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

From: Danielle Grossi <grossidn@googlemail.com>
Date: Tue, 21 Dec 2010 15:36:22 +0100
Message-ID: <AANLkTindvsskmnjgMGSbx4kpxew-68Scywdruw96j94Q@mail.gmail.com>
To: paoladimaio10@googlemail.com, Dieter Fensel <dieter.fensel@sti2.at>, "semantic-web@w3.org" <semantic-web@w3.org>
Dieter, this is a great idea. I'm currently working on a taxonomy that
could be beneficial to your initiative. I'll ping you directly to see
if what I'm doing can help.


On 12/21/10, Paola Di Maio <paola.dimaio@gmail.com> wrote:
> Dieter
> 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
> http://www.scribd.com/doc/2599547/Project10Xs-Semantic-Wave-2008-Report-Industry-Roadmap-to-Web-30-
> 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

Sent from my mobile device
Received on Tuesday, 21 December 2010 14:36:56 UTC

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