- From: Danielle Grossi <grossidn@googlemail.com>
- Date: Tue, 21 Dec 2010 15:36:22 +0100
- 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. Danielle 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