Diane-Michel.com

Resource:  National Cancer Institute - Cancer Biomedical Informatics Grid

caBIG™ stands for the cancer Biomedical Informatics Grid™. caBIG™ is an information network enabling all constituencies in the cancer community – researchers, physicians, and patients – to share data and knowledge.  The components of caBIG™ are widely applicable beyond cancer as well.

The mission of caBIG™ is to develop a truly collaborative information network that accelerates the discovery of new approaches for the detection, diagnosis, treatment, and prevention of cancer, ultimately improving patient outcomes.

The goals of caBIG™ are to:

• Connect scientists and practitioners through a shareable and interoperable infrastructure
• Develop standard rules and a common language to more easily share information
• Build or adapt tools for collecting, analyzing, integrating, and disseminating information associated with cancer research and care.

Since its inception, caBIG™ has committed to the following cornerstones:

• Federated: caBIG™ software and resources are widely distributed, interlinked, and available to everyone in the cancer research community, but institutions maintain local control over their own resources and data.
• Open-development: caBIG™ tools and infrastructure are being developed through an open, participatory process. caBIG™ leverages existing resources whenever possible, rather than building new tools in every case.
• Open-access:caBIG™ resources are freely obtainable by the cancer community to ensure broad data-sharing and collaboration.
• Open-source: The caBIG™ source code is available to view, alter, and redistribute.

Learn concepts and terminology critical for working with caBIG™

• “caBIG™ Essentials” Overview   (interactive, uses Adobe Flash)
  Introduces important terminology, key concepts, and describes different ways of connecting with caBIG™.
  • Alternative format:   “caBIG™ Essentials” Overview Slides
• caBIG™ Core Concepts
  There are a number of vital concepts to both understand and connect with caBIG™. They are introduced briefly here.
• How It Works
  Describes how caBIG™ is organized and operates.
• caBIG™ Primer
  Complete high-level overview of the program’s vision and mission, organization, activities, and challenges.

Look up acronyms and unfamiliar terms

• Visit the glossary: The Glossary of Acronyms and Terms has definitions for caBIG™ acronyms and common bioinformatics terms.
• View definitions as you read:. Terms in the glossary are underlined with blue dots. Roll your mouse over the term and it’s definition will show. E.g., rest your mouse on this term - Domain Analysis Model

Stand Up To Cancer

has announced an innovative approach to research designed to bring together the best and brightest investigators from leading institutions around the world. This unique initiative, which will foster scientific collaboration and accelerate the discovery of new therapies, will be administered by the American Association for Cancer Research (AACR) under the direction of a Scientific Advisory Committee led by Nobel Laureate Phillip A. Sharp, Ph.D., Institute Professor at the Massachusetts Institute of Technology and the David H. Koch Institute for Integrative Cancer Research at MIT.

>> Streaming link to the Stand Up to Cancer Program held September 5, 2008.

Video of I’m to Young for This Foundation, founded by Matthew Zachary. He is a cancer survivor and advocate for those suffering from cancer under the age of 40.

The following represent a listing of Semantic Web Search Engines that you may be interested in trying. I will be reviewing in an upcoming posting.

Semantic Web Search Engine (SWSE)
Watson
Yahoo! Microsearch
Falcons
Swoogle
Semantic Web Search
Zitgist Search

Advancing translational research with the Semantic Web
Resource: BMC Bioinformatics 2007, 8(Suppl 3):S2doi:10.1186/1471-2105-8-S3-S2

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/8/S3/S2
…………………………………………………………………………………………………………………….
Subject: Advancing Translational Research with the Semantic Web
Resource: BMC Bioinformatics: Open Access

……………………………………………………………………………………………………………………………….

Alan Ruttenberg¹ , Tim Clark² , William Bug³ , Matthias Samwald⁴ , Olivier Bodenreider⁵ , Helen Chen⁶ , Donald Doherty⁷ , Kerstin Forsberg⁸ , Yong Gao⁹ , Vipul Kashyap¹⁰ , June Kinoshita¹¹ , Joanne Luciano¹² , M Scott Marshall¹³ , Chimezie Ogbuji¹⁴ , Jonathan Rees¹⁵ , Susie Stephens¹⁶ , Gwendolyn T Wong¹¹ , Elizabeth Wu¹¹ , Davide Zaccagnini¹⁷ , Tonya Hongsermeier¹⁰ , Eric Neumann¹⁸ , Ivan Herman¹⁹ and Kei-Hoi Cheung²⁰

¹Millennium Pharmaceuticals, Cambridge, MA, USA

²Initiative in Innovative Computing, Harvard University, Cambridge, MA, USA

³Laboratory for Bioimaging and Anatomical Informatics, Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, PA, USA

⁴Section on Medical Expert and Knowledge-Based Systems, Medical University of Vienna, Vienna, Austria

⁵National Library of Medicine, Bethesda, MD, USA

⁶Agfa Healthcare, Waterloo, Ontario, Canada

⁷Brainstage Research, Pittsburgh, PA, USA

⁸AstraZeneca, Mölndal, Sweden

⁹MassGeneral Institute for Neurodegenerative Disease, Massachusetts General Hospital, Charlestown, MA, USA

¹⁰Partners HealthCare System, Wellesley, MA, USA

¹¹Alzheimer Research Forum, Boston, MA, USA

¹²Harvard Medical School, Boston, MA, USA

¹³Integrative Bioinformatics Unit, University of Amsterdam, Amsterdam, The Netherlands

¹⁴Cleveland Clinic Foundation, Cleveland, OH, USA

¹⁵Science Commons, Cambridge, MA, USA

¹⁶Oracle, Burlington, MA, USA

¹⁷Language & Computing, Reston, VA, USA

¹⁸Teranode Corporation, Seattle, WA, USA

¹⁹World Wide Web Consortium (W3C)

²⁰Center for Medical Informatics, Yale University School of Medicine, New Haven, CT, USA

author email corresponding author email

BMC Bioinformatics 2007, 8(Suppl 3):S2doi:10.1186/1471-2105-8-S3-S2

Abstract

Background

A fundamental goal of the U.S. National Institute of Health (NIH) “Roadmap” is to strengthen Translational Research, defined as the movement of discoveries in basic research to application at the clinical level. A significant barrier to translational research is the lack of uniformly structured data across related biomedical domains. The Semantic Web is an extension of the current Web that enables navigation and meaningful use of digital resources by automatic processes. It is based on common formats that support aggregation and integration of data drawn from diverse sources. A variety of technologies have been built on this foundation that, together, support identifying, representing, and reasoning across a wide range of biomedical data. The Semantic Web Health Care and Life Sciences Interest Group (HCLSIG), set up within the framework of the World Wide Web Consortium, was launched to explore the application of these technologies in a variety of areas. Subgroups focus on making biomedical data available in RDF, working with biomedical ontologies, prototyping clinical decision support systems, working on drug safety and efficacy communication, and supporting disease researchers navigating and annotating the large amount of potentially relevant literature.

Results

We present a scenario that shows the value of the information environment the Semantic Web can support for aiding neuroscience researchers. We then report on several projects by members of the HCLSIG, in the process illustrating the range of Semantic Web technologies that have applications in areas of biomedicine.

Conclusion

Semantic Web technologies present both promise and challenges. Current tools and standards are already adequate to implement components of the bench-to-bedside vision. On the other hand, these technologies are young. Gaps in standards and implementations still exist and adoption is limited by typical problems with early technology, such as the need for a critical mass of practitioners and installed base, and growing pains as the technology is scaled up. Still, the potential of interoperable knowledge sources for biomedicine, at the scale of the World Wide Web, merits continued work.

Reference:
BMS Bioinformatics Abstracts. (2008). Advancing translational research with the Semantic Web. Retrieved July 5, 2008, from http://www.biomedcentral.com/1471-2105/8/S3/S2/abstract/

Recently I’ve been exploring Semantic Web applications as a way to understand how they might be able to help my group working within medical research. My latest find is a Semantic search currently in Beta, called True Knowledge. The following video from their Website describes their product.

Currently they are working on two products:
“1. The True Knowledge Answer Engine - a search engine-like consumer site which can answer questions, be used to add knowledge and also be used just like a conventional search engine.
2. An API product for computer-generated queries.”

References:
TrueKnowledge.com (2008). About Retrieved May 30, 2008, from http://www.trueknowledge.com/about/

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”

Recently I discovered a semantic application based on brainstorming, and natural thought processes.  It is called IMINDI. They are now accepting Beta accounts, and it seems well worth your time.

According to the developer’s Website:

“IMINDI is a brainstorming, memory and collective intelligence tool. It will help you collect your thoughts and expand your mind in new and exciting directions by exploring and connecting with the thoughts of other Like Minds. Then, IMINDI gives you useful tools to share this information with others in notes, blogs, and embedded Mind Maps.

Two things make IMINDI unique. First, many of the functions in IMINDI exist elsewhere alone, but IMINDI is the first place to bring them all together: Mind Maps, social networking, semantic tagging, recommendations, and a database underneath them all. Secondly, that database is novel: a subjective yet concise encoding of how humans think. The Mindex is finally taking shape outside of our minds in a digital representation. Just imagine how useful this will be interfacing with all the other information on the internet.

At its core IMINDI is a “Thought Engine” that can augment the way we think of new ideas, concepts and questions, as opposed to a search engine which only helps you find information or answers to questions already formed in your mind.

The IMINDI Thought Engine enables you to add your thoughts and the connections between them in a naturally radiant fashion with one thought radiating outward to one or many associated thoughts; which themselves branch outwards or back towards others in an endless network. The interface is essentially a visual map of your mind that we call a “Journey.” Each Journey has its own theme, and once you have chosen a starting thought you can travel to wherever your mind takes you. You can also explore the thoughts and Journeys of other people using IMINDI if they have shared them with you or made them public. If you find that you like them you can connect your Journey to theirs; an act that quite literally expands your thoughts and takes them in directions that you might not have taken on your own.

Meanwhile, IMINDI keeps track of everyone’s Journeys, and those that are public are all put together and interconnected in a giant database we call the Global Mindex: literally the index of the human mind. IMINDI is new because it will allow everyone’s thoughts to be collected together, and because it will define more richly how those thoughts are linked together: not just that two thoughts are linked, but how they are linked, with categories like who, what, where, when, why, and how. Unlike sterile semantic tables and ontologies, IMINDI creates a new kind of database that describes the human mind in depth.

Reference
IMINDI.com (2008). What is IMINDI? Retrieved May 28, 2008, from http://www.imindi.com/help/04What.htm.

Powerset has just released a new search for Wikipedia this month.  According to their site: “Powerset’s technology improves the entire search process. In the search box, you can express yourself in keywords, phrases, or simple questions. On the search results page, Powerset gives more accurate results, often answering questions directly, and aggregates information from across multiple articles. Finally, Powerset’s technology follows you into enhanced Wikipedia articles, giving you a better way to quickly digest and navigate content.”

To see more about Powerset please view the following video:

Powerset Demo Video from officialpowerset on Vimeo.

References:
Powerset.com (2008). Ready Powerset go. Retrieved May 27, 2008, from http://www.powerset.com/

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