=Paper=
{{Paper
|id=Vol-527/paper-9
|storemode=property
|title=Fuzzy Taxonomies for Creative Knowledge Discovery
|pdfUrl=https://ceur-ws.org/Vol-527/pospaper2.pdf
|volume=Vol-527
|dblpUrl=https://dblp.org/rec/conf/semweb/MartinZM09
}}
==Fuzzy Taxonomies for Creative Knowledge Discovery==
https://ceur-ws.org/Vol-527/pospaper2.pdf
Fuzzy Taxonomies for Creative Knowledge Discovery
Trevor Martin1,2, Zheng Siyao1,3 and Andrei Majidian2
1
AI Group, University of Bristol, BS8 1TR UK
2
Intelligent Systems Lab, BT, Adastral Park, Ipswich IP5 3RE, UK
3
School of Computer Science and Engineering, BeiHang University, Beijing, China
Trevor. Martin@bristol.ac.uk, zhengsyao@gmail.com, Andrei .Majidian@bt.com
Abstract. A systematic form of creative knowledge discovery is outlined,
requiring taxonomies to generalise knowledge structures and mappings between
taxonomies to find parallels between knowledge structures from different
domains. These share many of the features needed to handle uncertainty in the
semantic web, and results will be relevant to the URSW community.
Keywords: fuzzy taxonomy, creative knowledge discovery, fuzzy association
rules, uncertainty in semantic web
1 Introduction
Almost by definition, creative knowledge discovery is difficult to automate and
harder to assess objectively. By creative knowledge discovery, we mean finding
previously unknown links between concepts or small “chunks” of knowledge in such
a way that useful additional knowledge is generated. It can be distinguished from
“standard” knowledge discovery by defining the latter as the search for explanatory
and/or predictive patterns and rules in large volume data within a specific domain. For
example, a knowledge discovery process might examine an ISP(internet service
provider)’s customer database and determine that people who have a high monthly
spend and who send more than three emails to the support centre in a single month are
very likely to change to a different provider in the following month. Such knowledge
is implicit within the data but is useful in predicting and understanding behaviour.
By contrast, creative knowledge discovery is more concerned with “thinking the
unthought-of” and looking for new links, new perspectives, etc. Such links are often
found by drawing parallels between different domains and looking to see how well
those parallels hold - for example, compare the ISP example mentioned above to a
hotel chain finding that regular guests who report dissatisfaction with two or more
stays often cease to be regular guests unless they are tempted back by special
treatment (such as complimentary room upgrades). This is a simple illustration of
similar problems (losing customers) in different domains. A solution in one domain
(complimentary upgrades) could inspire a solution in the second (e.g. a higher
download allowance at the same price). Of course, such analogies may break down
when probed too far but they often provide the creative insight necessary to spark a
82 T. Martin, Z. Siyao and A. Majidian
new solution through a new way of looking at a problem. In many cases, this
inspiration is often referred to as “serendipity”, or accidental discovery.
It is possible that many serendipitous discoveries are subsequently rationalised as
the outcome of rigorous application of the scientific process. The traditional view of
the scientist is as a generator and tester of hypotheses - often this is presented as an
almost mechanical process and systems such as King’s robot scientist [1] take this to
an extreme, using an inductive logic programming approach to systematically
generate and test hypotheses in a laboratory.
In this paper we outline a project to automate creative knowledge discovery. The
aim is to find parallels between different knowledge repositories - in this case,
semantically annotated networks of documents or process models - in the hope of
transferring useful links from one network to another. In the case of process models
from different domains, the aim is to identify possible improvements in one process if
its analogue in the other domain is more efficient in some way.
This work shares many of the problems faced by research into uncertainty in the
semantic web - the mapping between repositories is very similar to a mapping
between ontologies, and the creation of knowledge networks encounters several issues
that are well-known from the semantic web, such as the need for imprecise concepts,
integration of sources that represent entities and classes at different levels of detail
etc. The work is at an early stage, and this paper briefly outlines (i) a possible
approach to automating creativity which relies on the use of fuzzy taxonomies and (ii)
preliminary work on automatic extraction of taxonomies from data; this requires a
representation of uncertainty similar to that needed for the semantic web.
2 A Method for Creative Knowledge Discovery
Can creativity - in this sense of suddenly making novel connections - be
automated? Koestler [2] summarised this view of creativity as follows:
“The creative act is not an act of creation in the sense of the Old Testament. It does not create
something out of nothing: it uncovers, selects, re-shuffles, combines, synthesizes already
existing facts, idea, faculties, skills. The more familiar the parts, the more striking the new
whole”
Table 1 - attributes of two music players (taken from [4])
Conventional tape recorder Sony Walkman
big small
clumsy neat
records does not record
plays back plays back
uses magnetic tape uses magnetic tape
tape is on reels tape is in cassette
speakers in cabinet speakers in headphones
mains electricity battery
Sherwood [3] proposes a systematic approach, in which a situation or artefact is
represented as an object with multiple attributes, and the consequences of changing
Fuzzy Taxonomies for Creative Knowledge Discovery 83
attributes, removing constraints, etc are progressively explored. For example, given
an old style reel-to-reel tape recorder as starting point, Sherwood’s approach is to list
some of its essential attributes, substitute plausible alternatives for a number of these
attributes, and evaluate the resulting conceptual design or solution. Table 1 shows
how this could have led to the Sony Walkman in the late 70s [4]. Again, with the
benefit of hindsight the reader should be able to see that by changing magnetic tape to
a hard disk and considering the way music is purchased and distributed, the same
method could (retrospectively, at least) lead one to invent the iPod. Of course, having
the vision to choose new attributes and the knowledge and foresight to evaluate the
result is the hard part - and the creative steps are usually only obvious with hindsight.
This systematic approach is ideally suited to handling data which is held in an
object-attribute-value format, provided we have a means of changing/generalising
attribute values. We intend to use taxonomies for this purpose, so that “sensible”
changes can be made (e.g. mains, battery are both possible values for a power
attribute). Representing an object O as a set of attribute-value pairs
{( ai , v i ) attribute ai of object O has value v i } we generate a new “design” O* = {( ai , T (v i ))}
by changing one or more values using Ti, a non-deterministic transformation of a
value to another value from the same taxonomy. Given sufficient time, this would
! simply enumerate all possible combinations of attribute values.
! We can reduce the
search space by looking at the solution to an analogous problem in a different domain.
Our aim is to adapt previously developed tools for taxonomy matching [5] so that
analogies can be found; the next section briefly outlines a way to extract taxonomic
structure when it is not explicitly available.
3 Extracting Embedded Soft Taxonomies
An ontology essentially consists of a taxonomy of concepts, one or more relations
between concepts, and rules which impose constraints and allow data transformation.
The idea of an ontology is central to the semantic web [6], although there can be a
very high cost in creation and maintenance. This is reflected in practical experience -
it is rare to find web-based data that is fully marked up with RDF or OWL metadata.
It is far more common to encounter data that is stored in a relational database or an
equivalent XML-tagged format. Such data often contains implicit taxonomies - a
relational table may flatten hierarchical data into one or more attributes. For example,
a film database may record genre(s) and sub-genre(s) as separate fields, hiding the
hierarchical dependency. The hierarchy may be obvious to a human reader of the data,
but it is invisible to the machine. Similarly, XML tags can hide structure. XML relies
on human interpretation for its “semantics” - a programmer can take advantage of the
fact that and are subtypes of , but a program has
no way of knowing this unless it is made explicit by means of a taxonomy. Although
a well-designed schema will make hierarchical structure explicit, our experience is
that a significant proportion of data sources rely on programmer intuition instead.
We have investigated formal concept analysis (FCA) [7, 8] as a way of extracting
hidden structure from a dataset in object-attribute-value form. In its simplest form,
FCA considers a binary-valued table, where each row corresponds to an object and
84 T. Martin, Z. Siyao and A. Majidian
each column to an attribute (property). The extension to a fuzzy case is (relatively)
straightforward, by considering a fuzzy relation R* and alpha-cuts which reduce the
problem to the crisp case. A brief outline and promising initial results are given in [9].
4 Applications
Two specific domains form demonstrators for this work. XML process mining
algorithms exist to discover process model from log files; various additions include
heuristic and fuzzy approaches to handle noisy data. Semantic processing mining
involves ontology knowledge. The ProM [www.processmining.org] platform takes
SA-MXML (semantic annotated mxml) files as input, where the annotation conforms
to the Web Service Modelling Language. The aim of this demonstrator is to find
(partial) similarities between process models in different domains, and use process
simulation tools to determine whether one process can be improved by slightly
altering it to match the second process more closely. The second demonstrator is
based on web forum discussions and support centre documentation, and will attempt
to improve the automated provision of “help” information.
5 Summary
This paper has briefly outlined a project to automate aspects of creative knowledge
discovery. The project is in early stages. Although not a direct application of
uncertain reasoning in the semantic web, it shares many of the same problems and
useful cross-fertilisation of ideas should be possible.
Acknowledgement : this work was partly funded by the FP7 BISON (Bisociation
Networks for Creative Information Discovery) project, number 211898.
6 References
1. King, R.D., et al., Functional genomic hypothesis generation and experimentation by a robot
scientist. Nature, 2004(6971): p. 247-251.
2. Koestler, A., The Act of Creation. 1964: Macmillan. 751.
3. Sherwood,D. SilverBullet Machine::Guide to Creativity,2009, www.silverbulletmachine.com
4. Sherwood, D., Koestler's Law: The Act of Discovering Creativity-And How to Apply It in
Your Law Practice. Law Practice, 2006. 32(8).
5. Martin,T.P, B.Azvine, Y.Shen, Granular Assoc Rules for Multiple Taxonomies: A Mass
Assignment Approach in Uncertain Reasoning in the Sem Web, M.Nickles, Ed 2008,Springer.
6. Berners-Lee, T, J.Hendler, and O.Lassila, Semantic Web, in Scientific American 2001 p28-37.
7. Ganter, B, R. Wille, Formal Concept Analysis: Mathematical Foundations. 1998: Springer.
8. Priss, U., Formal Concept Analysis in Information Science.
9. Majidian, A. and T.P. Martin. Extracting Taxonomies from Data - a Case Study using Fuzzy
FCA. in Web Intelligence-09. 2009. Milan, Italy: IEEE Computing.