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Zemanta is a service that's focused on helping the blogger/content creator make the process of creating their content simpler and easier. As you write, Zemanta processes all of your text (like a spell checker in a word processing program does) and suggests things to you. Currently, Zemanta suggests stories/posts/research you might want to read as you compose your post, images you might want to include in the post, words you might want to hyperlink out with, and tags for search engines and other services to use to discover your content.

But I've never seen a service that brings social bookmarking and semantic search together the way AntStorm does. The service works on two major fronts: first, AntStorm allows you to upload your bookmarks, tag them, share them, and access them from any computer you choose, and second, AntStorm uses your tagged bookmarks to power a semantic search engine that will help you find new sites and services that match your interests.

Over the past two decades, more people have been lifted out of poverty in Asia than in any other region at any other time in history. In the next decade, there is the chance to fulfil the potential of this success story - the world is presented with an historic opportunity to end poverty in Asia .

The Future of the Internet - a federating theme for activities on future networks, software and service architectures, networked media systems and the Internet of Things. Over the last 20 years society, economy and technology evolved in many directions and into new areas. Many of these evolutions have created opportunities which must be taken into account when crafting future Networks.

Pierre Lévy

The purpose of this chapter is to delineate the key aspects comprising the epistemology that informs my research. It is written as a conversation between myself and three scholars, Ken Wilber, Max van Manen, and Humberto Maturana, each of whom has greatly influenced my thinking regarding the nature of knowing, knowledge, and reality in relation to human science research.

Using the 1994, South Canyon fire in Colorado as a case study, this article suggests Taylor's (1993) concept of worldview functions as an important "frame" for organizational sensemaking (Weick, 1995a). Taylor argues that organizations use either an "activity" or "particle" orientation. An activity view focuses attention on organizational units while a particle view sees the organization from the point of view of the product or customer. Results from this study indicate that an organization's worldview functions as an overarching metaphor that influences sensemaking and decision-making processes, and that the worldview perspective in retrospective accounts may differ from that of the participants themselves.

The Mike Wallace Interview Aldous Huxley 5/18/58 Aldous Huxley, social critic and author of Brave New World, talks to Wallace about threats to freedom in the United States, overpopulation, bureaucracy, propaganda, drugs, advertising, and television.

Control information overload. Emails, links, attachments, blog posts, news-all relevant data is organized and prioritized by contact.

Once students have gathered information on a topic, it is important that they reflect on whether the information they have gathered is sufficient to answer the research question. This requires students to make sense of the information they have gathered-to synthesize the information into new knowledge. In order to address the different leaning modalities that students possess, teachers should have students use a variety of forms of representation for this sensemaking stage (see Forms of Representation matrix).

Sensemaking is a process designed to enable groups of people to see patterns that were once hidden to them and develop a common understanding of what is required to address an issue. While the sensemaking (and subsequent intervention design) process will result in the production of artefacts (reports, lists of action items, descriptions of the current situation etc) much of the value is derived through participation in the process. It is not a process where you say 'make sense of this and tell me the answer'. Much of the benefit comes from determining 'what it means' for yourself. Sensemaking is beneficial at an individual level as our values and assumptions are tested and either confirmed or found wanting.

Instead of constructing theories like their researcher colleagues, researching clinicians must face their previous constructions (i.e., sensemaking from experience), create methods which allow for deconstruction (i.e., sensemaking challenged), and then work towards building reconstructions (i.e., sensemaking remade) (Dervin, 1992; Duffy, 1995; Shields & Dervin, 1993; Weick, 1995)

This present report summarizes the insights and recommendations of a symposium on sensemaking, sponsored by the Command and Control Research Program (CCRP) of the Assistant Secretary of Defense for Command, Control, Communications, and Intelligence, and held in Vienna, Virginia, on 23-25 October 2001.

understanding this content and making decisions based on it (especially in mission-critical situations) is not just a simple matter of consuming information. To effectively "make sense" of large, heterogeneous, and often unstructured content collections requires: - efficient, accurate, and context-based ways of extracting, filtering, and summarizing information; - better and more meaningful ways of organizing, visualizing, and interacting with the information; - faster, more objective methods for investigating hypotheses, detecting trends or patterns across multiple sources, and otherwise analyzing or interpreting information.

Sensemaking systems don't only help people find stuff faster. That's just the information retrieval part. The bigger story is about augmenting and amplifying our abilities to make sense. Sensemaking adds things like skimming, power reading, organizing, spotting patterns, tracing social networks, taking notes, summarizing, drilling for details, and flagging biases. Reading an article is different from reading a book, and that's different from reading from a collection or stream. Radically new forms of human-information interaction are being enabled by these new technologies. Sensemaking systems not only have front ends (visualization), but also back ends (content analytics and reasoning).

Using segments of rich media makes it possible to aggregate context and meaning on these chunks by using a number of different mechanisms. Starting with a granular node -- be it a sound bite, visual clip or written fact -- it is possible to aggregate contextual metadata through a series of steps that emergently progress from: * Starting with thousands of defined Audio Sound Bites & visual clips * Rating sound bites and clustering them with folksonomy tags * Sequencing audio sound bites within playlists * Collaboratively building larger sequences with nested playlists * Independently controlling the video & audio tracks with 2-dimensional nested playlists * Evaluating Multiple Storylines and Hypotheses with a 2-dimensional playlist matrix * Visualizing complex networks by mapping out feedback loop relationships between nodes

See also: http://lists.w3.org/Archives/Public/semantic-web/2008Apr/0005.html NaturalOWL is an open-source natural language generation engine written in Java. It produces descriptions of individuals (e.g., items for sale, museum exhibits) and classes (e.g., types of exhibits) in English and Greek from OWL DL ontologies. The ontologies must have been annotated in RDF with linguistic and user modeling resources. We demonstrate a plug-in for Protege that can be used to produce these resources and to generate texts by invoking NaturalOWL. We also demonstrate how NaturalOWL can be used by robotic avatars in Second Life to describe the exhibits of virtual museums. NaturalOWL demonstrates the benefits of Natural Language Generation (NLG) on the Semantic Web. Organizations that need to publish information about objects, such as exhibits or products, can publish OWL ontologies instead of texts. NLG engines, embedded in browsers or Web servers, can then render the ontologies in multiple natural languages, whereas computer programs may access the ontologies directly.

The W3C OWL 2 Web Ontology Language (OWL) is a Semantic Web language designed to represent ontologies - information about how individuals are grouped and fit together in a particular domain. OWL can represent rich and complex information about classes of individuals and their properties. OWL is a logical language, where every construct has a well-defined meaning, meanings that fit together to support exact and useful representation of many different kinds of information. OWL groups information into ontologies in the form of documents that can be stored and transmitted across the World Wide Web in the same way that data and other kinds of information are and that can be completely and effectively processed by tools that extract the information implicit in an ontology.