is headed and they emphasized that this is still an early stage and evolving area. Both acknowledged the need for simplification of products to ease their adoption. It must be straightforward for buyers to understand what they are licensing, the value they can expect for the price they pay; implementation, packaging and complementary services need to be easily understood.

Along the lines of simplicity, they emphasized the specialized nature of most of the successful semantic software applications, noting that these are not coming from the largest software companies. State-of-the-art tools are being commercialized and deployed for highly refined applications out of companies with a small footprint of experienced experts.

Expert System knows about the need for expertise in such areas as ontologies, search, and computational linguistic applications. For years they have been cultivating a team of people for their development and support operations. It has not always been easy to find these competencies, especially right out of academia. Aker and Scagliarini pointed out the need for a lot of pragmatism, coupled with subject expertise, to apply semantic tools for optimal business outcomes. It was hard in the early years for them to find people who could leverage their academic research experiences for a corporate mission.

Human resource barriers have eased in recent years as younger people who have grown up with a variety of computing technologies seem to grasp and understand the potential for semantic software tools more quickly.

Expert System itself is gaining traction in large enterprises that have segmented groups within IT that are dedicated to “learning” applications, and formalized ways of experimenting with, testing and evaluating new technologies. When they become experts in tool use, they are much better at proving value and making the right decisions about how and when to apply the software.

Having made good strides in energy, life sciences, manufacturing and homeland security vertical markets, Expert System is expanding its presence with the Cogito product line in other government agencies and publishing. The executives reminded me that they have semantic nets built out in Italian, Arabic and German, as well as English. This is unique among the community of semantic search companies and will position them for some interesting opportunities where other companies cannot perform.

I enjoyed listening and exchanging commentary about the semantic software technology field. However, Expert System and Gilbane both know that the semantic space is complex and they are sharing a varied landscape with a lot of companies competing for a strong position in a young industry. They have a significant share already.

For more about Expert System and the release of this sponsored research you can view their recent Press Release.

Federated Media Acquires Technology Suite from TextDigger

Federated Media Publishing, a “next-generation” media company, announced the acquisition of a platform for semantic and linguistic profiling of web-based content from TextDigger, a San Jose-based semantic search startup. FM provides a full suite of media and marketing services for brand advertisers that depends heavily on a proprietary media and marketing technology platform. TextDigger’s technology complements FM’s platform with a set of semantic solutions for content tagging, filtering and clustering, as well as related tools that enhance the user experience, ad targeting, and semantic search engine optimization for a site. TextDigger will continue its search business, all of TextDigger’s customers will continue to be supported by either FM or TextDigger, depending on the type of project or service. www.federatedmedia.net www.textdigger.com

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