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.

 \text{\sf Unicorns} \sqsubseteq \text{\sf Horses}\\ \text{Horses} \sqcap \text{Regicides} \not\equiv \bot

But getting displays to break paragraphs and  getting align to work at all is a whole  different kettle of ballgames.

Comments No Comments »

I’d like to thank the academy.

Comments No Comments »

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

[...]

So, rather than trying to dumb things down and arrive at such a single “accuracy” number, we propose instead to dumb things up—constructing measures that focus on the real needs of a measurable theory, including the meta-system/contextual-matrix in which it is embedded (including, explicitly and for the first time, the researchers and grad-students on the research team).

These two new measures are called recision and precall.

Recision is a measure of the amount of data that must be ignored (or surreptitiously dumped in the river with a new pair of cement shoes) in order to get publishable results. If 10% of your data must be “lost” in order to get good results that support your pre-computed conclusions, then your theory and your research team have a respectable recision score of 10%. If only 10% of your data is useable, then your recision score is a dismal4 90%.

Precall is a measure of your team’s ability to quickly and correctly predict how well your algorithm or system will perform on a new data set that you can briefly review. Correctly predicting “This will give good results.” or “This is gonna suck!” 90% of the time translates directly into a precall score of 90%. Good precall (especially during live demos) can save a project when results are poorer than they should be. The ability to look at some data and accurately predict and, more importantly, explain why such data will give poor results shows a deep understanding of the problem space.5 Even when performance is decent, though, prefacing each data run with “We have no idea how this will turn out!” makes your team look lucky, at best, or, at worst, foolish.

Comments No Comments »

Remove the following relationship  types:

  1. Broader Term (BT)
  2. Narrower Term (NT)
  3. Related Term (RT)
  4. Use For (UF)
  5. Use (USE)

Add the following relationship types:

  1. It’s Complicated

Advatages

  1. It’s Simple

Disadvantages:

  1. It’s Complicated

Comments No Comments »

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.

After revisiting Ackoff’s original articulation of the hierarchy, definitions of data, information, knowledge and wisdom as articulated in recent textbooks in information systems and knowledge management are reviewed and assessed, in pursuit of a consensus on definitions and transformation processes

We should have plenty of time afterwards for tea and cake.
The sad thing is, I think there is an operationalisable definition available using Shannon’s Information Theory as a starting point, but the result isn’t that exciting.   If you treat Knowledge as the ability to reason backwards from a desired end state from the inferred current state (based on perceived data), then executing that plan, updating current state as more information is learned;  then Wisdom can be the ability to infer forwards to the fuller set of consequences of those actions.  Since the only real law in this area is the law of unintended consequences, this isn’t too useful a definition. 

Comments No Comments »

“Data organization.” “Human Information  Organizing Behavior.” “Knowledge Organization Systems.”  What would characterize a “Wisdom Organization  System?”   

Comments 4 Comments »

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.

Never broke into an office. Never wire-tapped an office. Kid. I never broke into a laptop. I shall not corrupt a file northe contents thereof. Nor through inaction let that file or the digital contents thereof come to harm. That’s what I call the repo code kid. Don’t forget it etch it in your brain. Not many people got a code to live by anymore.

(nodding to Wisconsin)

Comments No Comments »

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:

This is your Unicorn. This is your Unicorn on SKOS. 

Comments 2 Comments »

  

What is cyberinfrastructure? A word.  And what is in that word “cyberinfrastructure? Air. A trim reckoning!   Who has it? He with grants a’Wednesday. Can he built it? No. Can he use it? No. T’isn’t sensible then? Yea, to the tenured.  But will it not work for the working? No. Why? Implementation will not suffer it. Therefore I’ll none of it.  Cyberinfrastructure is a mere scutcheon – and so ends my catechism.

 

When I coined the term cybrarian back in 1991, all I was trying to do was come up with a cute name for a Usenix  birds of a feather session for people working on sites protocols and software for finding and sharing information on the net (We’d need a bigger room now.) 

It wasn’t creating a new reality – the word just sounded pretty, and would be easy to filk with. 

 Cyberinfrastructure is different.

 It doesn’t scan well.  

Simon 

Comments 1 Comment »

This is the latest version of the Doorbell -> Mammal graph; it shows the direct and indirect broader terms of doorbells in LCSH.

This incarnation of the graphic adds one new piece of visual information that seems to be very very suggestive. Dashed lines are used to indicate broader term references that have never been validated since BT and NT references were automatically generated from the old SA (See Also) links  in 1988.  Click to download the PDF version

Dashed doorbell graph

Comments 2 Comments »