NosyJoe - Social search engine

NosyJoe is a social search engine that relies on you to sniff for and submit the web's interesting content and offers basically meaningful search results in the form of readable complete sentences and smart tags. NosyJoe is built upon the fundamental belief people are better than robots in finding the interesting, important and quality content around Web. Rather than crawling the entire Web building a massive index of information, which aside being an enormous technological task, requires huge amount of resources and is time consuming process would also load lots of unnecessary information people don't want, NosyJoe is focused just on those parts of the Web people think are important and find interesting enough to submit and share with others.

NosyJoe applies a semantic based textual analysis and intelligently extracts the meaningful structures like sentences, phrases, words and names from the content in order to make it just one idea more meaningfully searchable.

The information is then clustered and published across the NosyJoe's platform into contextual channels, time and source categories and semantic phrasal, name and word tags are also applied to meaningfully connect them together, which makes even the smallest content component web visible, indexable and findable. At the end a set of algorithms and user patterns are applied to further rank, organize and share the information.

Knoodl

Revelytix is a company based in Hunt Valley, MD that has created Knoodl, a web-based solution for ontology and/or controlled vocabulary editing. It combines features of vocabulary and ontology editing, interactive visualization, and wikis (which would be useful for annotating ontology elements). They have a community site for maintaining a variety of special topic ontologies and supporting documentation, although the communities vary widely in degree of completeness (many are test stubs, but the Tutorial community provides useful examples). You can also subscribe to RSS feeds to track changes to your favorite ontology. Because ontology editing is typically a community process, it's nice to see progress in establishing tools that provide an improved level of support to the community editing process.

Freebase

Freebase is a free, publicly usable database of information created by Metaweb. All content is user generated. An API is provided for developers to access the data and develop mashups or applications on top of it. A recent post on Programmable Web describes how Freebase can be used to create semantic web applications.

Even though the data in Freebase is user generated, it is very structured, with each piece of information having a "type" that can be used to describe the relationships between two pieces of data. For example, the Freebase entry for Baltimore is of the type "Location". The location type has been defined to have a latitude/longitude field, a "contains" field (for describing things in that location) , and a "contained by" field (ie Maryland, USA, North America, etc).

A lot of the data seems to have been automatically imported from Wikipedia. By using a more structured format, though, Freebase allows developers to do more with the available data.

Metacrap

Cory Doctorow talks about meta data and the inherent problems. Tags and other methods for organizing data have their benefits and downsides. Doctorow talks about all the problems with tagging and how the human element can cloud the data and prevent useful results...

http://www.well.com/~doctorow/metacrap.htm

Semantic Web Enablers

While doing some research I learned about the semantic wiki extension to MediaWiki called Semantic MediaWiki, as well as DBpedia. Both appear to be key approaches to enabling the semantic web. Semantic MediaWiki, described in this publication, describes an approach to eliciting Wikipedia-style knowledge from a broad base of contributors using semantic web W3C standards that permits the factual imormation to be machine processable. DBpedia is a community effort to transform Wikipedia information into RDF triples, rendering Wikipedia knowledge usable for the Semantic Web. These two tools illustrate the spectrum of approaches to populating Semantic Web knowledge stores, ranging from manual to automated methodologies.

Social Semantic Tagweb Ontology is not a Dewy Decimal System Taxonomy

I stumbled upon a very interesting presentation that Tom Gruber gave at The 5th International Semantic Web Conference about how the Social Web and the Semantic Web are not two opposed visions for the web, moreover, they are so compatible that we can form a Semantic Web that enhances our Social Web experience, and the Semantic Web can be more meaningful with the input (the Collective Intelligence) of people on the Social Web.
He raises many interesting points such as:

• The difference between taxonomy, ontology, and folxonomy and how they relate. He especially points out that the goal of the Semantic Web isn't to create a taxonomy (a rigid categorization of the web), but an ontology (a means for different data from different sources to be understood, related, and used).


• He observes that tagging consists of a relationship between an entity (tagget), a URI (what is being tagged), and a phrase (the tag).


• There are issues that arise in tagging (having to do with each of those entities), especially when one wants to search through tags from different websites:
  
  
  • Tag spelling, whitespace, capitalization, and pluralization - which "different" tags should actually be considered the same?
  
  
  • What if there are different URI's for the same "thing": How do we detect this? What do we do about it?
  
  
  • Different kinds of taggers: What if we let machines tag things - should those be considered to tags by humans?
  
  
  • What happens when you change your mind: what if something is un-tagged?
  
  
  • Tag polarity: Should users be able to tag things as not-something?
  
  


• Another interesting concept presented is the idea that browsing can be viewed as browsing along the dimensions of a hypercube: location dimensions, tag dimensions, time dimensions, etc.


• Useful things can be done like letting tags be inherited: if a picture was taken in Paris, it was also taken in France - so we can add the tag "France" to anything tagged "Paris", right? (what if it's Paris, Texas... or some other Paris?).


• More fun topics like structured data and how with Google we are getting locked into the string-of-words style of searching.

In short, there is a lot of focus on the potential of improving tagging systems, and on the potential of the future of tagging and the web in general.

If you have an extra hour of nothing to do, or if you just want to put it on in the background while you're doing chores, it's definitely worth watching/listening to.
I'd also be glad to hear your thoughts on this.