OpenURL ContextObject in SPAN COinS

enero 8, 2010

OpenURL COinS: A Convention to Embed Bibliographic Metadata in HTML

stable version 1.0


COinS ContextObjects in Spans is a simple, ad hoc community specification for publishing OpenURL references in HTML.


Main Page

1. Introduction

2. Specification : OpenURL ContextObject in SPAN COinS- Embedding Citation Metadata in HTML

3. Discussion : How to use COinS in HTML

4. Details 1. Empty SPANs. 2. Why “Z3988”? 3. What is a ContextObject? 4. Choosing the type of ContextObject for Compatibility.5. XHTML6. why the span element? 7. why class and title attributes?

5. Implementations 1. Embedding Sites 2. COinS Processors 3. Other Software support for COinS

6. Links

7. Notes

Using COinS to Provide OpenURL links COinS Generator Brief Guide to Implementing ContextObjects for Journal Articles Brief Guide to Implementing ContextObjects for Books

desdeOpenURL ContextObject in SPAN COinS.

Mozilla Labs Design Challenge | Resources

noviembre 10, 2009


As part of the inaugural Design Challenge: Spring 09 we produced a series of tutorial videos about user interface design, prototyping and Firefox extension development. All videos are available in open Ogg Theora format, Quicktime format and as a Vimeo stream.

Design Focused

Interaction Seduction

Designing for Mobile

Open Source Design, Mozilla and You

Design or Die – Innovation, UCD, Web and Life

Development Focused

Extension Bootcamp: Zero to Hello World! in 45 Minutes

Stupid/Awesome Extension Development Hacks

Making Prototypes with Canvas

Making Prototypes with jQuery

Engineering Prototypes

Ship It (or: Coffee is for Closers)

desdeMozilla Labs Design Challenge | Resources.

Comparison of reference management software – Wikipedia, the free encyclopedia

noviembre 9, 2009

The following tables compare reference management software.




In the “notes” section, there is a difference between:

  • web-based, referring to applications that may be installed on a web server (usually requiring MySQL or another database and PHPperlPython, or some other language for webapps)
  • centrally-hosted website


Comparison of reference management software – Wikipedia, the free encyclopedia.

Mozilla Labs Jetpack | Exploring new ways to extend and personalize the Web

noviembre 9, 2009

Mozilla Labs Jetpack | Exploring new ways to extend and personalize the Web.

Jetpack es una aplicación qué permite desarrollar aplicaciones sobre Firefox.


Jetpack is a newly formed experiment in using open Web technologies to enhance the browser, with the goal of allowing anyone who can build a Web site to participate in making the Web a better place to work, communicate and play.

In short, Jetpack is an API for allowing you to write Firefox add-ons using the web technologies you already know.

As with all Labs experiments, Jetpack is an open source project and everyone is welcome to participate in its design, development and testing.

Links a video sobre el tema:

API de visualización de Google – Google Code

abril 1, 2009

¿Qué es el API de visualización de Google?

El API de visualización de Google permite acceder a varias fuentes de datos estructurados que puede mostrar, eligiendo entre una amplia selección de visualizaciones. El API de visualización de Google también proporciona una plataforma que se puede utilizar para crear, compartir y reutilizar visualizaciones desarrolladas por la comunidad de desarrolladores en su totalidad.

  • Inserta visualizaciones directamente en tu sitio web: podrás elegir entre una amplia gama de visualizaciones creadas por la comunidad de desarrolladores para presentar datos de forma atractiva en tu sitio web.
  • Crea, comparte y reutiliza: escribe visualizaciones y crea gadgets a partir de ellas con las sencillas extensiones de gadgets del API. Publícalas aquí o en el directorio de gadgets. Conviértete en un participante activo de la comunidad de desarrolladores; reutiliza y comparte las visualizaciones con otros usuarios.
  • Crea extensiones para los productos de Google: crea aplicaciones de visualización para productos de Google como Google Docs. Distribuye tu aplicación en una lista cada vez mayor de productos que admiten gadgets.
  • Utiliza muchas fuentes de datos y una sola API: las aplicaciones de visualización creadas con el API permiten acceder a cualquier fuente de datos de servidor compatible o acceder a datos directamente desde el cliente mediante JavaScript, sin necesidad de cambiar el código de tu aplicación.

API de visualización de Google – Google Code.

ECML – Ontology Learning Tutorial

marzo 25, 2009

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)

© 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).

PLoS ONE : accelerating the publication of peer-reviewed science

marzo 22, 2009

1. About PLoS ONE

Scientific progress requires the exchange and discussion of data and ideas. PLoS ONE is a unique publication dedicated to presenting the results of scientific research from any scientific discipline in an open-access environment. At the same time, it provides a forum in which to discuss that scientific research and so provide for each and every paper its maximum possible impact. To achieve this, PLoS ONE combines traditional peer review with ‘Web 2.0’ tools to facilitate community evaluation and discourse around the published article.

To provide open access, PLoS journals use a business model in which our expenses—including those of peer review, journal production, and online hosting and archiving—are recovered in part by charging a publication fee to the authors or research sponsors for each article they publish. For PLoS ONE the publication fee is US$1300. Authors who are affiliated with one of our Institutional Members are eligible for a discount on this fee.

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Overview of the Editorial Process

There are several types of decisions possible:

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Organization of the Manuscript

Most articles published in PLoS ONE are organized in one of three fashions:

  • Title, Authors, Affiliations, Abstract, Introduction, Results, Discussion, Materials and Methods, Acknowledgments, References, Figure Legends, and Tables.
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We advise that abstracts should not exceed 250–300 words. There are no specific length restrictions for the remaining sections of the manuscript; however, we urge authors to present and discuss their findings concisely.

Download templates:

Title (150 characters or fewer)

The title should be specific to the project, yet concise. It should be comprehensible to readers outside your field. Avoid specialist abbreviations, if possible. Titles should be presented in title case, meaning that all words except for prepositions, articles, and conjunctions should be capitalized.

Detection of Specific Sequences among DNA Fragments Separated by Gel Electrophoresis

During the online submission process, you will also provide a brief “running head” of fewer than 30 characters.

Authors and Affiliations

Provide the first names or initials (if used), middle names or initials (if used), surnames, and affiliations—department, university or organization, city, state/province (if applicable), and country—for all authors. One of the authors should be designated as the corresponding author. It is the corresponding author’s responsibility to ensure that the author list, and the summary of the author contributions to the study are accurate and complete. If the article has been submitted on behalf of a consortium, all author names and affiliations should be listed at the end of the article.


The abstract succinctly introduces the paper. We advise that it should not exceed 250 – 300 words. It should mention the techniques used without going into methodological detail and should summarize the most important results. The abstract is conceptually divided into the following three sections: Background, Methodology/Principal Findings, and Conclusions/Significance. Please do not include any citations in the abstract. Avoid specialist abbreviations if possible.


Registration details should be included when reporting results of a clinical trial (see “Reporting Clinical Trials” for details). For each location that your trial is registered, please list: name of registry, registry number, and URL of your trial in the registry database.


The introduction should put the focus of the manuscript into a broader context. As you compose the introduction, think of readers who are not experts in this field. Include a brief review of the key literature. If there are relevant controversies or disagreements in the field, they should be mentioned so that a non-expert reader can delve into these issues further. The introduction should conclude with a brief statement of the overall aim of the experiments and a comment about whether that aim was achieved.


The results section should provide details of all of the experiments that are required to support the conclusions of the paper. There is no specific word limit for this section. The section may be divided into subsections, each with a concise subheading. Large datasets, including raw data, should be submitted as supporting information files; these are published online alongside the accepted article. We advise that the results section be written in past tense.


The discussion should spell out the major conclusions of the work along with some explanation or speculation on the significance of these conclusions. How do the conclusions affect the existing assumptions and models in the field? How can future research build on these observations? What are the key experiments that must be done? The discussion should be concise and tightly argued. Conclusions firmly established by the presented data, hypotheses supported by the presented data, and speculations suggested by the presented data should be clearly identified as such. The results and discussion may be combined into one section, if desired.

Materials and Methods

This section should provide enough detail to allow full replication of the study by suitably skilled investigators. Protocols for new methods should be included, but well-established protocols may simply be referenced. We encourage authors to submit, as separate supporting information files, detailed protocols for newer or less well-established methods. These are published online only, but are linked to the article and are fully searchable.


Details of the funding sources that have supported the work should be confined to the funding statement provided in the online submission system. Do not include them in the acknowledgments.


Only published or accepted manuscripts should be included in the reference list. Meetings abstracts, conference talks, or papers that have been submitted but not yet accepted should not be cited. Limited citation of unpublished work should be included in the body of the text only. All personal communications should be supported by a letter from the relevant authors.

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Overview of the Production Process

Prior to submission, authors who believe their manuscripts would benefit from professional editing are encouraged to use language-editing and copyediting services, such as the ones described on the following Web sites. PLoS does not take responsibility for or endorse these services, and their use has no bearing on acceptance of a manuscript for publication.

Before formal acceptance, the manuscript will be checked by PLoS staff to ensure that it complies with all essential format requirements. The authors’ files are then carefully tagged to generate XML and PDF files, but will not be subject to detailed copyediting. Obtaining this service is the responsibility of the author.

Scientific Editing Services (in alphabetical order):

PLoS ONE : accelerating the publication of peer-reviewed science.

Citas del Artículo “Some Observations on Mind Map and Ontology Building Tools for Knowledge Management

marzo 11, 2009

Citas de artículo.

Some Observations on Mind Map and Ontology Building Tools for Knowledge Management±

Biplab K. Sarker*, Peter Wallace and Will Gill
Research & Development, Innovatia Inc., Saint John E2L 4R5, Canada
e-mail: {biplab.sarker, peter.wallace, will.gill}
*corresponding author

Knowledge capture, knowledge integration and knowledge delivery are the essential parts of dynamic knowledge management. E-Learning is considered to be an integral part of knowledge delivery system. Information architect plays an important role in developing the system, and are primarily responsible for capturing and modeling knowledge from various Information sources as a part of eLearning. A mind map is a diagram used to represent words, ideas, tasks or other items linked to and arranged around a central key word or idea. It is possible to build and visualize a graphical representation of ontology with various mind mapping software tools to some extent since they (mind map and ontology) both deals with concepts and form a network. However, present mind mapping tools are not capable of drawing or defining an ontology perfectly and completely. In this paper, we present a brief description on the role of mind map and ontology in e-learning, the deficiencies of mind map and review the mind map and ontology building tools. The purpose for reviewing ontology building tools is to determine the toolkit most suitable for ontology creation, editing, and mind/concept mapping from the view points of Information Architects (IAs) who play a significant role in designing knowledge management systems. The paper also gives a fundamental understanding of ontology tools available on the market as open source products as well as commercial products in terms of their capability, availability, enhancement and further development. We provide a ranked list of the tools based on our needs and suitability for the IAs.

Keywords: knowledge management, ontology, e-learning, information mapping and software tools.

“Our principle objective in this paper is to find a tool convenient for building ontology with a very basic/beginner level experience and/or even without any experience. We present available tools on the market that can help an IA to create ontology from the MindMapping perspective and at the same time we can save the ontology as .owl file that can be used for further processing such as for reasoning/inference purposes (i.e. useful for Semantic Search) without losing any properties of the ontology. The paper is organized as follows. The next section represents various ontology tools available on the market. Section 3 provides analysis and evaluation of the ontology tools in depth based on our requirements. Section 4 concludes the paper with possible recommendations to the users such as IAs with beginner level experience in building ontology using semantic web technologies.”

Ontology Tools

1 Protégé
Protégé [5] is a free, open-source ontology editor/creator and knowledge-base framework and perhaps the most widely-used ontology creation tool on the market. Using protégé, ontologies can be edited and created using RDF/OWL script language (including OWL Full, DL and Light) or through its java-based plug-and-playenvironment.
OwlViz is a mapping visualization plugin designed for Protégé. It allows the user to view an ontology as aconcept map. One of the primary requirements in our research was the ability to create mind maps and topic maps. Therefore, this functionality within Protégé would significantly raise its stock. OwlViz is one of the solutions to this dilemma. However, OwlViz does not illustrate the relationships between each object, nor does it allow the user to create or edit the ontology within this view. The user has control over the degree to which the ontology is displayed (whether it’s just classes, subclasses and siblings, or the entire ontology, for example). Similar plugins include OntoViz and Techquila, although OwlViz was the better of the three. It is an effective way for a user with limited knowledge of OWL/RDF or ontologies to visually grasp what’s going on, however, for ontology creation or editing it serves very little purpose and fails to completely meet our needs.

2 Altova SemanticWorks
Altova’s SemanticWorks [6] software is a commercially available application that provides a great deal of performance and flexibility for ontology editing/creation. It is currently considered one of the top commercial ontology-creation tools on the market and provides a rich feature set. Users with a strong foundation in OWL/RDF will be at home using SemanticWorks and will have no problem creating and editing ontologies.

SMORE [7] and SWOOP [8] are both ontology editors that allow the user to create and edit ontologies using a similar interface. In fact, both tools are essentially the same, except that SMORE has an integrated web browser component. This additional functionality allows the user to browse the internet within the program and create an ontology from the terminology used on a web page. SWOOP does allow the user to enter the URL of online ontologies and bookmark them accordingly (SMORE does not). SWOOP also offers a slightly richer feature set (enable debugging, partition automatically, pellet query) and is completely open source.

2.4 CMapTools Ontology Editor(COE)
CmapTools [9] allows users to construct, navigate, share and criticize knowledge models represented as concept maps. The COE application provides users with an outlet to create ontologies in the form of concept maps. This was the version we evaluated extensively. CMap Server allows a group to collaborate online and provide feedback to one another.

CMapTools allow the user to import various types of XML and text documents and export ontologies in OWL, N-Triple (and its various formats) and Turtle. It offers validation and concept suggestion tools. CMaps a very appealing tool for our team’s purpose as it is the only toolset which is primarily a mind/concept mapping tool with ontological features. The intended users of the tool require the ability to create maps that can be loaded by our ontology experts in ontology software and vice versa.

One of the major benefits of CMap Tools is that users need only a very fundamental understanding of ontologies (mostly the types of relationships they must define). The ontology can then be created as a concept map using a simple drag and drop interface. A styles template also allows the user to quickly and easily customize their objects, lines and map in general. When loading ontology into CMap, it recognizes the types of relationships used and provides the repository of relationships to choose from when creating a relationship within the concept map, which is very helpful for anyone working on an ontology created by another author.

Analysis and Evaluation
We initially made a set of requirements necessary to evaluate a software tool for our IAs. Table. 1 gives a good understanding of the requirements for this purpose. Based on the requirements of the table 1, each tool we used was evaluated under these requirements. We have assigned a number on a 1 for poor – 10 for excellent scales. We finally agreed upon the appropriate tools from our findings currently available on the market and proceeded to analyze them in detail according to our requirements (Table 1). Some of the tools eliminated from our final lists tools are discussed in sec. 2.6. Tools that were no longer supported or updated were also ignored. Similarly, ontology tools that focused on irrelevant fields or were parts of larger application suites, such as LMS(Learning Management System) and CMS (Content Management Systems) suites, etc., were also ignored.

1 Some Observations on Mind Map and Ontology Building Tools for Knowledge Management.

CmapTools Ontology Editor

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