SemanticWeb Research

Now we are able to define a Service Oriented Architecture (SOA). SOA is an architectural style whose goal is to achieve loose coupling among interacting software agents. A service is a unit of work done by a service provider to achieve desired end results for a service consumer. Both provider and consumer are roles played by software agents on behalf of their owners.

Shared resource for the community of builders working with the semantic web.

Tom Gruber at Stanford University defines and conceptualizes ontologies as they are applied to the semantic web.

OWL Resource: definitions, specifications, etc.

Case studies include descriptions of systems that have been deployed within an organization, and are now being used within a production environment. Use cases include examples where an organization has built a prototype system, but it is not currently being used by business functions.

The European Academy for Semantic-Web Education, completely in English, involves studying one year at one of the above universities, and completing the second year with a stay in another of the partner universities. After this, the student may obtain, together with the European degree, a national Master of Science degrees for each of the visited universities (e.g., in Italy this would be the Laurea Specialistica degree).

2006

(Developed in 2003) Papers and tutorials are worth looking at

from CiteSeer x beta

This document concerns the management of the output of insight generators, the software agents utilized in the insight generation systems. The solution to managing these reports involves the automatic creation of a repository for all materials generated by various insight generators; this repository allows the user to navigate through this continually growing space of marketing reports, gaining new insights about the relationships between items of interest and adding new insights in the process. The goal of the system is to make all marketing information and insights generated by the man/machine interaction available to the user, so that there is a convergence towards a "conservation of information". To use a geometric metaphor, the goal is to make the user equidistant from all information at all times, as illustrated below.

The blogosphere is the most explosive social network you'll never see. Recent studies suggest that nearly 60 million blogs exist online, and about 175,000 more crop up daily (that's about 2 every second). Even though the vast majority of blogs are either abandoned or isolated, many bloggers like to link to other Web sites. These links allow analysts to track trends in blogs and identify the most popular topics of data exchange. Social media expert Matthew Hurst recently collected link data for six weeks and produced this plot of the most active and interconnected parts of the blogosphere.

Free mind mapping via Web application

Gabriela Groseck

The Semantic Network Service (SNS) of the Federal Environment Agency provides support for all questions concerning environmental terms including the common place names. SNS contains a bi-lingual (German/English) semantic network which consists of three components: * the Environmental Thesaurus UMTHES® with more than 50,000 inter-networked terms. (Descriptors and Non-Descriptors). * the Geo-Thesaurus-Environment (GTU) with more than 25,000 geographic names and the spatial intersections of all these places. * an Environmental Chronology containing more than 600 contemporary and historical events that affected the environment.

Tamara Munzner Ph.D. dissertation, Stanford University, June 2000. Abstract *Visualization/graphing strategy for large semantic networks is described. Very technical.* Many real-world domains can be represented as large node-link graphs: backbone Internet routers connect with 70,000 other hosts, mid-sized Web servers handle between 20,000 and 200,000 hyperlinked documents, and dictionaries contain millions of words defined in terms of each other. Computational manipulation of such large graphs is common, but previous tools for graph visualization have been limited to datasets of a few thousand nodes. Visual depictions of graphs and networks are external representations that exploit human visual processing to reduce the cognitive load of many tasks that require understanding of global or local structure. We assert that the two key advantages of computer-based systems for information visualization over traditional paper-based visual exposition are interactivity and scalability. We also argue that designing visualization software by taking the characteristics of a target user's task domain into account leads to systems that are more effective and scale to larger datasets than previous work.

Vic's compendium of software that supports knowledge management and information organisation in graphical form. Includes mind mappers, concept mappers, outliners, hierarchical organisers, KM support and knowledge browsers, 2D and 3D. The opinions are Vic's but material in quotes that follows "What they say" is quoted from the vendors' web sites. Product names used in this web site are for identification purposes only and trademarks are the property of their respective owners. Please let me know of any software that isn't here but should be: Vic at the above domain.

Topics of Interest - Be sure to click the link in "Proceedings of the workshop is available here..." Submissions are invited on work relating the Semantic Web with Social Network Analysis. Both theoretical as well as applciation papers are welcome. The topics of interest include (but are not limited to) the following: * Social Network Analysis of the Semantic Web * Network Analysis Methods * Analysis of Large Online Communities (Wikipedia, DMOZ, EBay, ...) * Semantic Web Communities * Web Communities of Practice * Online Harvesting of Semantic Network Information * Network Analysis for Building the Semantic Web * Emergent Semantics in Communities * Change Detection * Self-organization and Management of Semantic Networks * Trust Issues in Semantic Networks * Semantic Network Metadata * Folksonomies * Communities in P2P systems * Online Social Networking (FOAF, Orkut, ...) * Applications of Online Semantic Networks * Knowledge Management with Semantic Networks

The scope of the UMLS Semantic Network is broad, allowing for the semantic categorization of a wide range of terminology in multiple domains. Major groupings of semantic types include organisms, anatomical structures, biologic function, chemicals, events, physical objects, and concepts or ideas. The links between the semantic types provide the structure for the network and represent important relationships in the biomedical domain. The primary link between the semantic types is the 'isa' link. The 'isa' link establishes the hierarchy of types within the Network and is used for deciding on the most specific semantic type available for assignment to a Metathesaurus concept. There is also a set of non-hierarchical relationships, which are grouped into five major categories: `physically related to,' `spatially related to,' `temporally related to,' `functionally related to,' and `conceptually related to.'

A semantic network is a directed graph consisting of nodes (also termed points or vertices) which represent concepts and edges (also termed lines or arcs) which represent semantic relations between the concepts. A kind of knowledge representation used, for example, in hypertext systems.

John F. Sowa - This is a revised and extended version of an article that was originally written for the Encyclopedia of Artificial Intelligence, edited by Stuart C. Shapiro, Wiley, 1987, second edition, 1992. A semantic network or net is a graphic notation for representing knowledge in patterns of interconnected nodes and arcs. Computer implementations of semantic networks were first developed for artificial intelligence and machine translation, but earlier versions have long been used in philosophy, psychology, and linguistics. What is common to all semantic networks is a declarative graphic representation that can be used either to represent knowledge or to support automated systems for reasoning about knowledge. Some versions are highly informal, but other versions are formally defined systems of logic. Following are six of the most common kinds of semantic networks, each of which is discussed in detail in one section of this article.