Semantic Tools | Global Medical Research.org

«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:

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.
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).
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.
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.
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

Resource: Semantic Web Application - Calais

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

Resource: FOAF - Friend of a friend

FOAF - is an ingenious little application that allows you to create an RDF about yourself, and your friends.

You can create your own RDF file simply by visiting the FOAF-a-Matic.

According to the FOAF Website:

FOAF-a-matic is a simple Javascript application that allows you to create a FOAF (”Friend-of-A-Friend”) description of yourself. You can read more about FOAF in Edd Dumbill’s “XML Watch: Finding friends with XML and RDF” article, at the FOAF homepage on RDFWeb, and also the FOAF vocabulary description.

In short though, FOAF is a way to describe yourself — your name, email address, and the people you’re friends with — using XML and RDF. This allows software to process these descriptions, perhaps as part of an automated search engine, to discover information about you and the communities of which you’re a member. FOAF has the potential to drive many new interesting developments in online communities. Ben Hammersely’s “Click to the Clique” article for the Guardian Unlimited website further explores these ideas.

Reference:
FOAF project. (2008). Getting started with FOAF. Retrieved April 19, 2008, from http://www.foaf-project.org/you/index.html