Amaya Binary Releases

Amaya is intended to be a comprehensive client environment for testing and evaluating new proposals for Web standards and formats. A large part of the intended features of Amaya are implemented in this release, but some of them are not complete yet.

Check out the list of new features.

The Amaya binary distribution is available for PC Linux, Windows (NT, XP, 2000) and Mac OS X. Users having other architectures are expected to compile the Amaya source code.

Release schedule

There are about three Amaya releases a year. Between each major release, we may generate patch releases or snapshots which fix important bugs.

Each time a new release is available, an email is sent to the www-amaya mailing list.

Getting the binary distribution

The binary distribution is available for a set of Windows, Linux and Mac OS X platforms.

desdeAmaya Binary Releases.

Amaya Home Page

W3C’s Editor/Browser

Amaya is a Web editor, i.e. a tool used to create and update documents directly on the Web. Browsing features are seamlessly integrated with the editing and remote access features in a uniform environment. This follows the original vision of the Web as a space for collaboration and not just a one-way publishing medium.

Work on Amaya started at W3C in 1996 to showcase Web technologies in a fully-featured Web client. The main motivation for developing Amaya was to provide a framework that can integrate as many W3C technologies as possible. It is used to demonstrate these technologies in action while taking advantage of their combination in a single, consistent environment.

Amaya started as an HTML + CSS style sheets editor. Since that time it was extended to support XML and an increasing number of XML applications such as the XHTML family, MathML, and SVG. It allows all those vocabularies to be edited simultaneously in compound documents.

Amaya includes a collaborative annotation application based on Resource Description Framework (RDF), XLink, and XPointer. Visit the Annotea project home page.

Amaya – Open Source

Amaya is an open source software project hosted by W3C. You are invited to contribute in many forms (documentation, translation, writing code, fixing bugs, porting to other platforms…).

The Amaya software is written in C and is available for Windows, Unix platforms and MacOS X.

A public irc channel #amaya is available on irc.w3.org (port 6665).

Amaya Team

The application is jointly developed by W3C and the WAM (Web, Adaptation and Multimedia) project at INRIA. The core team includes: Irène Vatton (Project lead, INRIA), Laurent Carcone (W3C), Vincent Quint (INRIA).

Translations

Submitted translations of Amaya pages :

Polish | German | Spanish | Belarussian

desdeAmaya Home Page.

Semantic Web Use Cases and Case Studies: Twine

Image via CrunchBase

Jim Wissner Twine and Nova Spivack, Twine, USA

April 2009

Introduction

Twine helps people track, discover, and share content around topics they are interested in.

Twine is built on a semantic applications platform that combines W3C standards for RDF and OWL with natural-language processing, statistical analysis and graph analysis capabilities.

Twine is developed by Radar Networks, headquartered in San Francisco. Before developing Twine, Radar Networks had worked on the CALO Cognitive agent That Learns and Organizes project, a distributed research program focused on next-generation semantically-aware machine learning applications. The Twine product was initially shown in late 2007 and early 2008 and became publicly available in October 2008.

OWL Web Ontology Language Use Cases and Requirements

W3C Recommendation 10 February 2004

This version:

http://www.w3.org/TR/2004/REC-webont-req-20040210/

Latest version:

http://www.w3.org/TR/webont-req/

Previous version:

http://www.w3.org/TR/2003/PR-webont-req-20031215/

Editor:

Jeff Heflin (Lehigh University) heflin@cse.lehigh.edu

2.3 Corporate web site management

Large corporations typically have numerous web pages concerning things like press releases, product offerings and case studies, corporate procedures, internal product briefings and comparisons, white papers, and process descriptions. Ontologies can be used to index these documents and provide better means of retrieval. Although many large organizations have a taxonomy for organizing their information, this is often insufficient. A single ontology is often limiting because the constituent categories are likely constrained to those representing one view and one granularity of a domain; the ability to simultaneously work with multiple ontologies would increase the richness of description. Furthermore, the ability to search on values for different parameters is often more useful than a keyword search with taxonomies.

An ontology-enabled web site may be used by:

* A salesperson looking for sales collateral relevant to a sales pursuit.

* A technical person looking for pockets of specific technical expertise and detailed past experience.

* A project leader looking for past experience and templates to support a complex, multi-phase project, both during the proposal phase and during execution.

A typical problem for each of these types of users is that they may not share terminology with the authors of the desired content. The salesperson may not know the technical name for a desired feature or technical people in different fields might use different terms for the same concept. For such problems, it would be useful for each class of user to have different ontologies of terms, but have each ontology interrelated so translations can be performed automatically.

Another problem is framing queries at the right level of abstraction. A project leader looking for someone with expertise in operating systems should be able to locate an employee who is an expert with both Unix and Windows.

One aspect of a large service organization is that it may have a very broad set of capabilities. But when pursuing large contracts these capabilities sometimes need to be assembled in new ways. There will often be no previous single matching project. A challenge is to reason about how past templates and documents can be reassembled in new configurations, while satisfying a diverse set of preconditions.

desdeOWL Web Ontology Language Use Cases and Requirements.

Semantic Web Health Care and Life Sciences (HCLS) Interest Group

Semantic Web Health Care and Life Sciences (HCLS) Interest Group

Introduction

The mission of the Semantic Web Health Care and Life Sciences Interest Group, part of the Semantic Web Activity, is to develop, advocate for, and support the use of Semantic Web technologies for biological science, translational medicine and health care. These domains stand to gain tremendous benefit by adoption of Semantic Web technologies, as they depend on the interoperability of information from many domains and processes for efficient decision support.

The group will:

• Document use cases to aid individuals in understanding the business and technical benefits of using Semantic Web technologies.
• Document guidelines to accelerate the adoption of the technology.
• Implement a selection of the use cases as proof-of-concept demonstrations.
• Explore the possibility of developing high level vocabularies.
• Disseminate information about the group’s work at government, industry, and academic events.

Participation

Communications of the HCLS IG are public. This includes public meeting records and access to the archives of the public-semweb-lifesci@w3.org mailing list.

The HCLS IG welcomes active participation from representatives of W3C Member organizations. If you are part of a W3C Member organization and you already have a W3C user account, you can join the HCLS IG by filling in the participation form. Otherwise, please follow the instructions on how to become a W3C Member. Active participation means participating at the weekly phone meetings, joining the discussions on the mailing list and, possibly, and participating at the face to face meetings.

desdeSemantic Web Health Care and Life Sciences (HCLS) Interest Group.

Semantic Data Extractor – W3C Q&A Weblog

Image via Wikipedia

Semantic Data Extractor

Every so often, someone writes to me or to the public-qa-dev mailing list to report bugs, or simply to give thanks on the semantic data extractor.

I’m always pleasantly surprised when I hear that, what started as a 10 minutes demonstrator of the semantics attached to HTML, is actually used as a tool by a number of developers.

With a name such «semantic data extractor», it was a bit of a shame that the tool didn’t highlight the usage of GRDDL or RDFa on pages that use either of these technologies; I have just added detection of both of these to the extractor.

As a bonus, I have also added detection of non-semantic markup: at this time, it will detect purely-wrapping <div>, empty <span>, and tables with a single row or a single column (which have good chances to be layout tables); if you have suggestions for detecting other non-semantic

Semantic Data Extractor – W3C Q&A Weblog.

ECML – Ontology Learning Tutorial

Aims of the Tutorial

• Give an overview of Ontology Learning techniques as well as a synthesis of approaches
• Provide a ‘start kit’ for Ontology Learning
• Highlight interdisciplinary aspects and opportunities for a combination of techniques

Structure of the Tutorial

• Part I Introduction – Philipp Cimiano
• Part II Ontologies in Knowledge Management & Ontology
• Life Cycle – Michael Sintek
• Part III Methods in Ontology Learning from Text –
• Paul Buitelaar & Philipp Cimiano
• Part IV Ontology Evaluation – Marko Grobelnik
• Part V Tools for Ontology Learning from Text – All
• Wrap-up Paul Buitelaar

Some pre-History

• AI: Knowledge Acquisition.  Since 60s/70s: Semantic Network Extraction and similar for Story Understanding. Systems: e.g. MARGIE (Schank et al., 1973), LUNAR (Woods, 1973)
• NLP: Lexical Knowledge Extraction. 70s/80s: Extraction of Lexical Semantic Representations from Machine Readable: Dictionaries.  Systems: e.g. ACQUILEX LKB (Copestake et al.). 80s/90s: Extraction of Semantic Lexicons from Corpora for  Information Extraction. Systems: e.g. AutoSlog (Riloff, 1993), CRYSTAL (Soderland et al., 1995)
• IR: Thesaurus Extraction. Since 60s: Extraction of Keywords, Thesauri and Controlled Vocabularies. Based on construction and use of thesauri in IR (Sparck-Jones, 1966/1986, 1971). Systems: e.g. Sextant (Grefenstette, 1992), DR-Link (Liddy, 1994)

Ontologies in Computer Science

• Ontology refers to an engineering artifact:
• It is constituted by a specific vocabulary used to describe a certain reality, as well as
• a set of explicit assumptions regarding the intended meaning of
• the vocabulary.
• An ontology is an explicit specification of a conceptualization. ([Gruber 93])
• An ontology is a shared understanding of some domain of interest. ([Uschold & Gruninger 96])

Why Develop an Ontology?

• To make domain assumptions explicit
  • Easier to change domain assumptions
  • Easier to understand and update legacy data
• To separate domain knowledge from operational knowledge
  • Re-use domain and operational knowledge separately
• A community reference for applications
• To share a consistent understanding of what information means

Tools for Ontology Learning from Text.

SEKTbar: User profiling
Jožef Stefan Institute
􀂄 A Web-based user profile is automatically generated while the user is browsing the Web.
􀂉 It is represented in the form of a user-interest-hierarchy (UIH)
􀂉 The root node holds the user’s general interest, while leaves hold more specific interests
􀂉 UIH is generated by using hierarchical k-means clustering algorithm
􀂉 Nodes of current interest are determined by comparing UIH node centroids to the centroid computed out of the m most recently visited pages.
􀂄 The user profile is visualized on the SEKTbar (Internet Explorer Toolbar)
􀂉 The user can select a node in the hierarchy to see its specific keywords and associated pages (documents)
Availability: open source (C++, .NET)
Link: http://www.textmining.net

http://www.sekt-project.com

© Paul Buitelaar, Philipp Cimiano, Marko Grobelnik, Michael Sintek: Ontology Learning from Text. Tutorial at ECML/PKDD, Oct. 2005, Porto, Portugal.

ECML-OntologyLearningTutorial-20050923.pdf (application/pdf Objeto).

URI-based Naming Systems for Science

[To preview without draft comments, remove «#dc» from the address]

Authors’ Draft 23 May 2008

This version:

2008-05-23 16:26:06 -0400

Current version:

http://purl.org/science/report/uris-for-science

Authors:

Jonathan Rees, Science Commons

Alan Ruttenberg, Science Commons

Copyright © 2008 Creative Commons Corp.. Licensed under a Creative Commons Attribution 3.0 License except where otherwise noted.

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Abstract

Formal nomenclature is essential to scientific communication because of the importance of clearly specifying the entities under discussion. It takes on additional importance as computational agents are brought in to help marshal and process the enormous amount of information available to scientists, as computational agents are unable to resolve most of the ambiguities that human readers tolerate. In this note we will examine some examples of naming systems used in science, and extrapolate from this analysis to consider the suitability and effective use of Uniform Resource Identifiers (URIs) in computationally mediated communication about science.

URI-based Naming Systems for Science.

Web of  data. Resources