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SenseBot (www.sensebot.net) represents a new type of Search Engine that delivers a summary in response to your search query instead of a collection of links to Web pages. SenseBot parses top results returned by a major Web search engine (e.g., Google) and prepares a text summary of them. The summary serves as a digest on the topic of your query, blending together the most significant and relevant aspects of the search results. The summary itself becomes the main result of your search.

Semantic Cloud is the API that powers semantic search engine SenseBot and contextual linking tool for bloggers LinkSensor. The API supports SOAP and REST protocols (HTTP GET). The idea is to empower semantic startups or any ventures that are looking to utilize an affordable high-quality semantic solution to build their applications. Semantic API features include: * extraction of semantic concepts from a page or document; * creating a "semantic cloud" of concepts describing a group of documents; * generating a multi-document summary of a set of pages; * generating an essay on a topic based on a set of documents. Multiple parameters allow the client to control the type and format of results.

Mutual respect, trust, and recognition that cultural differences exist, matter, and must be explicitly dealt with are requirements of successful international projects. In summary, I would suggest these principles for the success of international collaboration: * Two (or more) teams share the same goal and seek the overall optimal result, not the local optimum. * Each team should clearly recognize and value the other party's different culture and traditions. * The single most important word in international projects is trust. Team members earn trust by being sincere, honest, and open-minded.

TagCrowd is a web application for visualizing word frequencies in any user-supplied text by creating what is popularly known as a tag cloud or text cloud. TagCrowd is taking tag clouds far beyond their original function: * as topic summaries for speeches and written works * as blog tool or website analysis for search engine optimization (SEO) * for visual analysis of survey data * as brand clouds that let companies see how they are perceived by the world * for data mining a text corpus * for helping writers and students reflect on their work * as name tags for conferences, cocktail parties or wherever new collaborations start * as resumes in a single glance * as visual poetry

This summary was born out of a question on Twitter and percolated to FriendFeed, which was "Who is using RDF and integrating other resources at the minute and what are those resources? From this question, several resources were highlighted

In the process of knowledge discovery, workers examine available information in order to make sense of it. By sensemaking, we mean interacting with and operating on the information with a variety of information processing mechanisms [3,18]. Previously, we introduced a concept that uses the spreadsheet metaphor with cells containing visualizations of complex data. In this paper, we extend and apply a cognitive model called "visual sensemaking " to the Visualization Spreadsheet. We use the task of making sense of a large Web site as a concrete example through out the paper for demonstration. Using a variety of visualization techniques, such as the Disk Tree and Cone Tree, we show that the interactions of the Visualization Spreadsheet help users draw conclusions from the overall relationships of the entire information set.

S. I. Hayakawa's Language in Thought and Action has been one of the course's handbooks for a memorable number of years already, but the manner in which it has been used has changed somewhat over the years. Although the basic concern of the book is with «informal» semantics (not the formal brand of semantics concerned with, e.g. the computation of truth-value), i.e. the «symbolic» way in which utterances are used to convey meaning, it also raises the more cognitive issue of how language affects human thought and conditions behaviour, and addresses the resulting «ethical» question of how language should be used to achieve cooperation and understanding rather than confrontation and conflict. These questions (though viewed in a somewhat «optimistic» perspective) give the book additional value as one of the pioneering works in «critical linguistics», a discipline which was to develop only much later.

This paper presents two design patterns for the hypermedia domain: `Navigational Contexts' and `Information on Demand'. They are applied in two different aspects of hypermedia applications design: the design of healthy navigational structures and the design of understandable and usable hypermedia interfaces, respectively. These two patterns are part of an effort for developing a Pattern Language for that domain.

The confluence of computers, communications, and databases is quickly creating a distributed database where many applications require real-time access to both temporally accurate and multimedia data. This is particularly true in military and intelligence applications, but these required features are needed in many commercial applications as well. We are developing a distributed database, called BeeHive, which could offer features along different types of requirements: real-time, fault-tolerance, security, and quality-of service for audio and video. Support of these features and potential trade-offs between them could provide a significant improvement in performance and functionality over current distributed database and object management systems. In this paper, we present a high level design for BeeHive architecture and sketch the design of the BeeHive Object Model (BOM) which extends object-oriented data models by incorporating time and other features into objects, resulting in a highly reflective architecture.

The combination of classification, search enhancement, and contextual navigation delivers Findability.

The goal of this project is to develop a Python tool to make an in-depth quantitative analysis about Wikipedia, generating graphics and statistical results for each language version of Wikipedia.