Ferenc and Rich, (01)
FK> The current ontologies focus on objects only, properties are not
> structured into repositories and relations are now defined in a
> funny idiosyncratic way that does not allow integration of ontologies.
> The contained in, or partof relation, etc. too are very trivial and
> uninteresting from a NL translation point of view. (02)
I agree that much richer approaches are needed. Properties are
important *monadic* predicates. The ContainedIn and PartOf are just
two among the many, many important dyadic predicates. The thematic
roles or case relations are other important linguistic relations. (03)
But just as important, if not more so, are the structural operators
for combining larger, prefabricated patterns. The Framenet group
at Berkeley has been working on that: (04)
http://framenet.icsi.berkeley.edu/ (05)
Chuck Fillmore, who was one of the first to emphasize the importance
of case relations in his famous paper "The Case for Case", was also
the prime mover in starting the Framenet project. (06)
The approach that I use with conceptual graphs was inspired by some
of the work on case relations combined with the semantic networks
of AI. Following is a review article in the Encyclopedia of AI: (07)
http://www.jfsowa.com/pubs/semnet.htm
Semantic Networks (08)
For conceptual graphs, I defined six "canonical formation rules" for
combining graph structures. My original version of 1976 contained
four rules, the 1984 version contained a different selection of four,
and the latest version (since 2000) has six rules, organizes as three
pairs of generalization/specialization rules. See Section 3 of (09)
http://www.jfsowa.com/cg/cg_hbook.pdf
Conceptual Graphs (010)
RC> Your hint about structuring properties into repositories is an
> interesting one. Other than the very simple clustering approach
> of FCA (formal concept analysis), I don’t know of approaches that
> START with properties, other than my own stuff
> http://www.englishlogickernel.com
> and partition properties into groups. (011)
Actually, Aristotle thought of it first. The Tree of Porphyry from
the 3rd century AD (see Figure 1 in the semnet.htm article) showed
how categories and properties are interrelated. Ramon Lull in the
13th century had rotating disks for combining properties, and
Leibniz was inspired by Lull to develop the first computational
approach to developing a lattice of concept types. For a brief
summary of both Leibniz's Universal Characteristic and FCA, see (012)
http://www.jfsowa.com/logic/math.htm#Lattice (013)
There have also been various conferences about the section of features
or properties for data mining. See, for example, the following CFP. (014)
John Sowa (015)
============================================================================= (016)
International Workshop on Feature Selection in Data Mining (FSDM10) (017)
21st of June 2010, Hyderabad, India (018)
(In conjunction with PAKDD 2010) (019)
http://featureselection.asu.edu/fsdm10 (020)
Knowledge discovery and data mining (KDD) is a multidisciplinary effort
to mine gold nuggets of knowledge from data. The increasingly large data
sets from many application domains have posed unprecedented challenges
to KDD; in the meantime, new types of data are evolving such as social
media, text, and microarray data, to name a few. Researchers and
practitioners in multiple disciplines and various IT sectors confront
similar issues in feature selection, and there is a pressing need for
continued exchange and discussion of challenges and ideas, exploring new
methodologies and innovative approaches to generate breakthroughs. (021)
Feature selection is effective in data preprocessing and reduction that
is an essential step in successful data mining applications. Feature
selection has been a research topic with practical significance in many
areas such as statistics, pattern recognition, machine learning, and
data mining (including Web, text, image, and microarrays). The
objectives of feature selection include: building simpler and more
comprehensible models, improving data mining performance, and helping
prepare, clean, and understand data. Workshop on Feature Selection in
Data Mining (FSDM2010) aims to further the cross-discipline,
collaborative effort in variable and feature selection research.
FSDM2010 will be held at the 14th Pacific-Asia Conference on Knowledge
Discovery and Data Mining (PAKDD 2010) (022)
The workshop invites all papers related to feature selection, and
especially welcomes contributions that highlight emerging feature
selection challenges in data mining. Possible paper topics include, but
are not limited to: (023)
- Dimensionality reduction (024)
- Feature weighting (025)
- Feature ranking (026)
- Subset selection (027)
- Feature extraction/construction (028)
- Feature selection methodology (029)
- Integration with data mining algorithms (030)
- Pitfalls and learned lessons in feature selection studies (031)
- Novel data structures (032)
- Selection in small sample domains (033)
- Data streams and time series (034)
- Feature selection bias and variance (035)
- Selection in extremely high-dimensional domains (036)
- Real-world case studies and applications that highlight the role of
feature selection (037)
- Emerging challenges (038)
--- KEY DATES --- (039)
Paper Submission deadline: March 19th, 2010 (040)
Author Notification: April 16th, 2010 (041)
Camera-ready: April 30th, 2010 (042)
Workshop: June 21th, 2010 (043)
--- ORGANIZATION --- (044)
Huan Liu, Hiroshi Motoda, Rudy Setiono, Zheng Zhao (045)
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