[ontolog-forum] FW: FW: CfP 11/16/2015: Knowledge-Based AI Track at 2016

["Obrst, Leo J." <lobrst@xxxxxxxxx>]

Thomas,

 

[I originally posted this just to you, but we have agreed to share our exchange and invite others into the discussion.]

 

As you undoubtedly know, recently there’s has been the emergence of so-called “distributional semantics” these days in computational linguistics/NLP. This is based on corpus linguistics, i.e., large-scale statistical, but light-weight “knowledge-based” or formal linguistic/semantic methods.

 

Distributional semantics:  words as “meaning” the contexts/collocations they can occur in, what I consider kind of Witgenstein 2 (Investigations, not Tractatus) in nature.

 

Some folks are trying to combine distributional semantics with more formal compositional semantics, the latter of which has not focused primarily on lexical semantics, but rather on the composition of words into higher forms and their semantic  interpretations, i.e., going back to Montague in the late 1960s.  E.g.,  see [1, 2]. Also, for a good overview of lexical theories, see [3]. For a new type-based approach, see [4].

 

However, there are potentially some useful emerging approaches in ontology research, i.e., quality (or value) spaces, and so-called semantic reference spaces/ranges, especially [5-6]. We are using this in our current clinical care healthcare ontology research (forthcoming), which can combine quantitative and qualitative quality value spaces, so that, e.g., nominal qualities (“named” qualities; think of “low/medium/high X”) can be mapped into a quantitative range, though imprecisely, given that you have some ordering on the regions. I am myself thinking of something similar to this for so-called “semantic fields”, i.e., that one can begin to think of these spaces and their points/regions as “drifting” over time.

 

Thanks,

Leo

 

[1] Lewis, M., & Steedman, M. 2013. Combining Distributional and Logical Semantics. Transactions of the Association for Computational Linguistics, 1, 179-192. https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/93.

[2] Baroni, M., Bernardi, R., & Zamparelli, R. 2014. Frege in space: A program of compositional distributional semantics. Linguistic Issues in Language Technology, 9. http://csli-lilt.stanford.edu/ojs/index.php/LiLT/article/download/6/5.

[3] Geeraerts, Dirk. 2009. Theories of Lexical Semantics. Oxford University Press.

[4] Asher, Nicholas. 2011.  Lexical Meaning in Context: A Web of Words. Cambridge: Cambridge University Press. 2011.

[5] Probst, F. 2007. Semantic Reference Systems for Observations and Measurements. PhD dissertation, U. Muenster, Germany. http://ifgi.uni-muenster.de/~probsfl/publications/PROBST-Thesis-SemanticReferenceSystemsForObservationsAndMeasurements.pdf.

[6] Probst, F. 2008.  Observations, measurements and semantic reference spaces. Applied Ontology 3 (2008) 63-89.

 

 

 

From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx [mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On Behalf Of Thomas Johnston
Sent: Tuesday, September 15, 2015 11:42 PM
To: [ontolog-forum] <ontolog-forum@xxxxxxxxxxxxxxxx>
Subject: Re: [ontolog-forum] FW: CfP 11/16/2015: Knowledge-Based AI Track at 2016 FLAIRS

 

Leo,

 

The description of knowledge-based vs "statistics"-based AI is an entry point into so much that I am interested in. Perhaps the distinction will eventually cease to be a distinction between the association of ontologies with the former but not with the latter. Perhaps the distinction will eventually become one between software systems which are given human-developed ontologies, and software systems which abstract their own ontologies from the patterns they discover by naming those patterns and then developing additional patterns by playing with set-theoretic quasi-random combinations of those named patterns.

 

And the way we play these set-theoretic games in our heads, I have suspected for a long time, is by means of background processes in which Venn-diagram-like representations of those labelled patterns are what is actually manipulated, at close to the neural level. (This is a bit like Gardenfors, but also quite a bit unlike him.)

 

I suspect this is far too brief, and also far too off the beaten academic path, to be more than vaguely suggestive, if even that. The interests that it hints at are (i) my interest in lexical semantics, vs. what I see as a one-sided concentration on the semantics of statements on the part of philosophers especially; and (ii) my interest in diachronic semantics, i.e. in how the semantic web that is embodied in the neurochemical web of our brains evolves over time, as it obviously does.

 

I'm tempted to just erase this. But I feel among friends here, and so I've decided that it doesn't matter if this sounds foolish. And if there are others here to whom this is not completely non-sensical, I'd enjoy hearing from you, and especially hearing about the "core bibliographies" relevant to this stuff that you are currently working with.

 

Regards,

 

Tom 

 

 

On Tuesday, September 15, 2015 12:27 AM, "Obrst, Leo J." <lobrst@xxxxxxxxx> wrote:

 

 

FYI.

 

-----Original Message-----

From: Christian Hempelmann [mailto:C.Hempelmann@xxxxxxxxx]

 

FLAIRS Knowledge-Based AI Track

 

Special Track at FLAIRS-29, Key Largo, Florida USA

 

In cooperation with the Association for the Advancement of Artificial

Intelligence

 

Key Largo, Florida, USA

May 16 - 18, 2016

 

Paper submission deadline: November 16, 2015.

Notifications: January 18, 2016.

 

Camera-ready version due: February 22, 2016.

 

All accepted papers will be published as FLAIRS proceedings by the AAAI.

 

Call for Papers

 

What is Knowledge-Based AI?

After an early dominance in AI, especially in NLP, approaches based on

engineered knowledge-resources such as rule bases and ontologies modeling

(part of) the world, the statistical winter set in in the 1980s. Fueled by

increased computing power, statistics-based replacements for modeling the

world, including machine-learning and neural networks, lead to early

successes before they hit their ceiling and resulted in algorithmic arms

races. To get AI out of these trenches and back into mobile warfare,

knowledge-based methods have not only been being paid  lipservice to in

the many "semantic" revolutions, but actual applications have been built,

often with complementary methodologies that paired statistical and

knowledge-based solutions.

The scope of the track includes research, proof-of-concept and industry

applications in the area of knowledge-based AI, i.e. systems whose

functionality is informed by computational knowledge resources

(ontologies, lexicons, semantic networks and/or knowledge bases). While

the knowledge-based AI is often juxtaposed to the statistics-based AI, we

see the contrast as unnecessarily exclusionary, in that systems combining

the intuitive directness of knowledge representation with the efficiency

of statistics-based computation have distinct advantages.

 

What is the GOAL of the track?

To showcase recent knowledge-based theories, methodologies, and

applications in AI and to foster new approaches of this kind, also those

paired with statistical and machine-learning approaches.

 

Who might be interested?

The scope of the track includes research, proof-of-concept and industry

applications in the area of knowledge-based AI, i.e. systems whose

functionality is informed by computational knowledge resources

(ontologies, lexicons, semantic networks and/or knowledge bases). While

the knowledge-based AI is often juxtaposed to the statistics-based AI, we

see the contrast as unnecessarily exclusionary, in that systems combining

the intuitive directness of knowledge representation with the efficiency

of statistical approximation have distinct advantages.

What kind of studies will be of interest?

Papers and contributions are encouraged for any work relating to

Knowledge-Based AI. Topics of interest may include (but are in no way

limited to):

              € ontologies

              € spreading activation networks

              € lexicon acquisition

              € knowledge integration

              € applications in knowledge-based AI

              € hybrid probabilistic/machine-learning & knowledge-based systems

Note: We invite original papers (i.e. work not previously submitted, in

submission, or to be submitted to another conference during the reviewing

process).

 

Submission Guidelines

Interested authors should format their papers according to AAAI formatting

guidelines. The papers should be original work (i.e., not submitted, in

submission, or submitted to another conference while in review). Papers

should not exceed 6 pages (4 pages for a poster) and are due by November

16, 2015. For FLAIRS-29, the 2016 conference, the reviewing is a double

blind process. Fake author names and affiliations must be used on

submitted papers to provide double-blind reviewing. Papers must be

submittcoued as PDF through the EasyChair conference system, which can be

accessed through the main conference web site

(http://www.flairs-29.info/). Note: do not use a fake name for your

EasyChair login - your EasyChair account information is hidden from

reviewers. Authors should indicate the [your track name] special track for

submissions. The proceedings of FLAIRS will be published by the AAAI.

Authors of accepted papers will be required to sign a form transferring

copyright of their contribution to AAAI. FLAIRS requires that there be at

least one full author registration per paper.

Please, check the website http://www.flairs-29.info/ for further

information.

 

Conference Proceedings

Papers will be refereed and all accepted papers will appear in the

conference proceedings, which will be published by AAAI Press.

 

Organizing Committee

              € Christian F. Hempelmann, Texas A&M University-Commerce,

              € Gavin Matthews, NTENT.com,

              € Max Petrenko, NTENT.com,

 

Program Committee

              € Christian F. Hempelmann, Texas A&M University-Commerce,

              € Elena Kozerenko, Russian Academy of Sciences,

              € Gavin Matthews, NTENT.com,

              € Leo Obrst, MITRE,

              € Max Petrenko, NTENT.com,

              € Victor Raskin, Purdue University,

              € Julia M. Taylor, Purdue University,

              € Tony Veale, University College Dublin,

              € Yorick Wilks, University of Sheffield & IHMC, Florida,

              € Michael Witbrock, VP for Research, Cycorp.

 

Further Information

Questions regarding the Knowledge-Based AI Special Track should be

addressed to the track co-chairs:

              € Christian F. Hempelmann, Texas A&M University-Commerce,

ontology@xxxxxxxxx

              € Gavin Matthews, NTENT.com, gmatthews@xxxxxxxxx

              € Max Petrenko, NTENT.com, mpetrenko@xxxxxxxxx

 

Questions regarding Special Tracks should be addressed to Zdravko Markov,

markovz@xxxxxxxx

Conference Chair: William (Bill) Eberle, Tennessee Technological

University, USA (WEberle@xxxxxxxxxx)

Program Co-Chairs: Zdravko Markov, Central Connecticut State University,

USA (markovz@xxxxxxxx)

Ingrid Russell, University of Hartford, USA (irussell@xxxxxxxxxxxx)

Special Tracks Coordinator: Vasile Rus, The University of Memphis, USA

(vrus@xxxxxxxxxxx)

 

Conference Web Sites

Paper submission site: follow the link for submissions at

http://www.flairs-29.info/

FLAIRS-29 conference web page: http://www.flairs-29.info/

Florida AI Research Society (FLAIRS): http://www.flairs.com

<http://www.flairs.com/>

 

Christian F. Hempelmann, PhD | Assistant Professor of Computational

Linguistics

Department of Literature and Languages

c.hempelmann@xxxxxxxxx

Texas A&M University-Commerce

P.O. Box 3011 | Commerce, TX 75429-3011

Tel. 903.468.5291 | Fax: 903.886.5980 | www.tamuc.edu

The Texas A&M University System

 

_______________________________________________

Dr. Leo Obrst                The MITRE Corporation, Information Semantics

lobrst@xxxxxxxxx        Cognitive Science & Artificial Intelligence, CCG

Voice: 703-983-6770  7515 Colshire Drive, M/S H317

Fax: 703-983-1379      McLean, VA 22102-7508, USA

 

 

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