Diane-Michel.com

Facilitating breakthrough medical research through collaborative intelligence, and the Semantic Web.
  • rss
  • Home
  • Diane Michel’s Blog
  • Stand Up to Cancer

Diane | November 17, 2009

Source:  National Institutes of Health

Wide Variety of Bacteria Mapped Across the Human Body

By analyzing bacterial communities in and on several people, scientists have begun to create an atlas of bacterial diversity that documents the different types of microbes that thrive in distinct regions of the human body. This research sets the stage for determining how changes in bacterial communities help to cause or prevent disease.

Our bodies play host to a wide variety of microbes, called the human microbiota, that outnumber our own cells by about 10 to 1. Many of these microbes help us stay healthy—for instance by aiding digestion or crowding out disease-causing microbes. But details about how microbial communities vary in different body regions, among people or over time are not yet well understood.

In a study funded in part by NIH’s National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), Dr. Rob Knight and his colleagues at the University of Colorado, Boulder, and George Washington University, St. Louis, began to chart a baseline map of the human microbiota in healthy people. The results were published on November 5, 2009, in the advance online edition of Science.

The scientists surveyed bacterial communities in up to 27 different locations on the bodies of 9 healthy adults. Sampled regions included hair on the head, ear canals, nostrils, mouth, lower gut and 18 different skin sites ranging from foreheads and armpits to navels and feet. Swabs from these regions were collected 4 times over 3 months.

As in other recent studies of the human microbiota, Knight and colleagues identified bacteria by extracting DNA from each sample and then analyzing a bacteria-specific gene, called the 16S ribosomal RNA gene. Overall, the detected microbes belong to 22 bacterial phyla. Four phyla were dominant, representing more than 90% of the identified bacteria.

The researchers found wide variability in bacterial communities on each person and between people. The greatest diversity over time was seen on hair, nostril and ear canal sites, as well as some skin regions, especially the forearms, palm, index finger, back of the knee and sole of the foot. These regions were also the most divergent between people, as was the lower gut. The mouth had the least bacterial variability of any tested region.

The researchers also tested how well bacteria from one body region could survive on another. They transferred bacteria from the tongue to the disinfected forearms and foreheads of some volunteers and tracked them for up to 8 hours. Tongue bacteria persisted longer on the forearms than foreheads, suggesting that the oily forehead may be too harsh a habitat for some bacteria. Bacterial communities transplanted from forehead to forearms and vice versa could not survive well in the new habitats, coming to resemble the native mix rather than the transplants within hours.

“This is the most complete view we have yet of the microbial side of ourselves, one that our group and others will be adding to over the coming years,” says Knight. “If we can better understand this variation, we may be able to begin searching for biomarkers for disease.”

—by Vicki Contie

Related Links:
  • Unexpected Microbe Diversity on Human Skin:
    http://www.nih.gov/researchmatters/june2009/06012009skin.htm
  • Human Microbiome Project:
    http://nihroadmap.nih.gov/hmp/

Reference:
National Institutes of Health (November 16, 2009) Wide variety of bacteria mapped across the human body.  Retrieved November 16, 2009 from http://www.nih.gov/researchmatters/november2009/11162009bacteria.htm

Comments
No Comments »
Categories
Uncategorized
Tags
advance online edition, Bacteria, Bacteriology, Colorado, diabetes, Digestive and Kidney Diseases, Environmental microbiology, George Washington University St. Louis, Gut flora, Health/Medical/Pharmaceuticals, Kidney Diseases, Microbiology, Microorganism, NIH's National Institute of Diabetes and Digestive, Rob Knight, Skin, St. Louis, University of Colorado Boulder, Vicki Contie
Comments rss Comments rss
Trackback Trackback

Genome Screen Reveals Two-Way Communication Between Common Biological Pathways and Body’s Daily Clock

Diane | October 9, 2009
September 17, 2009

News Release
Insulin, folate metabolism influence circadian rhythms, according to Penn studyPHILADELPHIA – While scientists have known for several years that our body’s internal clock helps regulate many biological processes, researchers have found that the reverse is also true: Many common biological processes – including insulin metabolism – regulate the clock, according to a new study by investigators at the University of Pennsylvania School of Medicine, the Genomics Institute of the Novartis Research Foundation, and the University of California at San Diego.
Related Image

  • The Expanded Clock Gene Network. (Click to view larger version.)

Related Links

  • BioGPS Database
  • BioGPS Circadian Gene Data
  • A Biological Basis for the 8-Hour Workday?
  • Institute for Translational Medicine and Therapeutics
  • University of Pennsylvania School of Medicine
  • University of Pennsylvania Health System

The new data, published online in Cell this week, suggest that someday physicians may be able to use small molecules that inhibit or stimulate these biological processes in order to influence a person’s clock when it gets out of sync due to jetlag or shift work. Researchers may also be able to find new ways to treat metabolic disorders that are intimately tied to the body’s daily cycles.

Using a genome-wide screen, the investigators found that reducing expression of any one of hundreds of genes could substantially alter the length of the circadian cycle, which controls the 24-hour sleep/wake cycle. The clock-influencing genes are involved in a large number of biological processes, but the researchers found that components of insulin metabolism, folate metabolism, and the cell cycle were overrepresented in the gene screen, suggesting that these pathways are closely linked to the clock.

“Clock biologists all appreciated that the communication went one direction – from the clock to biological processes – but I don’t think anyone anticipated that there would be this level of integration with cell metabolism and the cell cycle, or all these other pathways impinging on clock function,” says John Hogenesch, PhD, Associate Professor of Pharmacology in the Institute for Translational Medicine and Therapeutics at Penn. Hogenesch is a co-senior author on the paper with Steve Kay, Dean of the Division of Biological Sciences at UCSD. “There were some hints this might occur for some genes, but not to this extent.”

The idea that biological processes might have feedback systems with the circadian clock makes some sense to Hogenesch. For example, he points to the influence of insulin metabolism, saying “If your energy requirements aren’t being met, instead of spending a lot of energy on a cell division, a cell might necessarily delay it. It is the same strategy we use when we are not ready to do something, we delay. Maybe procrastination is an evolutionary cellular strategy enabled by the clock to confront situations where resources are limited.”

While biologists regularly draw molecular pathways as if they are distinct from one another, they know the reality is much different. “This is a good example showing how dozens of pathways are functionally interconnected with clock function and vice versa,” Hogenesch says. “It is important to remember that when you start to change function with a drug, for example, that the perturbation can have unanticipated consequence. Sometimes these consequences are good, but sometimes not.”

Hogenesch stresses that while the new experiments show a feedback loop between biological processes and the clock in individual cells in culture, it is not yet clear how feedback systems work in the whole organism. Currently the team is working on biochemical and genetic experiments to answer that question.

In addition to publishing the data in the journal, the investigators have displayed the data on the BioGPS open-access searchable database (http://biogps.gnf.org). The circadian genome-wide screen data can be found at http://biogps.gnf.org/circadian/ and are linked to expression data from Penn, gene function data at Wikipedia and the National Center for Biotechnology Information (NCBI), as well as gene structure information from the University of California at Santa Cruz.

Hogenesch, who helped develop the website when he worked at the Genomics Institute of the Novartis Research Foundation in San Diego, said the site relies on web 2.0 technology and is very simple to use and customize. He and his colleagues built the site because many researchers want to do large database searches but are not computer scientists or informatics specialists. “Andy Su [of Novartis] and I decided to develop a site that even my mom could use, and pitched at the 90 percent of biologists who want to use something but don’t have the skill sets. We decided to build something that would allow them to take advantage of large datasets such as this one.”

Co-first authors on the paper are Eric E. Zhang and Tsuyoshi Hirota of the Genomics Institute of the Novartis Research Foundation, and UCSD, and Andrew C. Liu, of the Genomics Institute of the Novartis Research Foundation and the University of Memphis, Tenn. Other authors on the paper include Loren J. Miraglia, Genevieve Welch, Xianzhong Liu, Jon W. Huss III, Jeff Janes, and Andrew I. Su of the Genomics Institute of the Novartis Research Foundation, and Pagkapol Y. Pongsawakul, Ann Atwood, and Steve A. Kay of UCSD.

This research was funded by Silvio O. Conte Center and the National Institute of Mental Health. 

###

PENN Medicine is a $3.6 billion enterprise dedicated to the related missions of medical education, biomedical research, and excellence in patient care. PENN Medicine consists of the University of Pennsylvania School of Medicine (founded in 1765 as the nation’s first medical school) and the University of Pennsylvania Health System.

Penn’s School of Medicine is currently ranked #3 in the nation in U.S.News & World Report’s survey of top research-oriented medical schools; and, according to the National Institutes of Health, received over $366 million in NIH grants (excluding contracts) in the 2008 fiscal year. Supporting 1,700 fulltime faculty and 700 students, the School of Medicine is recognized worldwide for its superior education and training of the next generation of physician-scientists and leaders of academic medicine.

The University of Pennsylvania Health System (UPHS) includes its flagship hospital, the Hospital of the University of Pennsylvania, rated one of the nation’s top ten “Honor Roll” hospitals by U.S.News & World Report; Pennsylvania Hospital, the nation’s first hospital; and Penn Presbyterian Medical Center, named one of the nation’s “100 Top Hospitals” for cardiovascular care by Thomson Reuters. In addition UPHS includes a primary-care provider network; a faculty practice plan; home care, hospice, and nursing home; three multispecialty satellite facilities; as well as the Penn Medicine at Rittenhouse campus, which offers comprehensive inpatient rehabilitation facilities and outpatient services in multiple specialties.

Comments
No Comments »
Categories
Uncategorized
Tags
2.0 technology, Andrew C. Liu, Andrew I. Su, Andy Su, Ann Atwood, Associate Professor, author, Biologist, Biotechnology, California, Circadian rhythms, Dean, Division of Biological Sciences, Division of Biological Sciences at UCSD, energy, energy requirements, Eric E. Zhang, feedback systems, Genevieve Welch, Genomics, Genomics Institute, Genomics Institute of the Novartis Research Foundation, Health/Medical/Pharmaceuticals, Institute for Translational Medicine and Therapeutics, Jeff Janes, John Hogenesch, Jon W. Huss III, Loren J. Miraglia, metabolic disorders, multispecialty satellite facilities, National Center for Biotechnology Information, National Institute of Health, National Institute of Mental Health, Novartis, Novartis AG, Novartis Research Foundation, outpatient services, Pagkapol Y. Pongsawakul, Penn Medicine, Penn Presbyterian Medical Center, Pennsylvania, Pennsylvania Hospital, Presbyterian Medical Center, Professor of Pharmacology, Rittenhouse campus, San Diego, Santa Cruz, Silvio O. Conte Center, Sleep, Steve A. Kay, Steve Kay, Tennessee, the University of Pennsylvania, the University of Pennsylvania School of Medicine, Thomson Reuters Group Ltd, Tsuyoshi Hirota, U.S.News & World Report, UCSD, United States, University of California at San Diego, University of Memphis, University of Pennsylvania Health System, University of Pennsylvania School of Medicine, USD, Xianzhong Liu
Comments rss Comments rss
Trackback Trackback

A little fun with social media and health

Diane | August 7, 2009

Enjoy this fun video and how hospitals are using social media as a way to communicate.

Comments
No Comments »
Categories
Uncategorized
Comments rss Comments rss
Trackback Trackback

Semantic Web: Wikipedia’s Collective Definition

Diane | May 30, 2008

As I work at trying to wrap my brain around the power of the Semantic Web, I have actually found Wikipedia to be quite helpful for quick succinct definitions. Here is a nice roundup of the latest postings on Semantic Web definitions: (Again directly from Wikipedia)

The Semantic Web is an evolving extension of the World Wide Web in which the semantics of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content. It derives from W3C director Tim Berners-Lee’s vision of the Web as a universal medium for data, information, and knowledge exchange.

At its core, the semantic web comprises a set of design principles, collaborative working groups, and a variety of enabling technologies. Some elements of the semantic web are expressed as prospective future possibilities that are yet to be implemented or realized. Other elements of the semantic web are expressed in formal specifications.

Some of these include Resource Description Framework (RDF), a variety of data interchange formats (e.g. RDF/XML, N3, Turtle, N-Triples), and notations such as RDF Schema (RDFS) and the Web Ontology Language (OWL), all of which are intended to provide a formal description of concepts, terms, and relationships within a given knowledge domain.

Contents
1 Purpose
2 Relationship to the Hypertext Web
2.1 Markup
2.2 Descriptive and extensible
2.3 Semantic v.s. non-Semantic Web
3 Skeptical reactions
3.1 Practical feasibility
3.2 An unrealized idea
3.3 Censorship and privacy
3.4 Doubling output formats
3.5 Need
4 Components
5 Projects
5.1 Neurocommons
5.2 FOAF
5.3 SIOC
5.4 SIMILE
5.5 Linking Open Data
6 Services
6.1 Notification Services
6.2 Semantic Web Ping Service
6.3 Piggy Bank
6.4 Triplify
7 See also
8 Notes
9 Further reading
10 External links
11 Semantic Web Software & Demonstrations

Purpose
Humans are capable of using the Web to carry out tasks such as finding the Finnish word for “cat”, reserving a library book, and searching for a low price on a DVD. However, a computer cannot accomplish the same tasks without human direction because web pages are designed to be read by people, not machines. The semantic web is a vision of information that is understandable by computers, so that they can perform more of the tedious work involved in finding, sharing and combining information on the web.

Tim Berners-Lee originally expressed the vision of the semantic web as follows[6]:

I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A ‘Semantic Web’, which should make this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The ‘intelligent agents’ people have touted for ages will finally materialize.

– Tim Berners-Lee, 1999

Semantic publishing will benefit greatly from the semantic web. In particular, the semantic web is expected to revolutionize scientific publishing, such as real-time publishing and sharing of experimental data on the Internet. This simple but radical idea is now being explored by W3C HCLS group’s Scientific Publishing Task Force.

Tim Berners-Lee has further stated:

People keep asking what Web 3.0 is. I think maybe when you’ve got an overlay of scalable vector graphics – everything rippling and folding and looking misty – on Web 2.0 and access to a semantic Web integrated across a huge space of data, you’ll have access to an unbelievable data resource.

– Tim Berners-Lee, A ‘more revolutionary’ Web

Relationship to the Hypertext Web

Markup
Many files on a typical computer can be loosely divided into documents and data. Documents like mail messages, reports, and brochures are read by humans. Data, like calendars, addressbooks, playlists, and spreadsheets are presented using an application program which lets them be viewed, searched and combined in many ways.

Currently, the World Wide Web is based mainly on documents written in Hypertext Markup Language (HTML), a markup convention that is used for coding a body of text interspersed with multimedia objects such as images and interactive forms. Metadata tags, for example, such as these:



provide a method by which computers can categorise the content of web pages.

The semantic web takes the concept further; it involves publishing the data in a language, Resource Description Framework (RDF), specifically for data, so that it can be categorized as human perception and be “understood” by computers. So all data is not only stored, but filed and well handled.

HTML describes documents and the links between them. RDF, by contrast, describes arbitrary things such as people, meetings, or airplane parts.

For example, with HTML and a tool to render it (perhaps Web browser software, perhaps another user agent), one can create and present a page that lists items for sale. The HTML of this catalog page can make simple, document-level assertions such as “this document’s title is ‘Widget Superstore’”. But there is no capability within the HTML itself to assert unambiguously that, for example, item number X586172 is an Acme Gizmo with a retail price of €199, or that it is a consumer product. Rather, HTML can only say that the span of text “X586172″ is something that should be positioned near “Acme Gizmo” and “€ 199″, etc. There is no way to say “this is a catalog” or even to establish that “Acme Gizmo” is a kind of title or that “€ 199″ is a price. There is also no way to express that these pieces of information are bound together in describing a discrete item, distinct from other items perhaps listed on the page.

Descriptive and extensible
The semantic web addresses this shortcoming, using the descriptive technologies Resource Description Framework (RDF) and Web Ontology Language (OWL), and the data-centric, customizable Extensible Markup Language (XML). These technologies are combined in order to provide descriptions that supplement or replace the content of Web documents. Thus, content may manifest as descriptive data stored in Web-accessible databases, or as markup within documents (particularly, in Extensible HTML (XHTML) interspersed with XML, or, more often, purely in XML, with layout/rendering cues stored separately). The machine-readable descriptions enable content managers to add meaning to the content, i.e. to describe the structure of the knowledge we have about that content. In this way, a machine can process knowledge itself, instead of text, using processes similar to human deductive reasoning and inference, thereby obtaining more meaningful results and facilitating automated information gathering and research by computers.

Skeptical reactions

Practical feasibility
Critics question the basic feasibility of a complete or even partial fulfillment of the semantic web. Some develop their critique from the perspective of human behavior and personal preferences, which ostensibly diminish the likelihood of its fulfillment (see e.g., metacrap). Other commentators object that there are limitations that stem from the current state of software engineering itself (see e.g., Leaky abstraction).

Where semantic web technologies have found a greater degree of practical adoption, it has tended to be among core specialized communities and organizations for intra-company projects.[8] The practical constraints toward adoption have appeared less challenging where domain and scope is more limited than that of the general public and the World-Wide Web.


An unrealized idea

The original 2001 Scientific American article by Berners-Lee described an expected evolution of the existing Web to a Semantic Web. Such an evolution has yet to occur. Indeed, a more recent article from Berners-Lee and colleagues stated that: “This simple idea, however, remains largely unrealized.” Nonetheless, sometimes they even claim that many of the components of the initial vision have already been deployed.

Censorship and privacy
Enthusiasm about the semantic web could be tempered by concerns regarding censorship and privacy. For instance, text-analyzing techniques can now be easily bypassed by using other words, metaphors for instance, or by using images in place of words. An advanced implementation of the semantic web would make it much easier for governments to control the viewing and creation of online information, as this information would be much easier for an automated content-blocking machine to understand. In addition, the issue has also been raised that, with the use of FOAF files and geo location meta-data, there would be very little anonymity associated with the authorship of articles on things such as a personal blog.

Doubling output formats
Another criticism of the semantic web is that it would be much more time-consuming to create and publish content because there would need to be two formats for one piece of data: one for human viewing and one for machines. However, many web applications in development are addressing this issue by creating a machine-readable format upon the publishing of data or the request of a machine for such data. The development of microformats has been one reaction to this kind of criticism.

Specifications such as eRDF and RDFa allow arbitrary RDF data to be embedded in HTML pages. The GRDDL (Gleaning Resource Descriptions from Dialects of Language) mechanism allows existing material (including microformats) to be automatically interpreted as RDF, so publishers only need to use a single format, such as HTML.

Need
The idea of a ’semantic web’ necessarily coming from some marking code other than simple HTML is built on the assumption that it is not possible for a machine to appropriately interpret code based on nothing but the order relationships of letters and words. If this is not true, then a ’semantic web’ may be possible built on HTML alone, making a specially built ’semantic web’ coding system unnecessary.

There are latent dynamic network models that can, under certain conditions, be ‘trained’ to appropriately ‘learn’ meaning based on order data, in the process ‘learning’ relationships with order (a kind of rudimentary working grammar). See for example latent semantic analysis.

Components
The semantic web comprises the standards and tools of XML, XML Schema, RDF, RDF Schema and OWL that are organized in the Semantic Web Stack. The OWL Web Ontology Language Overview describes the function and relationship of each of these components of the semantic web:

XML provides an elemental syntax for content structure within documents, yet associates no semantics with the meaning of the content contained within.

XML Schema is a language for providing and restricting the structure and content of elements contained within XML documents.

RDF is a simple language for expressing data models, which refer to objects (“resources”) and their relationships. An RDF-based model can be represented in XML syntax.

RDF Schema is a vocabulary for describing properties and classes of RDF-based resources, with semantics for generalized-hierarchies of such properties and classes.

OWL adds more vocabulary for describing properties and classes: among others, relations between classes (e.g. disjointness), cardinality (e.g. “exactly one”), equality, richer typing of properties, characteristics of properties (e.g. symmetry), and enumerated classes.

SPARQL is a protocol and query language for semantic web data sources.
Current ongoing standardizations include:

Rule Interchange Format (RIF) as the Rule Layer of the Semantic Web Stack
The intent is to enhance the usability and usefulness of the Web and its interconnected resources through:

Servers which expose existing data systems using the RDF and SPARQL standards. Many converters to RDF exist from different applications. Relational databases are an important source. The semantic web server attaches to the existing system without affecting its operation.

Documents “marked up” with semantic information (an extension of the HTML tags used in today’s Web pages to supply information for Web search engines using web crawlers). This could be machine-understandable information about the human-understandable content of the document (such as the creator, title, description, etc., of the document) or it could be purely metadata representing a set of facts (such as resources and services elsewhere in the site). (Note that anything that can be identified with a Uniform Resource Identifier (URI) can be described, so the semantic web can reason about animals, people, places, ideas, etc.) Semantic markup is often generated automatically, rather than manually. Common metadata vocabularies (ontologies) and maps between vocabularies that allow document creators to know how to mark up their documents so that agents can use the information in the supplied metadata (so that Author in the sense of ‘the Author of the page’ won’t be confused with Author in the sense of a book that is the subject of a book review).

Automated agents to perform tasks for users of the semantic web using this data
web-based services (often with agents of their own) to supply information specifically to agents (for example, a Trust service that an agent could ask if some online store has a history of poor service or spamming).

Projects
This section provides some example projects and tools, but is very incomplete. The choice of projects is somewhat arbitrary but may serve illustrative purposes.

Neurocommons
The Neurocommons is an open RDF database developed by Science Commons. It was compiled from major life sciences databases with a focus on neuroscience. It is accessible via a web-based front end using the SPARQL query language.

FOAF
A popular application of the semantic web is Friend of a Friend (or FoaF), which describes relationships among people and other agents in terms of RDF.

SIOC
The SIOC Project – Semantically-Interlinked Online Communities provides a vocabulary of terms and relationships that model web data spaces. Examples of such data spaces include, among others: discussion forums, weblogs, blogrolls / feed subscriptions, mailing lists, shared bookmarks, image galleries.

SIMILE
Semantic Interoperability of Metadata and Information in unLike Environments Massachusetts Institute of Technology

SIMILE is a joint project, conducted by the MIT Libraries and MIT CSAIL, which seeks to enhance interoperability among digital assets, schemata/vocabularies/ontologies, meta data, and services..

Linking Open Data

Datasets in the Linking Open Data project, as of September 2007The Linking Open Data project is a community lead effort to create openly accessible, and interlinked, RDF Data on the Web. The data in question takes the form of RDF Data Sets drawn from a broad collection of data sources. There is a focus on the Linked Data style of publishing RDF on the Web. See #Triplify for a small plugin to expose data from your Web application as Linked Data. The project is one of several sponsored by the W3C’s Semantic Web Education & Outreach Interest Group (SWEO).

Services

Notification Services

Semantic Web Ping Service
The Semantic Web Ping Service is a notification service for the semantic web that tracks the creation and modification of RDF based data sources on the Web. It provides Web Services for loosely coupled monitoring of RDF data. In addition, it provides a breakdown of RDF data sources tracked by vocabulary that includes: SIOC, FOAF, DOAP, RDFS, and OWL.

Piggy Bank
Another freely downloadable tool is the Piggy Bank plug-in to Firefox. Piggy Bank works by extracting or translating web scripts into RDF information and storing this information on the user’s computer. This information can then be retrieved independently of the original context and used in other contexts, for example by using Google Maps to display information. Piggy Bank works with a new service, Semantic Bank, which combines the idea of tagging information with the new web languages. Piggy Bank was developed by the Simile Project, which also provides RDFizers, tools that can be used to translate specific types of information, for example weather reports for US zip codes, into RDF. Efforts like these could ease a potentially troublesome transition between the web of today and its semantic successor.

Triplify
Triplify is a small plugin for database driven Web applications which:
exposes RDF, Linked Data and JSON is extremely simple to configure – just provide some SQL queries, selecting the information to be exposed, is complemented by a light-weight registry for RDF data sources.

Since the Web mainly consists of Web pages served by a relatively small number of Web applications (consider all the Drupal, Wordpress and MediaWiki installations) once a Triplify configuration is created it can be reused without modifications for other instances of the same Web application thus making the “semantification” of Web applications very easy.

See also
Website Parse Template
List of emerging technologies
Semantic Web Services
Social Semantic Web
Swoogle
Web 3.0

Notes
^ Berners-Lee, Tim; James Hendler and Ora Lassila (May 17, 2001). “The Semantic Web”. Scientific American Magazine. Retrieved on 2008-03-26.
^ a b W3C Semantic Web Frequently Asked Questions. W3C. Retrieved on 2008-03-13.
^ Herman, Ivan (2008-03-07). Semantic Web Activity Statement. W3C. Retrieved on 2008-03-13.
^ Design Issues. W3C. Retrieved on 2008-03-13.
^ Herman, Ivan (2008-03-12). W3C Semantic Web Activity. W3C. Retrieved on 2008-03-13.
^ Berners-Lee, Tim; Fischetti, Mark (1999). Weaving the Web. HarperSanFrancisco, chapter 12. ISBN 9780062515872.
^ Victoria Shannon (2006-06-26). A ‘more revolutionary’ Web. International Herald Tribune. Retrieved on 2006-05-24.
^ a b Ivan Herman (2007). State of the Semantic Web. Semantic Days 2007. Retrieved on 2007-07-26.
^ Berners-Lee, Tim (2001-05-01). The Semantic Web. Scientific American. Retrieved on 2008-03-13.
^ Nigel Shadbolt, Wendy Hall, Tim Berners-Lee (2006). The Semantic Web Revisited. IEEE Intelligent Systems. Retrieved on 2007-04-13.

Further reading
Antoniou, Grigoris (2004-04-01). A Semantic Web Primer. The MIT Press. ISBN 0262012103.
Davies, John (2006-07-11). Semantic Web Technologies: Trends and Research in Ontology-based Systems. Wiley. ISBN 0470025964.
Passin, Thomas B. (2004-03-01). Explorer’s Guide to the Semantic Web. Manning Publications. ISBN 1932394206.

External links
W3C Semantic Web Activity
semanticweb.org the Semantic Web community wiki, including descriptions of many related tools, events, and ontologies
The Semantic Web: An Introduction
Semantic Web Tutorial
Semantic Web in c#
Introduction to Ontologies and Semantic Web

Semantic Web Software & Demonstrations
Human Computation Video Luis Von Ahn presents innovative techniques to incorporate RDF info into a database of images, video or other group of data.
MetaQuery Tools provided by Ambient Webs LLC
Open Source Semantic Tools
SWED portal provided by WordPressHelp


References:
Wikipedia (May 29, 2008). Semantic Web. Retrieved May 29, 2008, from “http://en.wikipedia.org/wiki/Semantic_Web”

Comments
No Comments »
Categories
Semantic Tutorial, Semantic Web, Uncategorized
Comments rss Comments rss
Trackback Trackback

Semantic MediaWiki

Diane | May 27, 2008

Semantic MediaWiki (SMW) is a free extension of MediaWiki – the wiki-system powering Wikipedia – that helps to search, organise, tag, browse, evaluate, and share the wiki’s content. While traditional wikis contain only texts which computers can neither understand nor evaluate, SMW adds semantic annotations that bring the power of the Semantic Web to the wiki.

Introduction to Semantic Mediawiki

Wikis have become a great tool for collecting and sharing knowledge in communities. This knowledge is mostly contained within texts and multimedia files, and is thus easily accessible for human readers. But wikis get bigger and bigger, and it can be very time-consuming to look for an answer inside a wiki. As a simple example, consider the following question a user might have:

«What are the hundred world-largest cities with a female mayor?»

Wikipedia should be able to provide the answer: it contains all large cities, their mayors, and articles about the mayor that tell us about their gender. Yet the question is almost impossible to answer for a human, since one would have to read all articles about all large cities first! Even if the answer is found, it might not remain valid for very long. Computers can deal with large datasets much easier, yet they are not able to support us very much when seeking answers from a wiki: Even sophisticated programs cannot yet read and «understand» human-language texts unless the topic and language of the text is very restricted. The wiki’s keyword search does not help either in discovering complex relationships.

Semantic MediaWiki enables wiki communities to make some of their knowledge computer-processable, e.g. to answer the above question. The hard problem for the computer is to find out what the words in a wiki page (e.g. about cities) mean. Articles contain many names, but which one is the current mayor? Humans can easily grasp the problem by looking into a language edition of Wikipedia that they do not understand (Korean is a good start unless you are fluent there). While single tokens (names, numbers, …) might be readable, it is impossible to understand their relevance in the article. Similarly, computers need some help for making sense of wiki texts.

In Semantic MediaWiki, editors therefore add «hints» to the information in wiki pages. For example, someone can mark a name as being the name of the current mayor. This is done by editors who modify a page and put some special text-markup around the mayor’s name. After this, computers can access this information (of course they still do not «understand» it, but they can search for it if we ask them to), and support users in many different ways.

More information can be found in the user manual.

Where SMW can help

Semantic MediaWiki introduces some additional markup into the wiki-text which allows users to add “semantic annotations” to the wiki. While this first appears to make things more complex, it can also greatly simplify the structure of the wiki, help users to find more information in less time, and improve the overall quality and consistency of the wiki. To illustrate this, we provide some examples from the daily business of Wikipedia:

  1. Manually generated lists. Wikipedia is full of manually edited listings such as this one. Those lists are prone to errors, since they have to be updated manually. Furthermore, the number of potentially interesting lists is huge, and it is impossible to provide all of them in acceptable quality. In SMW, lists are generated automatically like this. They are always up-to-date and can easily be customised to obtain further information.
  2. Searching information. Much of Wikipedia’s knowledge is hopelessly buried within millions of pages of text, and can hardly be retrieved at all. For example, at the time of this writing, there is no list of female physicists in Wikipedia. When trying to find all women of this profession that are featured in Wikipedia, one has to resort to textual search. Obviously, this attempt is doomed to fail miserably. Note that among the 20 first results, only 5 are about people at all, and that Marie Curie is not contained in the whole result set (since “female” does not appear on her page). Again, querying in SMW easily solves this problem (in this case even without further annotation, since existing categories suffice to find the results).
  3. Inflationary use of categories. The need for better structuring becomes apparent by the enormous use of categories in Wikipedia. While this is generally helpful, it has also led to a number of categories that would be mere query results in SMW. For some examples consider the categories Rivers in Buckinghamshire, Asteroids named for people, and 1620s deaths, all of which could easily be replaced by simple queries that use just a handful of annotations. Indeed, in this example Category:Rivers, Property:located in, Category:Asteroids, Category:People, Property:named after, and Property:date of death would suffice to create thousands of similar listings on the fly, and to remove hundreds of Wikipedia categories.
  4. Inter-language consistency. Most articles in Wikipedia are linked to according pages in different languages, and this can be done for SMW’s semantic annotation as well. With this knowledge, you can ask for the population of Bejing that is given in Chinese Wikipedia without reading a single word of this language. This can be exploited to detect possible inconsistencies that can then be resolved by editors. For example, the population of Edinburgh at the time of this writing is different in English, German, and French Wikipedia.
  5. External reuse. Some desktop tools today make use of Wikipedia’s content, e.g. the media player Amarok displays articles about artists during playback. However, such reuse is limited to fetching some article for immediate reading. The progam cannot exploit the information (e.g. to find songs of artists that have worked for the same label), but can only show the text in some other context. SMW leverages a wiki’s knowledge to be useable outside the context of its textual article. Since semantic data can be published under a free license, it could even be shipped with a software to save bandwidth and download time.

Reference:
Semantic-Mediawiki.org (2008). Help:  Introduction to Semantic-Media Wiki. Retrieved May 27, 2008, from http://semantic-mediawiki.org/wiki/Help:Introduction_to_Semantic_MediaWiki

Comments
No Comments »
Categories
Uncategorized
Tags
textual search
Comments rss Comments rss
Trackback Trackback

Semantic Web Application: Calais

Diane | May 24, 2008

Interested in creating your own semantic metadata?  Calais, produced by Thomson Reuters might be a solution for you.  The web service is currently free for either commercial or non-commercial use.

One part of their service that one can put to work right away is “Tagaroo” a semantic Web plug in for Word Press blogs that automatically generates semantic tags.  All one has to do is register for an account for free.    Upon registering an API key is emailed to you. Then it is possible to download and use the plug in.

“The Calais Web Service automatically creates rich semantic metadata for the content you submit – in well under a second. Using natural language processing, machine learning and other methods, Calais analyzes your document and finds the entities within it. But, Calais goes well beyond classic entity identification and returns the facts and events hidden within your text as well.”

Reference:

OpenCalais.com (2008). About Open Calais.  Retrieved May 23, 2008, from http://www.opencalais.com/about

Comments
No Comments »
Categories
Uncategorized
Tags
free semantic web service, Semantic Web, semantic web service, semantic web tagging, tagaroo
Comments rss Comments rss
Trackback Trackback

Navigation

  • About Me
  • Breast Cancer
  • Brilliant Thinkers
  • Business
  • Cancer Research
  • Cardiovascular Health
  • Charter for Compassion
  • Collective Intelligence
  • Diabetes Research
  • Education
  • Education: Medical
  • Education: Technologies
  • Future Think
  • Global Health
  • Growing Cells
  • Health Care Reform
  • Healthcare Reform
  • Heart Disease
  • Information Design
  • Lung Disease
  • Medical Research
  • Medical Research Guidelines
  • Obama Healthcare Initiatives
  • Obama Healthcare Reform
  • Online Learning
  • Open Source Medical Information
  • Parkinsons
  • Rock Stars of Science
  • Second Hand Smoke
  • Second Life
  • Second Life Introduction
  • Second Life Medical Research
  • Secondhand smoke
  • Semantic search
  • Semantic Tutorial
  • Semantic Web
  • Semantic Web – Medical
  • Semantic Web Applications
  • Semantic Web Search Engines
  • SL: Medical Research
  • SL: Teacher's Resources
  • Stem Cell Research
  • TB – Tuberculosis
  • TED
  • Tim Berners-Lee
  • Uncategorized
  • Usability
  • Virtual Reality: Second Life
  • Web 2.0

Search

rss Comments rss valid xhtml 1.1 design by jide powered by Wordpress get firefox