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Re: a list of companies active in the semantic technology area

From: Paola Di Maio <paola.dimaio@gmail.com>
Date: Tue, 21 Dec 2010 14:33:16 +0100
Message-ID: <AANLkTimJStsBXH225C_riCAqeKkCrmR3NK987xaR2tDX@mail.gmail.com>
To: Dieter Fensel <dieter.fensel@sti2.at>
Cc: "semantic-web@w3.org" <semantic-web@w3.org>

I just remembered

there is some interesting market segmentation and list of companies that can
also serve as reference for this collection
The report was called Project 10X semantic wave 2008


On Tue, Dec 21, 2010 at 2:27 PM, Paola Di Maio <paola.dimaio@gmail.com>wrote:

> re.< categories of semantic technologies
> 1.      NLP, with sub-categories including sentiment analysis
> 2.      Triple stores
> 3.      Latent-semantic technology
> 4.      etc...
> A while back I excerpted a list of 'types of semantic technologies' based
> on a TopQuadrant report
> I am sure it can be improved/argued, but it was the only resource I found
> of this kind
> I paste it below in case it can be useful
> I d be keen to maintain an uptodate set of categories for this domain
> source:Top Quadrant in
> http://lists.w3.org/Archives/Public/public-semweb-ui/2009Jan/att-0006/global_user_models_ethnography.pdf
> (Reproduced with permission)
> ANNEX I Semantic Technologies Capabilities (Source: 25)
> Answer Engine: To provide a direct reply to a search questions as opposed
> to returning a list of relevant documents. It interprets a question asked
> in
> a natural language, checks multiple data sources to collect knowledge
> nuggets required for answering the question and may even create an answer
> on the fly by combining relevant knowledge nuggets. Interpretation of
> questions using domain knowledge. Aggregation and composition of the
> answer.
> Automated Content Tagging: To provide semantic tags that allows a document
> or other work-product to be "better known" by one or more systems
> so that search, integration or invocation of other applications becomes
> more effective. Tags are automatically inserted based on the computer
> analysis
> of the information, typically using natural language analysis techniques. A
> predefined taxonomy or ontology of terms and concepts is used to drive the
> analysis. Machine learning approaches based on statistical algorithms such
> as Bayesian networks.
> Concept-based Search: To provide precise and concept-aware search
> capabilities specific to an area of interest using knowledge
> representations
> across multiple knowledge sources both structured and un-structured.
> Knowledge model provides a way to map translation of queries to knowledge
> resources.
> Connection and Pattern Explorer : Discover relevant information in
> disparate but related sources of knowledge, by filtering on different
> combinations
> of connections or by exploring patterns in the types of connections present
> in the data. Inferences over models to identify patterns using the
> principles
> of semantic distance.
> Content Annotation: Provide a way for people to add annotations  to
> electronic content. By annotations we mean comments, notes, explanations
> and
> semantic tags. Knowledge model is used to assist people in providing
> consistent attribution of  artifacts.
> Context-Aware Retriever: To retrieve knowledge from one or more systems
> that is highly relevant to an immediate context, through an action taken
> within a specific setting -- typically in a user interface. A user no
> longer needs to leave the application they are in to find the right
> information.
> Knowledge model is used to represent context. This “profile” is then used
> to constrain a concept-based search.
> Dynamic User Interface: To dynamically determine and present information on
> the Web page according to user's context. This may include related
> links, available resources, advertisements and announcements. Context is
> determined based on user's search queries, Web page navigation or other
> interactions she has been having with the system. A model of context and a
> memory of activities are used to control UI generation.
> Enhanced Search Query: To enhance, extend and disambiguate user submitted
> key word searches by adding domain and context specific
> information. For example, depending on the context a search query "jaguar"
> could be enhanced to become "jaguar, car, automobile", "jaguar, USS,
> Star Trek", "jaguar, cat, animal" or "jaguar, software, Schrödinger".
> Knowledge models are used to express the vocabulary of a domain.
> Expert Locator: To provide users with convenient access to experts in a
> given area who can help with problems, answer questions, locate and
> interpret specific documents, and collaborate on specific tasks. Knowing
> who is an expert in what can be difficult in an organization with a large
> workforce of experts. Expert Locator could also identify experts across
> organizational barriers. The profiles of experts are expressed in a
> knowledge
> model. This can then be used to match concepts in queries to locate
> experts.
> Generative Documentation: Maintain a single source point for information
> about a system, process, product, etc., but deliver that content in a
> variety
> of forms, each tailored to a specific use. The format of the document, and
> the information it contains, is automatically presented as required by each
> particular audience. Knowledge model is used to represent formatting and
> layout. Semantic matching is a key component of the solution.
> Interest-based Information Delivery: Filter information for people needing
> to monitor and assess large volumes of data for relevance, volatility or
> required response. The volume of targeted information is reduced based on
> its relevance according to a role or interest of the end user. Sensitive
> information is filtered according to the "need to know". A profile of each
> user’s interests is expressed in a knowledge  model. This is then be used
> to
> provide “smart” filtering of information that is either attributed with
> meta-data or has knowledge surrogates.
> Navigational Search; Use topical directories, or taxonomies, to help people
> narrow in on the general neighborhood of the information they seek. A
> Taxonomy that takes into account user profiles, user goals and typical
> tasks performed is used to drive a search engine. To optimize information
> access by different stakeholders, multiple interrelated taxonomies are
> needed.
> Product Design Assistant: To support the innovative product development and
> design process, by bringing engineering knowledge from
> manydisparate sources to bear at the appropriate point in the process.
> Possible enhancements to the design process that result include rapid
> evaluation, increased adherence to best practices and more systematic
> treatment of design constraints.
> Semantic Data Integrator: Systems developed in different work practice
> settings have different semantic structures for their data. Time-critical
> access
> to data is made difficult by these differences. Semantic Data Integration
> allows data to be shared and understood across a variety of settings. A
> common knowledge model is used to provide one or more unified views of
> enterprise data. Typically this is done by using mapping. Rules are
> executed to resolve conflicts, provide transformations and build new
> objects from data elements.
> Semantic Form Generator and Results Classifier: To improve the data
> collection process and data input analysis by providing knowledge driven
> dynamic forms. A knowledge model is used to intelligently guide the user
> through data capture. The results are automatically classified and analyzed
> according to the model
> Semantic Service Discovery and Choreography: Service Oriented Architectures
> enable increased reuse of existing services and the dynamic
> automation of processes through service composition and choreography.
> Knowledge models are used to enhance the functionality of service
> directories. Invocation methods, terminology and semantic description allow
> the dynamic discovery of services by machines.
> Virtual Consultant: Offer a way for customers to define their individual
> goals and objectives, and then show them what products and services can
> help them meet those goals. Understanding customer’s goals and requirements
> through a questionnaire or dialog establishes a profile that helps you
> communicate effectively with them now and in the future.
> On Mon, Dec 20, 2010 at 11:13 PM, Obrst, Leo J. <lobrst@mitre.org> wrote:
>> Also, there are many companies who are not vendors who are very active in
>> semantic technologies. Are you only interested in vendors?
>> Thanks,
>> Leo
>> _____________________________________________
>> Dr. Leo Obrst        The MITRE Corporation, Information Semantics
>> lobrst@mitre.org     Information Discovery & Understanding, Command &
>> Control Center
>> Voice: 703-983-6770  7515 Colshire Drive, M/S H305
>> Fax: 703-983-1379    McLean, VA 22102-7508, USA
>> *From:* semantic-web-request@w3.org [mailto:semantic-web-request@w3.org]
>> *On Behalf Of *Michael F Uschold
>> *Sent:* Monday, December 20, 2010 5:07 PM
>> *To:* Dieter Fensel
>> *Cc:* semantic-web@w3.org; Dave McComb; Simon Robe
>> *Subject:* Re: a list of companies active in the semantic technology area
>> Dieter,
>> This is a fabulous idea.  There is at least on major shortcoming: you
>> forgot to include the company I work for (Semantic Arts<http://semanticarts.com>:-)).  I'll make sure you get an entry from us.
>> Seriously, I'm delighted to see this happen.  I do wonder how you may
>> attempt to draw the line that defines "semantic technology".  There will
>> always be grayness. If it is too broad, the usefulness of this list could
>> decrease. That could be ameliorated by a rich set of semantic technology
>> categories, so a person could focus on a particular technology are, or
>> ignore other ones. I.e one facet could be 'type of technology" which could
>> include
>> 1.      NLP, with sub-categories including sentiment analysis
>> 2.      Triple stores
>> 3.      Latent-semantic technology
>> 4.      etc...
>> I look forward to seeing this in the linked data cloud!
>> Michael
>> On Mon, Dec 20, 2010 at 11:50 AM, Dieter Fensel <dieter.fensel@sti2.at>
>> wrote:
>> Dear all,
>> we started to collect a list of companies active in the semantic
>> technology area.
>> A first draft is at http://semantic-technology-companies.sti2.at/
>> Obviously this list is biased and severely incomplete. Also one may want
>> to add more details on the specifics of the mentioned companies. Here is
>> were I ask your help. Could you please post me (or to the list if you
>> think it is of immediate general interest) any information on missing
>> companies
>> or important details on the ones listed. Also if you know similar
>> initiatives
>> please drop me a note. So please apologize for the lack of completeness
>> but it
>> is an invitation to jointly improve this list.
>> In the long term we want to establish a repository of semantic technology
>> vendors. Obviously we plan more advanced interaction and semantics
>> for future versions of this web site. For the moment, it is only the data
>> that matter. If you want to cooperate on the repository aspect
>> you are more than welcome to contact me, too. Yes, we may want
>> to "eat our own dog food" no matter how many (definitely being different)
>> dogs we have.
>> Thank you for your help,
>> Dieter
>> ---------------------------------
>> Dieter Fensel
>> Director STI Innsbruck
>> University Innsbruck
>> http://www.sti-innsbruck.at/
>> phone: +43-512-507-6488/5
>> fax: +43-512-507-9872
>> --
>> Michael Uschold, PhD
>>    Senior Ontology Consultant, Semantic Arts
>>    LinkedIn: http://tr.im/limfu
>>    Skype: UscholdM
Received on Tuesday, 21 December 2010 13:33:50 UTC

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