Concept Searching Webinar
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Concept Searching Webinar

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September 2009 Webinar

September 2009 Webinar

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  • The key points on this slide are:Been in business since 2002, first customers in 2003Major Enterprises with up to 66 000 users have deployed successfully to manage unstructured dataOwned by the Founders – no external investment. Profitable with 35% growth in 2008 and already trading for similar growth in 2009.Increasing number of specialized Partners in this space buying into our value proposition.Concept Searching was founded in 2002 with the goal of developing statistical search and classification products that delivered critical functionality currently unavailable in the marketplace. The products were launched in 2003 and Concept Searching has experienced growth and profitability every year since.  Concept Searching is the only statistical classification software company in the world that uses concept extraction and compound term processing to achieve the highest precision without the loss of recall. Our products are the only solutions that are fully integrated with MOSS and Microsoft Search. In side-by-side comparisons against industry leaders, Concept Searching has been able to dramatically illustrate the strength of the technology. Concept Searching counts an ever growing number of global and Fortune 500 and Fortune 1000 clients. We have built a strong partnership channel with Microsoft Partners. Continuing to invest in product development Concept Searching is defining new standards for the search and classification industry and is committed to delivering quantifiable business benefits to organizations around the world.
  • Traditional search assumes the end user knows what they are looking for, or must enter the ‘right’ combination of words to get the ‘right’ result.Knowledge workers need to identify content in the context of what they are seeking. The fundamental problem with search solutions is that they are based on an index of single words. Yet most queries are expressed in short patterns of words and not single words in isolation – which are highly ambiguous. In the example above, a search engine would identify all the documents that contained the words: triple, heart, bypass instead of documents that contained the concept of ‘triple heart bypass’. Since the concept has been identified, other documents that have related concepts will be identified even if they do not contain that exact phrase. The metadata generation issue is increasingly a growing concern in enterprises. Not only for search but also for records management, compliance, and enterprise content management. A comprehensive approach requires more than syntactic metadata and requiring end users to add rich metadata is haphazard and subjective at best. Since conceptClassifier for SharePoint is no longer restricted to keyword identification, compound term metadata can be automatically generated either when the content is created or ingested. The generation of metadata based on concepts extracts compound terms and keywords from a document or corpus of documents that are highly correlated to a particular concept. By identifying the most significant patterns in any text, these compound terms can then be used to generate non-subjective metadata based on an understanding of conceptual meaning. Compound term processing can address many challenges facing large enterprises and provide many benefits. Identification of concepts within a large corpus of information removes the ambiguity in search, eliminates inconsistent meta-tagging, and automatic classification and taxonomy management based on concept identification simplifies development and on-going maintenance.  
  • “At last a tool set that enables enterprise content be the driver for business productivity”Concept Searching provides a comprehensive suite of tools for the automatic classification and taxonomy management of enterprise content. The ability to identify ‘concepts in context’ generates far richer meta data, improving the precision and relevancy in the information retrieval process.  Concept Searching provides a comprehensive suite of tools for automatic semantic metadata generation, automated classification and taxonomy management of enterprise content. The metadata generation issue is increasingly a growing concern in large enterprises. A comprehensive approach requires more than syntactic metadata (i.e. date, author, title) and requiring end users to add rich metadata is haphazard and subjective at best. Since Concept Searching’s technology is no longer restricted to keyword identification, compound term metadata can be automatically generated either when the content is created or ingested. The generation of metadata based on concepts extracts compound terms and keywords from a document or corpus of documents that are highly correlated to a particular concept. By identifying the most significant patterns in any text, these compound terms can then be used to generate non-subjective metadata based on an understanding of conceptual meaning.The ability to identify ‘concepts in context’ generates far richer meta data, improving the precision and relevancy in the information retrieval process. Meta-tags are automatically added to the properties field of each document making the document more valuable to the organization by increasing the ability of the document to be retrieved using Microsoft Search Products that use keywords and metadata to retrieve information. conceptClassifier for SharePoint is fully integrated with both SharePoint, Microsoft Office, Exchange, FAST and Microsoft Enterprise Search. The automatic extraction of compound terms enables the Subject Matter Expert (SME) to use the terms within the taxonomy generation process, reducing the time to build out and maintain taxonomies by 80%. (Compound Term Processing performs matching on the basis of compound terms as opposed to keywords. Compound terms are built by combining two (or more) simple terms, for example ‘triple’ is a single word term but ‘triple heart bypass’ is a compound term. By identifying and forming compound (multi-word) terms and placing these in the search engine’s index the search can be performed with a greater degree of accuracy because the ambiguity inherent in single words is no longer a problem. A search for ‘survival rate after triple bypass surgery’ will locate documents about this topic even if the precise phrase is not contained in any of the documents. A traditional search query return would return all documents that contained the words ‘triple’, all the words that contain ‘heart’, and all the words that contain ‘bypass’.)Features: Downloadable in 30 minutes – no programming required  Automatic classification and compound term meta data extraction Classification technology uses concept extraction and compound term processing Taxonomy based and faceted navigation Robust suite of tools to build an maintain taxonomiesFully integrated with Content TypesAutomatic classification from MS Office and OutlookTaxonomy browse, faceted navigation, and preview functionality from the search interfaceCan automatically classify from SharePoint, folders, and web sites providing a single interface to all permmissable content Simple intuitive interface designed for the SME  Fully SOA compliant, delivered as Web Parts, based on open standards  Integrates with Microsoft Office, Microsoft Records Center, and the Microsoft Business Data Catalog 
  • A taxonomy is a classification structure that is represented by a hierarchical view of topics that have been grouped together because they share the same quality of characteristic. A taxonomy provides a unified view and access to relevant information across often disperse silos of information. Concept Searching supports multiple taxonomies within an organization. Taxonomy development is traditionally a very time consuming and costly activity. Our Taxonomy Manager has been proven to reduce taxonomy development time by 80%, generating a time savings of 6-12 months and a cost savings of $150K - $300K. Concept Searching also has a robust and frequently expanding library of off-the-shelf taxonomies covering a wide variety of domains to help jumpstart a classification project by providing off the shelf taxonomies to cover nearly any industry.The taxonomy (or multiple taxonomies) can be used by Subject Matter Experts (SME’s) to easily build taxonomies and classify document into predefined categories based on a small number of descriptors or clues. Once classified the documents can then be applied to a corporate taxonomy and made available to the organization. The taxonomy management features includes:- Ability to change the node weighting (score)- Auto clue suggestion: automatic generation of node clues from compound terms found in the document corpus eliminating training sets and complex Boolean rules- Dynamic screen updating: the user interface is fully AJAX enabled so changes to the taxonomy are immediately available for further refinementDocument movement feedback: this feature enables the SME to see the cause and effect on the taxonomy without re-indexing.The metadata generation issue is increasingly a growing concern in large enterprises. A comprehensive approach requires more than syntactic metadata (i.e. date, author, title) and requiring end users to add rich metadata is haphazard and subjective at best. Since Concept Searching’s technology is no longer restricted to keyword identification, compound term metadata can be automatically generated either when the content is created or ingested. The generation of metadata based on concepts extracts compound terms and keywords from a document or corpus of documents that are highly correlated to a particular concept. By identifying the most significant patterns in any text, these compound terms can then be used to generate non-subjective metadata based on an understanding of conceptual meaning. Compound term processing is a new approach to an old problem. Instead of identifying single keywords, compound term processing identifies multi-word terms that form a complex entity and identifies them as a concept. By deriving these compound terms from the clients own document corpus we can tag content with meaningful semantic metadata and enable Microsoft’s Enterprise search to filter across that metadata at retrieval thus deliver a higher degree of accuracy because the ambiguity inherent in searching against single words in isolation is no longer a problem. As a result, a search for “survival rates following a triple heart bypass” will locate documents about this topic even if this precise phrase is not contained in any document.  Compound term processing can address many challenges facing large enterprises and provide many benefits. Identification of concepts within a large corpus of information removes the ambiguity in search, eliminates inconsistent meta-tagging, and automatic classification and taxonomy management based on concept identification simplifies development and on-going maintenance. The unique compound term processing enables the identification of compound terms (not keywords) from highly relevant content that can be used to trigger the automatic meta-tagging and the auto-classification processes. This conceptual metadata is added to the original metadata for the category/folder. More semantic metadata that can be linked to a document or record results in information that becomes more useful to the organization. Meta-tags are automatically added to the properties field of each document making the document more valuable to the organization by increasing the ability of the document to be retrieved using Microsoft Search Products that use keywords and metadata to retrieve information.
  • Following the automatic generation (tagging) of compound terms and semantic metadata the documents in the document libraries are then automatically classified to multiple categories within the taxonomy. The terms generated can be edited from within SharePoint or from within the Taxonomy Manager tool. The content will remain and can be accessed from the original location but can be linked to multiple categories/nodes.
  • Enterprises are increasingly understanding the value and critical need to utilize Content Types to structure their content and identify the type of document regardless of its physical site or library storage location. Content Types can be used to enforce metadata governance, adhere to policies and drive workflows in line with business processes. Included in the new release is the ability to assign taxonomies to specific Content Types. Documents that correspond to the selected Content Types will be classified and documents that do not correspond to a content type or do not include some metadata elements that a specific content type has specified will not be classified. This essential functionality allows different taxonomies to be assigned to different Content Types for example, assign the HR taxonomy to all Content Types of type “HR”, including any Content Types derived from “HR” and assign the Finance taxonomy to all Content Types of type “Finance”, including any Content Types derived from “Finance”.  The configuration can be performed using a wizard that runs inside SharePoint. The taxonomies will be available for these documents regardless of their location. conceptClassifier’s site columns and Event Handlers are associated to the Content Types. This delivers the ability to automatically add classification functionality to new sites when created.
  • conceptClassifier for SharePoint fully supports Content Types. An add-on features includes the ability to update Content Types based on the identification of content during the classification process. This is particularly useful in records management and data privacy and security. This provides the ability to develop a series of actions that can occur when content contains specific metadata as defined by the organization.  
  • conceptClassifier for SharePoint integration with Microsoft Office and Microsoft Exchange the automatic metadata generation and classification without end user participation. Alternatively, the Subject Matter Expert (SME) or Knowledge Worker can be granted the authority to modify the results from within the traditional Microsoft Office interface. The knowledge worker is the most qualified person to anticipate how the asset will be searched for and how to make it easy to find. The automatic classification returns not only single words but identifies concepts within the document to assist the knowledge worker in the classification process. This guided approach enables the knowledge worker to precisely and accurately classify the document for reuse and retrieval. Placing the ability to classify documents into the hands of knowledge workers results in rich and comprehensive metadata, significantly improving the organization’s ability to leverage their information capital. · Gives business experts the ability to classify critical business · information with highly relevant metadata· Greatly improves the search and retrieval process by ensuring accurate and complete metadata· Expedites organizational access to real-time information· Provides a consistent content management approach· Delivers metadata rich information retrieval thereby maximizing productivity and organizational agility  
  • Knowledge workers need to identify content in the context of what they are seeking. The fundamental problem with most enterprise search solutions, and all statistical search solutions, is that they are based on an index of single words. Yet most queries are expressed in short patterns of words and not single words in isolation which are highly ambiguous.  A concept search engine can isolate the key meaning that is normally expressed as proper nouns, nouns phrases and verb phrases. Although linguistic products can do this, their performance is highly variable depending upon the vocabulary and language in use. A statistical based language independent concept search can accept queries in natural language with the user typing words, phrases or whole sentences. The system then analyzes the natural language query to extract the keywords and phrases to identify the main concepts and retrieve content that is highly relevant. Precision and recall are the two key performance measures for information retrieval. Precision is the retrieval of only those items that are relevant to the query. Recall is the retrieval of all items that are relevant to the query. Yet most information retrieval technologies are less than 22% accurate for both precision and recall. The ideal goal is to have them balanced. Compound Term Processing has the ability to increase precision with no loss of recall.  Documents that have been auto-classified are now accessible by searching for all the content within a folder and by using Microsoft Enterprise Search which can now filter on highly relevant metadata that has been created with Taxonomy Manager. Search results are clustered into categories or facets enabling an end user to rapidly drill into a result set based on organizational, functional, product line, and geographic metadata that have been generated using Taxonomy Manager and automatically tagged to relevant documents and records within document libraries. Based on the end user search refinement new facets will be generated when the query changes.
  • Knowledge workers need to identify content in the context of what they are seeking. The fundamental problem with most enterprise search solutions, and all statistical search solutions, is that they are based on an index of single words. Yet most queries are expressed in short patterns of words and not single words in isolation which are highly ambiguous.  A concept search engine can isolate the key meaning that is normally expressed as proper nouns, nouns phrases and verb phrases. Although linguistic products can do this, their performance is highly variable depending upon the vocabulary and language in use. A statistical based language independent concept search can accept queries in natural language with the user typing words, phrases or whole sentences. The system then analyzes the natural language query to extract the keywords and phrases to identify the main concepts and retrieve content that is highly relevant. Precision and recall are the two key performance measures for information retrieval. Precision is the retrieval of only those items that are relevant to the query. Recall is the retrieval of all items that are relevant to the query. Yet most information retrieval technologies are less than 22% accurate for both precision and recall. The ideal goal is to have them balanced. Compound Term Processing has the ability to increase precision with no loss of recall.  Documents that have been auto-classified are now accessible by searching for all the content within a folder and by using Microsoft Enterprise Search which can now filter on highly relevant metadata that has been created with Taxonomy Manager. Search results are clustered into categories or facets enabling an end user to rapidly drill into a result set based on organizational, functional, product line, and geographic metadata that have been generated using Taxonomy Manager and automatically tagged to relevant documents and records within document libraries. Based on the end user search refinement new facets will be generated when the query changes.
  • Knowledge workers need to identify content in the context of what they are seeking. The fundamental problem with most enterprise search solutions, and all statistical search solutions, is that they are based on an index of single words. Yet most queries are expressed in short patterns of words and not single words in isolation which are highly ambiguous.  A concept search engine can isolate the key meaning that is normally expressed as proper nouns, nouns phrases and verb phrases. Although linguistic products can do this, their performance is highly variable depending upon the vocabulary and language in use. A statistical based language independent concept search can accept queries in natural language with the user typing words, phrases or whole sentences. The system then analyzes the natural language query to extract the keywords and phrases to identify the main concepts and retrieve content that is highly relevant. Precision and recall are the two key performance measures for information retrieval. Precision is the retrieval of only those items that are relevant to the query. Recall is the retrieval of all items that are relevant to the query. Yet most information retrieval technologies are less than 22% accurate for both precision and recall. The ideal goal is to have them balanced. Compound Term Processing has the ability to increase precision with no loss of recall.  Documents that have been auto-classified are now accessible by searching for all the content within a folder and by using Microsoft Enterprise Search which can now filter on highly relevant metadata that has been created with Taxonomy Manager. Search results are clustered into categories or facets enabling an end user to rapidly drill into a result set based on organizational, functional, product line, and geographic metadata that have been generated using Taxonomy Manager and automatically tagged to relevant documents and records within document libraries. Based on the end user search refinement new facets will be generated when the query changes.
  • Knowledge workers need to identify content in the context of what they are seeking. The fundamental problem with most enterprise search solutions, and all statistical search solutions, is that they are based on an index of single words. Yet most queries are expressed in short patterns of words and not single words in isolation which are highly ambiguous.  A concept search engine can isolate the key meaning that is normally expressed as proper nouns, nouns phrases and verb phrases. Although linguistic products can do this, their performance is highly variable depending upon the vocabulary and language in use. A statistical based language independent concept search can accept queries in natural language with the user typing words, phrases or whole sentences. The system then analyzes the natural language query to extract the keywords and phrases to identify the main concepts and retrieve content that is highly relevant. Precision and recall are the two key performance measures for information retrieval. Precision is the retrieval of only those items that are relevant to the query. Recall is the retrieval of all items that are relevant to the query. Yet most information retrieval technologies are less than 22% accurate for both precision and recall. The ideal goal is to have them balanced. Compound Term Processing has the ability to increase precision with no loss of recall.  Documents that have been auto-classified are now accessible by searching for all the content within a folder and by using Microsoft Enterprise Search which can now filter on highly relevant metadata that has been created with Taxonomy Manager. Search results are clustered into categories or facets enabling an end user to rapidly drill into a result set based on organizational, functional, product line, and geographic metadata that have been generated using Taxonomy Manager and automatically tagged to relevant documents and records within document libraries. Based on the end user search refinement new facets will be generated when the query changes.
  • “At last a tool set that enables enterprise content be the driver for business productivity”Concept Searching provides a comprehensive suite of tools for the automatic classification and taxonomy management of enterprise content. The ability to identify ‘concepts in context’ generates far richer meta data, improving the precision and relevancy in the information retrieval process.  Concept Searching provides a comprehensive suite of tools for automatic semantic metadata generation, automated classification and taxonomy management of enterprise content. The metadata generation issue is increasingly a growing concern in large enterprises. A comprehensive approach requires more than syntactic metadata (i.e. date, author, title) and requiring end users to add rich metadata is haphazard and subjective at best. Since Concept Searching’s technology is no longer restricted to keyword identification, compound term metadata can be automatically generated either when the content is created or ingested. The generation of metadata based on concepts extracts compound terms and keywords from a document or corpus of documents that are highly correlated to a particular concept. By identifying the most significant patterns in any text, these compound terms can then be used to generate non-subjective metadata based on an understanding of conceptual meaning.The ability to identify ‘concepts in context’ generates far richer meta data, improving the precision and relevancy in the information retrieval process. Meta-tags are automatically added to the properties field of each document making the document more valuable to the organization by increasing the ability of the document to be retrieved using Microsoft Search Products that use keywords and metadata to retrieve information. conceptClassifier for SharePoint is fully integrated with both SharePoint, Microsoft Office, Exchange, FAST and Microsoft Enterprise Search. The automatic extraction of compound terms enables the Subject Matter Expert (SME) to use the terms within the taxonomy generation process, reducing the time to build out and maintain taxonomies by 80%. (Compound Term Processing performs matching on the basis of compound terms as opposed to keywords. Compound terms are built by combining two (or more) simple terms, for example ‘triple’ is a single word term but ‘triple heart bypass’ is a compound term. By identifying and forming compound (multi-word) terms and placing these in the search engine’s index the search can be performed with a greater degree of accuracy because the ambiguity inherent in single words is no longer a problem. A search for ‘survival rate after triple bypass surgery’ will locate documents about this topic even if the precise phrase is not contained in any of the documents. A traditional search query return would return all documents that contained the words ‘triple’, all the words that contain ‘heart’, and all the words that contain ‘bypass’.)Features: Downloadable in 30 minutes – no programming required  Automatic classification and compound term meta data extraction Classification technology uses concept extraction and compound term processing Taxonomy based and faceted navigation Robust suite of tools to build an maintain taxonomiesFully integrated with Content TypesAutomatic classification from MS Office and OutlookTaxonomy browse, faceted navigation, and preview functionality from the search interfaceCan automatically classify from SharePoint, folders, and web sites providing a single interface to all permmissable content Simple intuitive interface designed for the SME  Fully SOA compliant, delivered as Web Parts, based on open standards  Integrates with Microsoft Office, Microsoft Records Center, and the Microsoft Business Data Catalog 

Concept Searching Webinar Concept Searching Webinar Presentation Transcript

  • conceptClassifier for SharePoint
  • Speakers
    NS Rana – Business Productivity Advisor, Microsoft
    NS is a 19 year IT Industry veteran. For the last 12 years NS has worked at Microsoft in various roles helping organizations both large and small achieve their full potential by effectively adopting and deploying Microsoft Technologies. In his current role as a Business Productivity Advisor, he defines, manages and delivers solution scenarios that include Collaboration, Enterprise Content Management, Enterprise Search, Business Intelligence powered by technologies like Microsoft Office and SharePoint.
     
    Donald T. Miller – Vice President Business Development, Concept Searching
    With over 20 years of experience, Don Miller is an industry veteran of search and information management solutions and is the Vice President of Business Development for Concept Searching.
    Val Orekhov – Chief Architect, Portal Solutions
    Val is the Chief Architect at Portal Solutions. Portal Solutions is a leading systems of Microsoft solutions and recently published a white paper on the next step in knowledge management which is knowledge optimization. Knowledge Optimization is positioned as the next logical evolution of Knowledge Management, and continues the company’s long tradition of thought leadership in the enterprise software industry.
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • Agenda
    • Microsoft
    • Search options & futures
    • Concept Searching
    • conceptClassifier for SharePoint
    • Portal Solutions
    • Real world application
    • QA
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • Who We Are
    • Company founded in 2002
    • Focus on management of structured and unstructured information
    • Locations: UK, US, & South Africa
    • Client base: Fortune 500/1000 organizations
    • 2009 ‘100 Companies that Matter in KM’ (KM World Magazine)
    • KMWorld Trend Setting Product for 2009
    • Microsoft Enterprise Search ISV , FAST Partner
    • Promoted to Depth Partner, July 09
    • Product launched in 2003
    • taxonomyManager, conceptClassifier, conceptSearching
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • Metadata Drives the Enterprise
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • The Enterprise Information Problem – Metadata is Everywhere
    Metadata Problems
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • Traditional Manual Metadata Approach Bleed 100Ks of Dollars From Your Company
    A manual metadata approach is unacceptable
    INACCURATE x INCONSISTENT = INCOMPLETE & UNACCEPTABLE
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • conceptClassifier and taxonomyManager are changing the metadata market.
    What are Enterprise Metadata Approaches?
    High
    Requires Domain Expertise
    Cumbersome and slow to build
    Requires Boolean Logic/Developer
    Concept Searching
    has changed the game!
    Productivity
    Gains
    Requires Domain Expertise
    Requires large training sets per node
    Cumbersome and slow to build
    Low
    High
    Low
    Cost Savings
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • Make Metadata Work for You And The Enterprise!
    • Create enterprise framework/model
    • Apply consistent metadata vocabulary to enterprise content
    • Automatically tag all content with appropriate metadata
    • Use metadata to improve Records Management
    • Apply work flows with metadata
    • Guide users to relevant content with metadata
    • Reduce IT infrastructure costs with metadata management for life cycle management
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • Technology Differentiator
    • Compound Term Processing
    • Only statistical metadata generation and classification company that uses concept extraction and unique Compound Term Processing technology
    triple heart bypass
    • conceptClassifier will generate semantic metadata using compound terms that identifies ‘triple heart bypass’ as a concept as opposed to single keywords
    • Search will return results based on the concept even if the exact terms are not contained in the document (i.e. ‘coronary artery surgery’, ‘heart surgery’)
    • Can be used by any search engine index or any application/process that uses metadata
    Triple
    Baseball
    Three
    Heart
    Organ
    Center
    Bypass
    Highway
    Avoid
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • Integration directly into MS Search & FAST
    conceptClassifier for SharePoint
    Automatic Classification
    • Automatic Semantic Metadata Generation
    • Unique compound term processing technology
    • Improves Business Processes
    • Search
    • Workflow
    • ECM, Enterprise Metadata Management
    • Records Management, Compliance & Governance
    • Data Privacy & Security
    • Automated Classification
    • From within MS Office, Outlook
    • Taxonomy Tools
    • Proven to reduce taxonomy development by 80%
    • Microsoft Integration
    • Runs natively in SharePoint – not an add-on
    • Fully integrated with Content Types
    • Content Type Updater
    • Technology
    • Downloadable in 30 minutes – no programming required
    • Fully SOA compliant, delivered as Web Parts, based on open standards
    • Highly scalable
    • Microsoft Search Enhancement
    • Fully integrated with Microsoft Enterprise Search, SharePoint search, and FAST ESP
    • Provides taxonomy browse and enhances faceted search
    • Text preview capabilities from search interface
    • Provides a single search interface to end users from within SharePoint to multiple repositories (SharePoint, file stores, web sites)
    Taxonomy Development Management
    We Make Metadata Work For You
    MS Office Integration for Metadata
    Faceted & Taxonomy Navigation Plus Text Preview
    Full Integration with Content Types
    Single Classification Interface to SharePoint, File Stores, & Websites
    MOSS Record Center Workflow Automation
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • Taxonomy & Compound Term Processing
    • Compound Term Processing
    • Semantic metadata automatically generated
    from the organization’s own content and used as clues to build out the taxonomy
    • Hierarchical view of content
    • Content will be automatically classified to one or more nodes based on concepts within the content
    • Reduces time to develop, build, and maintain a taxonomy by as much as 80%
    • Can import industry standard taxonomies
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • Automatic Classification & Metadata Tagging
    • Content is automatically tagged with semantic metadata and uploaded to SharePoint
    • Content is automatically classified to one or more nodes in one or more taxonomies
    • Documents are automatically classified to multiple categories
    • Editable from within SharePoint & the Concept Searching Taxonomy Manager
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • Full Support for Content Types
    • Eliminates time consuming manual metadata definition
    • Enforces governance, policies, and drives workflows in line with business processes
    • Enables different taxonomies to be assigned to different Content Types
    • Authorized users have complete control over automatically generated metadata
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • Automatic Update of Content Types
    Event Handler
    Based on a pre-defined Event Handler, the Content Type can be automatically changed when classified.
    • When organizationally defined metadata is identified within content the Content Type Updater will automatically change the Content Type
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • Office Integration
    • Fully integrated with Microsoft Office & Exchange
    • Content automatically tagged with semantic metadata stored in custom properties
    • Content automatically classified to corporate or departmental taxonomies
    • Delivers governance at the desktop, improves ECM
    • Automatic metadata generation or optionally authorized users can change the classification
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • Navigation – Taxonomy & Faceted
    • Microsoft Enterprise Search/FAST ESP can utilize highly relevant compound term metadata
    • Browsable taxonomy navigation via Concept Searching Web Part
    • Faceted navigation (integrated with Microsoft CodePlex)
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • FAST ESP Search
    • Cross taxonomy navigation filter
    • Taxonomy Browse
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • Aggregates Multiple Content Sources
    • Ability to specify multiple file sources including:
    • SharePoint
    • Web Sites
    • Exchange Public folders
    • File stores
    • Can also include RSS feeds
    • Automatically classify and place semantic metadata in search engine index
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • Text Preview Capability
    • Text preview capability from search interface
    • Reduces network bandwidth costs
    • Faster access to review content before file download
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • conceptClassifier Summary for SharePoint
    • Compound Term Processing and automatic semantic metadata generation
    • Automatically extract semantic value
    • Reduce tedious inaccurate manual labor process of applying metadata and save 5-15 minutes per day per information worker employee
    • Classify SharePoint, File Shares, Web Sites, Email, Documentum, Interwoven Worksite, Lotus and more
    • Taxonomy Tools proven to reduce taxonomy development by 80%
    • Fully integrated into SharePoint – not an add-on
    • Enable communication strategies
    • Advanced Search Enhancements for MOSS, SharePoint 2010, and FAST
    • Test and validate Classification Results
    • Improved Taxonomy Browse Capabilities
    • Improved Navigation and Filtering Facets for MOSS or FAST
    • Fully integrated with Content Types
    • Improves any business process that requires metadata
    We make metadata work for you!
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • Metadata Consistency Drives Business Agility
    Enterprise Content Management & Improved Search
    • Findability first time, every time
    • Deliver a robust content management approach maximizing SharePoint technologies
    Identification of Unknown Data Exposures
    • Reduced litigation, costs associated with data breaches
    Compliance & Records Management
    • Eliminate inconsistent meta-tagging
    • Preserve record integrity
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com
  • Additional Information:
    Concept Searching • Martin Garland• +1 (703) 531-8567 • marting@conceptsearching.com