IRC log of dig on 2010-04-06

Timestamps are in UTC.

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I'm working with the modified libraries jim used for his project; There are functions for doing things with client certs but none of it seems to work. I've determined that the reason is that the function is checking for $_SERVER['SSL_CLIENT_CERT'] which doesn't exist and instead I'm getting back lots of SSL_CLIENT_FOO in $_SERVER with the certificate information. Might anyone know what I should do in this case?
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16:45:47 [oshani]
timbl, here's the linked data book: http://patterns.dataincubator.org/book/
16:51:36 [timbl]
Thanks
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18:40:15 [oshani]
lkagal, we were just wondering whether we are having a DIG meeting with Jie today?
18:40:42 [lkagal]
Oshani, yes, with Jie and K. Haven't they sent out a notice to the mailing list ?
18:41:00 [oshani]
lkagal, no they haven't. So are they both dialling in?
18:41:36 [lkagal]
I'm not sure.
18:42:45 [oshani]
lkagal. I asked because they are both not at MIT right now. Perhaps they'll come in around 4. We'll dial in anyways.
18:42:57 [lkagal]
Ok, thanks
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DIG meeting starting now in G531
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20:08:12 [oshani]
Semantic Information Theory by Jie Bao
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-------------------------------------------
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Work in progress at BBN
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Familiarity with Information Theory, Kolmogotov Complexity and Semantic Web
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Information is about uncertainity of things, but we are interested in the semantic value of the information
20:10:57 [oshani]
Will explain about the classical information theory and why it is not enough for our semantic web
20:11:05 [oshani]
Explains about the CTA project
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... Network Science Collaborative Technology Alliance
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...does lot of interdisciplinary work
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...this work is carried out as a sub project of that CTA project
20:12:27 [oshani]
Shannon's Information Model
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...classical paper in the 1948
20:13:15 [oshani]
...this theory has not changed in the last 60 years
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...communication system includes different parts: source, transmitter, receiver, destination, and noise source
20:14:28 [oshani]
Weaver's View
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...Shannon's model only deals with the technical specification of info theory
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...that is Level A
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..Level B is Semantic level that we are interested in studying
20:15:44 [oshani]
...the critical problem is understand the effectiveness of the messgae
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...that is Level C
20:17:00 [timbl]
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20:17:04 [oshani]
...extends Shanon's model with "Semantic Transmitter", "Semantic Receiver" and "Semantic Noise Source"
20:17:36 [oshani]
Mike: Question about the logical placement of the semantic noise
20:17:59 [oshani]
Jie: Yes, our model addresses that
20:18:09 [oshani]
...explains about the model
20:19:21 [oshani]
Mike: How can you define the intended msg?
20:19:31 [oshani]
Jie: We will not discuss this today
20:19:45 [oshani]
Jie: Our model includes a Social Machie
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s/Machie/Machine
20:20:07 [oshani]
Joe: What do those arrows represent?
20:20:40 [oshani]
...also the utlity of the social machine
20:20:58 [oshani]
timbl: Social Machine is something like an online democratic system
20:22:17 [oshani]
...We had the concept of intention, but we did not quantify it
20:23:00 [oshani]
...content/type in a http transaction conveys the intention at some level
20:24:20 [oshani]
...it's very hard to figure out the intended msg even in a f2f communication
20:24:44 [oshani]
baojie: Yes, it is hard. This problem is in the utility level
20:25:04 [oshani]
Mike: <missed the question>
20:25:44 [oshani]
baojie: our model is analogous to communication between TMS
20:25:55 [oshani]
.. (Turing Machines)
20:26:31 [oshani]
...our intention is not to reproduce the source
20:26:49 [oshani]
...but to infer something from the communication
20:26:57 [oshani]
Existing Measurements
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...a signal has a PD
20:27:16 [kkw]
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20:28:03 [oshani]
...explains the formula that can be used to measure the uncertainty of some random variable X
20:28:39 [oshani]
...the source has a maximum entropy which is represented by the maxima in the graph
20:29:10 [oshani]
Kolmogorov Complexity
20:29:29 [oshani]
...in Shanon's entropy information is defined as a PD
20:29:38 [oshani]
...you have to know the distribution
20:30:43 [oshani]
...how can we relate that to a case such as the existence of a human being (?)
20:31:59 [oshani]
Mike: There are many TMs, so this cannot be generalized to that equaltion
20:32:43 [oshani]
baojie gives some background to entropy and K-complexity
20:34:45 [oshani]
Shannon's information theory does not work in semantic data
20:34:55 [oshani]
...gives an example using subclasses
20:35:59 [oshani]
...it does consider inference
20:36:14 [oshani]
Also, it does not support K-complexity
20:38:06 [oshani]
Few questions to ask - slide 20
20:39:19 [oshani]
The model assumes no noise, and only machine readable knowledge (no utility or human intervention)
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20:41:52 [oshani]
Mike: Suppose the KB has all the basic laws of geometry, can we assume that this theory can derive Pythogaras thm?
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20:43:08 [oshani]
baojie explains some heuristics for "Knowledge Entropy" (slide 23)
20:44:27 [oshani]
Knowledge Entropy - A formal definition
20:46:57 [oshani]
...gives an example of infinite Inference Closure based on person-father relationship
20:47:47 [oshani]
...blank nodes in RDF also gives this kind of infinite Inference Closure
20:49:27 [oshani]
Heuristics for Knowledge Entropy:
20:50:14 [oshani]
...less relationships means for information (because new information cannot be inferred)
20:53:12 [oshani]
Joe: linking to a central ontology makes the information useful
20:54:21 [oshani]
Example in slide 28: solid edges = axioms, dotted edges = inferences
20:55:52 [oshani]
Shared knowledge (slides 29-30)
20:58:54 [oshani]
slide 32: Joe: noise in the sense of lost information as opposed to extra useless information?
20:59:29 [oshani]
Mike: transmitting wise, you would transmit the minimum information
20:59:44 [oshani]
...get rid of the redundant information
21:00:07 [oshani]
baojie: in communication theory there is coding to prevent redundant information
21:00:35 [oshani]
... but we are doing inference, and not transmission of information
21:01:55 [oshani]
... Channel encoding theory addresses these concerns
21:02:42 [oshani]
timbl: (slide 32) is moving X from K better defined?
21:05:25 [oshani]
... anonymizing things is an example for this
21:05:49 [oshani]
Ian gives another example from some music dataset
21:06:19 [jambo]
(audioscrobbler)
21:08:55 [oshani]
timbl: Would K-complexity of the IC solve the problem?
21:09:43 [oshani]
baojie: IC description states that the k-complexity of the IC cannot be larger than the original information
21:09:57 [kkw]
apologies...I need to sign off...DHS status can wait til I arrive tomorrow.
21:10:08 [oshani]
okay kkw bye!
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21:11:21 [oshani]
Mike: (missed the question - anybody want to fill in?)
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22:26:00 [sandro]
http://assembly.state.ny.us/leg/?default_fld=&bn=A10335&Summary=Y&Memo=Y&Text=Y
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