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Re: [ontolog-forum] foundation ontology primitives

To: "'[ontolog-forum] '" <ontolog-forum@xxxxxxxxxxxxxxxx>
From: "Rich Cooper" <rich@xxxxxxxxxxxxxxxxxxxxxx>
Date: Sun, 7 Feb 2010 15:26:35 -0800
Message-id: <20100207232644.7153B138D0E@xxxxxxxxxxxxxxxxx>

Ferenc and John,


Thanks for the call for papers, though India is a little too far for comfortable travel given the present state of aircraft terminals.  I do agree that what pattern recognition technology has long called features, or invariant value sets, should be given more shrift in database technology, and finding such patterns might be eased by a proper ontology interpreter which has been fit specifically to the application.


As I understand the Tree of Porphyry, "Things" are the starting point at the root, and predefined questions can be asked at each node about predefined properties at that node to traverse from the top node (root: all things) to the actually specified thing ("Sally Jennings, teacher, daughter of Peter Jennings") at the terminal node when there are no more questions to ask.  


The porphyric approach is very different than starting with scoped sets of properties, as Ferenc suggested.  In the tree of Porphyry, John, you are starting with the set of all things (at the root) and asking set narrowing questions about specified properties and their values re members of the set until you reach an end point in the tree with only zero or one member thing - a hierarchy by construction, and predicates at each node are based on fluents, by construction.  See 7,209,923 for details.  (auto URL formation short circuited below to avoid spam cops):




That approach works great to distinguish among TYPES that have already been defined through FCA.  I am more interested in the huge clutter of textual information in databases that has not already been typed, parsed, fit into a framed meaning, and "interpreted".  I think that material is more complex than people realize, and can be very valuable.  


The FCA method is to cluster groups of things that all have the "same" properties, and to divide the members into subsets or elements based on fluents, or on constraints among the properties.  So anything visual (at least those visualized by humans) must have properties such as color, intensity, shape, shade, etc.  That is as true for a book as for a salamander.  But it doesn’t relate equilateral triangles to equiangular ones, unless the designer throws that in too.  So Ferenc’s suggestion has nothing DIRECTLY to do with TYPE construction per se, but with an inventory of exactly that set of properties that can be identified by the observer at each node in the porphyric node in current question.  


This is also a different approach from starting with Things, as in the poryphyral approach.  


Another way to look at it is to begin with a set of "primitive properties" (John, you will enjoy that word) which can be observed.  


Only after defining what those observables are, can you construct the tree of porphyry and specifically identify all the TYPES of things that can possibly have values for each of those properties.  To begin with, there are no questions for the things at the root node of the tree.  So you have to know what to measure about Thing existence, how to represent those measurements, and how to compare two such measurements.  


Starting with a set of defined properties limits the distinctions that can be made to manage the debate/discourse about how to represent Things.  THAT is a property, IMHO. 







Rich Cooper


Rich AT EnglishLogicKernel DOT com


-----Original Message-----
From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx [mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On Behalf Of John F. Sowa
Sent: Sunday, February 07, 2010 9:20 AM
To: [ontolog-forum]
Subject: Re: [ontolog-forum] foundation ontology primitives


Ferenc and Rich,


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.


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.


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:




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.


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:



    Semantic Networks


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



    Conceptual Graphs


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.


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




There have also been various conferences about the section of features

or properties for data mining.  See, for example, the following CFP.


John Sowa




     International Workshop on Feature Selection in Data Mining (FSDM10)


                   21st of June 2010, Hyderabad, India


                    (In conjunction with PAKDD 2010)





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.


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)


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:


- Dimensionality reduction


- Feature weighting


- Feature ranking


- Subset selection


- Feature extraction/construction


- Feature selection methodology


- Integration with data mining algorithms


- Pitfalls and learned lessons in feature selection studies


- Novel data structures


- Selection in small sample domains


- Data streams and time series


- Feature selection bias and variance


- Selection in extremely high-dimensional domains


- Real-world case studies and applications that highlight the role of

feature selection


- Emerging challenges


--- KEY DATES ---


Paper Submission deadline: March 19th, 2010


Author Notification: April 16th, 2010


Camera-ready: April 30th, 2010


Workshop: June 21th, 2010




Huan Liu, Hiroshi Motoda, Rudy Setiono, Zheng Zhao




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