=Paper=
{{Paper
|id=None
|storemode=property
|title=Information technologies - synergy of theory and application
|pdfUrl=https://ceur-ws.org/Vol-683/paper2.pdf
|volume=Vol-683
|dblpUrl=https://dblp.org/rec/conf/itat/Vojtas10
}}
==Information technologies - synergy of theory and application==
https://ceur-ws.org/Vol-683/paper2.pdf
Information technologies - synergy of theory and application⋆
Peter Vojtáš
Department of Software Engineering, School of Computer Science, Faculty of Mathematics and Physics, Charles
University, Malostranské nám. 25, 118 00 Praha 1, Czech Republic
vojtas@ksi.mff.cuni.cz
In this lecture we give several examples and lessons ues (see [2])? (Second) In our model we have a con-
learned from research, development and experiments tinuous semantic, on the other side in [6] GAP was
in the area of theory and applications of information not continuous (and general connection between these
technology. We will try to describe a possible synergy two models was also an open problem). This was all
of theory and application too. Namely, to describe solved in [2], introducing a model based on left con-
where practical needs bring new problems for theory tinuous conjunctors, with a weak form of border con-
and where theory helps to formulate methods, which dition (without associativity and commutativity) and
should be verified in practice. with body aggregation . We have shown that FLP is
In the theoretical part we will mention research on (in a sense) equivalent to GAP.
correctness and completeness of fuzzy logic program- In the application part we make difference between
ming [1,2] and various measures for evaluating success. case studies and use cases. Case studies include de-
In applications we mention acquaintance with devel- scriptions of systems that have been deployed within
opment and experiments of preferential querying and an organization, and are now being used within a pro-
user dependent top-k answers [3]. In all of these it also duction environment. Use cases include examples
depends on whether our task is deductive (querying), where an organization has built a prototype system,
inductive (learning) or abductive. In practice, it is im- but it is not currently being used by business func-
portant to have a user behavior model (for many dif- tions [18]. Repeatability of experiments is also an is-
ferent users). To see what is (can be, must be) done au- sue, see e.g. [19]. So our applications here are not true
tomatically (trained, assisted, unsupervised, . . . ) and deployed applications, they are rather experiments
what by human, what is domain dependent and what (use case prototypes) and repeatability is not always
is generic. Our fuzzy model is not a mere generaliza- fully satisfactory enabled.
tion from two values to many values. The key point of Main impulse for these considerations came from
our study here is our understanding of fuzzy value as a referee refuting our paper in an application oriented
preference degree. Using fuzzy as preferences enlight- conference. He/She asked – where from do you have
ens phenomena which in a two valued world are not rules of your FLP? So far main motivation in fuzzy
visible at all. were toy examples with tall Swedes and young bas-
From mathematical point of view one can general- ketball players. Our motivation was real life examples
ize LP to many valued logic. Also here we face several where fuzzy degree is the degree of user preference
challenges. Should our rules be implications or clauses, (and no more fuzzy linguistic variables with modifiers
should our computation be refutation or query answer- like very tall . . . ). A well developed counterpart of this
ing, is unification touched by this or not? In two valued is already in preferential querying, where only top-k
logic these are equivalents, in [1] we have developed a most preferred answers are interesting. Main contri-
model of ([0,1] valued) fuzzy logic programming FLP bution here was made by R. Fagin (see e.g. [10]), who
with implicative rules and computation based on back- (in a datalog setting, without function symbols) as-
ward usage of modus pones (and possible extension sumed we have objects in several lists repeatedly or-
with fuzzy similarity in [4]). dered by different attributes (local) preferences and
Concerning implementation of this system, gave an original optimal algorithm for top-k for this
M. Lieskovsky has constructed in [5] a fuzzy War- setting. This direction was further investigated
ren abstract machine. Our system enables new form by P. Gurský who has implemented and experimented
of cuts for threshold queries (see [1]). with several heuristics (see e.g. [7]). V. Vaneková has
Further development went in two directions. developed several knowledge representation models for
(First) What happens in finitely valued case when dif- this ([8]).
ferent attributes take different number of truth val- But referee asked where from do you have those
rules (Fagin assumes we have the (query) rules, with
⋆ local preferences and global aggregation (in good con-
This work was partially supported by Czech Grant
Agency GA CR under number 202/10/0761. cordance with our result FLP = GAP)? So now the
8 Peter Vojtáš
question is, where from we have local preferences (user 8. V. Vaneková, P. Vojtáš: Comparison of scoring and
preferences on attributes represented by a fuzzy (rank- order approach in description logic EL(D). SOFSEM
ing) set) and where from do we have combination (ag- 2010: 709–720.
gregation] function giving the global preference? This 9. A. Eckhardt, T. Horváth, P. Vojtáš: Learning different
is an inductive task. With T. Horváth, A. Eckhardt we user profile annotated rules for fuzzy preference top-k
querying. SUM 2007, 116–130.
have developed several inductive models (see e.g. [9]).
10. R. Fagin, A. Lotem, M. Naor: Optimal aggregation al-
Moreover a practical problem occurred. Well, as- gorithms for middleware. J. Comput. Syst. Sci. 66 (4),
sume we have different users (with different attribute 2003, 614–656.
preferences and aggregation). What are and where 11. P. Jenček, P. Vojtáš, M. Kopecký, C. Hösch: Sociomap-
from are inputs? Do we assume user implicit inputs ping in text retrieval systems. FQAS 2009, 122–133.
(e.g. click stream behavior) or (some form of) user 12. B. Václav, A. Eckhardt, and P. Vojtáš: A web shop
explicit inputs. User aspects of these problems are de- with user preference search capabilities. To appear in
veloped in[3,11,12] and we have to admit that exper- Web Intelligence/IAT Workshops 2010, IEEE Com-
iments are mostly done with an artificially generated puter Society, 2010.
user, very few human user experiments were done (and 13. J. Pokorný, P. Vojtáš: A data model for flexible query-
we have a problem how to evaluate them). Supporting ing. ADBIS 2001, 280–293.
14. A. Eckhardt, J. Pokorný, P. Vojtáš: A system recom-
data storage for these tasks is challenged too, we gave
mending top-k objects for multiple users preferences.
a model of fuzzy relational algebra for flexible query- FUZZ-IEEE 2007, 1–6.
ing in [13] and an index structure for multiple user 15. A. Eckhardt, T. Horváth, D. Maruščák, R. Novotný,
preferential queries in [14]. P.Vojtáš: Uncertainty issues and algorithms in au-
For theoretical part it is now clear that equality tomating process connecting web and user. URSW
of fuzzy sets (correct answers and computed answers) (LNCS Vol.) 2008, 207–223.
is not a good measure and correctness and complete- 16. J. Dědek, P. Vojtáš: Fuzzy classification of web re-
ness results have to be reconsidered with some order ports with linguistic text mining. Web Intelligence/IAT
violation/concordance measures. Workshops 2009, 167–170.
Further, from an experimental point of view, we 17. R. Novotný, P. Vojtáš, D. Maruščák: Information ex-
went in direction of web information extrac- traction from web pages. Web Intelligence/IAT Work-
shops 2009, 121–124.
tion ([16,17]), because it is also interesting to know
18. Semantic Web Case Studies and Use Cases,
where are all these data from (after where are rules http://www.w3.org/2001/sw/sweo/public/UseCases/.
from). Situation connecting web and user is heavily 19. S. Manegold et al.: Repeatability & work-
influences by uncertainty, starting research is done ability evaluation of SIGMOD 2009. SIG-
in [15]. MOD Record 38 (3), 2009, 40–43, see e.g.
We can conclude, that synergy between theoretical http://www.sigmod08.org/sigmod call papers.shtml
and applied (experimental) research and development sub 6.
was beneficial for both of them.
References
1. P. Vojtáš: Fuzzy logic programming. Fuzzy Sets and
Systems, 124 (3), 2001, 361–370.
2. S. Krajči, R. Lencses, P. Vojtáš: A comparison of fuzzy
and annotated logic programming. Fuzzy Sets and Sys-
tems, 144, 2004, 173–192.
3. A. Eckhardt, P. Vojtáš: Combining various methods of
automated user decision and preferences modeling. In:
MDAI 09, Springer Verlag, 172-181.
4. Jesús Medina, Manuel Ojeda-Aciego, Peter Vojtáš:
Similarity-based unification: a multi-adjoint approach.
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5. M. Lieskovsky: Quantitative search strategies. Neural
Network World.
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ized annotated logic programming and its applications.
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7. P. Gurský, P. Vojtáš: On top-ksearch with no random
access using small memory. ADBIS 2008, 97–111.