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Gary Edwards

The NeuroCommons Project: Open RDF Ontologies for Scientific Reseach - 0 views

    The NeuroCommons project seeks to make all scientific research materials - research articles, annotations, data, physical materials - as available and as useable as they can be. This is done by fostering practices that render information in a form that promotes uniform access by computational agents - sometimes called "interoperability". Semantic Web practices based on RDF will enable knowledge sources to combine meaningfully, semantically precise queries that span multiple information sources.

    Working with the Creative Commons group that sponsors "Neurocommons", Microsoft has developed and released an open source "ontology" add-on for Microsoft Word. The add-on makes use of MSOffice XML panel, Open XML formats, and proprietary "Smart Tags". Microsoft is also making the source code for both the Ontology Add-in for Office Word 2007 and the Creative Commons Add-in for Office Word 2007 tool available under the Open Source Initiative (OSI)-approved Microsoft Public License (Ms-PL) at and,respectively.

    No doubt it will take some digging to figure out what is going on here. Microsoft WPF technologies include Smart Tags and LINQ. The Creative Commons "Neurocommons" ontology work is based on W3C RDF and SPARQL. How these opposing technologies interoperate with legacy MSOffice 2003 and 2007 desktops is an interesting question. One that may hold the answer to the larger problem of re-purposing MSOffice for the Open Web?

    We know Microsoft is re-purposing MSOffice for the MS Web. Perhaps this work with Creative Commons will help to open up the Microsoft desktop productivity environment to the Open Web? One can always hope :)

    Dr Dobbs has the Microsoft - Creative Commons announcement; Microsoft Releases Open Tools for Scientific Research ...... Joins Creative Commons in releasing the Ontology Add-in
Paul Merrell

The Strongest Link: Libraries and Linked Data - 2 views

  • Abstract Since 1999 the W3C has been working on a set of Semantic Web standards that have the potential to revolutionize web search. Also known as Linked Data, the Machine-Readable Web, the Web of Data, or Web 3.0, the Semantic Web relies on highly structured metadata that allow computers to understand the relationships between objects. Semantic web standards are complex, and difficult to conceptualize, but they offer solutions to many of the issues that plague libraries, including precise web search, authority control, classification, data portability, and disambiguation. This article will outline some of the benefits that linked data could have for libraries, will discuss some of the non-technical obstacles that we face in moving forward, and will finally offer suggestions for practical ways in which libraries can participate in the development of the semantic web.
    See also Wikipedia on Linked Data:
Gary Edwards

Wolfram Alpha is Coming -- and It Could be as Important as Google | Twine - 0 views

  • The first question was could (or even should) Wolfram Alpha be built using the Semantic Web in some manner, rather than (or as well as) the Mathematica engine it is currently built on. Is anything missed by not building it with Semantic Web's languages (RDF, OWL, Sparql, etc.)? The answer is that there is no reason that one MUST use the Semantic Web stack to build something like Wolfram Alpha. In fact, in my opinion it would be far too difficult to try to explicitly represent everything Wolfram Alpha knows and can compute using OWL ontologies. It is too wide a range of human knowledge and giant OWL ontologies are just too difficult to build and curate.
  • However for the internal knowledge representation and reasoning that takes places in the system, it appears Wolfram has found a pragmatic and efficient representation of his own, and I don't think he needs the Semantic Web at that level. It seems to be doing just fine without it. Wolfram Alpha is built on hand-curated knowledge and expertise. Wolfram and his team have somehow figured out a way to make that practical where all others who have tried this have failed to achieve their goals. The task is gargantuan -- there is just so much diverse knowledge in the world. Representing even a small segment of it formally turns out to be extremely difficult and time-consuming.
  • It has generally not been considered feasible for any one group to hand-curate all knowledge about every subject. This is why the Semantic Web was invented -- by enabling everyone to curate their own knowledge about their own documents and topics in parallel, in principle at least, more knowledge could be represented and shared in less time by more people -- in an interoperable manner. At least that is the vision of the Semantic Web.
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  • Where Google is a system for FINDING things that we as a civilization collectively publish, Wolfram Alpha is for ANSWERING questions about what we as a civilization collectively know. It's the next step in the distribution of knowledge and intelligence around the world -- a new leap in the intelligence of our collective "Global Brain." And like any big next-step, Wolfram Alpha works in a new way -- it computes answers instead of just looking them up.
    A Computational Knowledge Engine for the Web In a nutshell, Wolfram and his team have built what he calls a "computational knowledge engine" for the Web. OK, so what does that really mean? Basically it means that you can ask it factual questions and it computes answers for you. It doesn't simply return documents that (might) contain the answers, like Google does, and it isn't just a giant database of knowledge, like the Wikipedia. It doesn't simply parse natural language and then use that to retrieve documents, like Powerset, for example. Instead, Wolfram Alpha actually computes the answers to a wide range of questions -- like questions that have factual answers such as "What country is Timbuktu in?" or "How many protons are in a hydrogen atom?" or "What is the average rainfall in Seattle this month?," "What is the 300th digit of Pi?," "where is the ISS?" or "When was GOOG worth more than $300?" Think about that for a minute. It computes the answers. Wolfram Alpha doesn't simply contain huge amounts of manually entered pairs of questions and answers, nor does it search for answers in a database of facts. Instead, it understands and then computes answers to certain kinds of questions.
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