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Jan
29
2010
NARA Archival Research Catalog – Compressed for Your Dowloading PleasurePosted by: ses in UncategorizedNARA has released the bulk contents of it’s Archival Research Catalog on data.gov, in ARC XML format. Unfortunately, when I say bulk contents, I mean bulk contents; the current distribution contains nearly 20GB of raw data; the Organizational authority file adds another 1.7GB. Since XML was deliberately designed for redundancy, XML encoded data sets are usually rather compressible. These data are no exception; bzip2 on the ARC data set achieves compression rations between 40:1 and 50:1. The 7-zip format does even better, but is not considered archival. The real credit for this work belongs to the good people at NARA and their contributing agencies (and viewers like you). Don’t forget to visit the original pages on data.gov to give comments and feedback on this data, and don’t forget to show off all the cool UIs you build on top of this. Mark Matienzo, (now a Yalie as of Monday), Richard Urban, GSLIS, University of Illinois,and Simon Spero, SILS, University of North Carolina have prepared a compressed distribution of these data sets, allowing much more efficient access to the data. This distribution is available from iBiblio at this location; http://www.ibiblio.org/fred2.0/NARA/ One interesting datum that emerged during the compression process is that the compressed size of Part1 is nearly twice as large as most of the other Parts, despite having the same raw size. There are several possible explanations for this:
If only had a time machine, I could do a poster and go to the iConference. Alas, all I have is the scarf and the Jelly babies, and I’ve eaten the Jelly babies. This release is dedicated to the memory of Prof. Brian Vickery (1918-2009), whose passing was announced on Thursday the 28th. Higgledy-Piggledy Polyhierarchically —– See the Readme file for a bit more details. It’s time to try and find a latex -> wordpress plugin that isn’t too smart for my own good. I’d like to be able to use some AMS macros, but the plugins seem to be trying to protect me from myself. Single equations are easy. \text{} works, so there’s some amsmath in there.
But getting displays to break paragraphs and getting align to work at all is a whole different kettle of ballgames. Those cynical chaps at the Speculative Grammarian make a mockery of all that is good and holy about Computational Linguistics and Information Retrieval. Recision and Precall – Accuracy Measures for the 21st Century [...]
Oct
04
2008
Using Social Software To Simplify Ontology Design: Model 1Posted by: ses in UncategorizedRemove the following relationship types:
Add the following relationship types:
Advatages
Disadvantages:
There’s all sorts of wisdom in academia. It seems to have started in business schools, but LIS has taken up the banner too. It’s only a matter of time before we start awarding MSWS degrees. It’s in handbooks of Measuring System Design. This isn’t on UNC’s subscription, so I couldn’t say what the SI unit of Wisdom is, but I’ll take a guess at the kilowit. Other brave souls have attempted to identify the definitions of the Wisdom hierarchy (and why it changes to the Information Hierarchy when Information Scientists talks about it, and the Knowledge Hierarchy when Knowledge Management professionals are involved). See The wisdom hierarchy: representations of the DIKW hierarchy for one effort.
We should have plenty of time afterwards for tea and cake. “Data organization.” “Human Information Organizing Behavior.” “Knowledge Organization Systems.” What would characterize a “Wisdom Organization System?” Self Archiving has failed. Stealth Archiving is the answer. An ordinary person spends there time getting out avoiding tense situations. A repo-rat spends his time getting in to tense situations.
TThere’s a critical difference between subject based knowledge organization schemes created to organize books and other documents, (e.g. LCC, DDC, and LCSH), which are defined in terms of the things which a work is about, and rigorous, class based ontologies that are based on the properties of the things themselves. Some relationships may be valid in terms of subjects, but may not apply directly to the things themselves. For example, a car wheels are part of cars, and cars are a type of vehicle. These relationships are always* true, and are examples of two different types of hierarchical relationships – partitive (is-part-of) (BTP) , and generic (is-a-kind-of) (BTG) Always-aboutness is the combination of all the different types of hierarchical relationship – this is the Broader Term (BT) relationship. In terms of aboutness, we can easily say that everything about car wheels is always about cars, and everything about cars is always about vehicles. We can also directly infer that everything about car wheels must always be about vehicles. In other words, the always-aboutness relationship must be transitive. Transitivity doesn’t hold when you mix different types of hierarchy – car wheels are not a kind of vehicle, but that has nothing to do with the transitivity of plain unqualified Broader Term. When thinking about systems for organizing information, it’s absolutely critical to remember the difference between the document, and the thing the document is about. Or in other words: |


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