Extensions of Semantic web modeling towards ordering of query results J. Pokorný (Charles University, Prague), P. Vojtáš (Šafárik University, Košice)

Extensions of Semantic web modeling towards ordering of

query results

J. Pokorný (Charles University, Prague), P. Vojtáš (Šafárik University, Košice)

Vienna June 20th, 2005 W3C and Semantic Web 2

Motivation – user looking for best (top-k) objects

Same data – different problems and solutions - Kossmann’s skylines - Fagin’s threshold algorithm - fuzzy logic programming

Learning of user’s aggregation function @

DL: Value or existential restrictions

Fuzzy Description logic EL with @

Page Content Rank

Galois - Tukey connections – Questions and Answers

Conclusions

Outline

J. Pokorný, P. Vojtáš. Extensions of Semantic web modeling towards ordering of query results


Skyline of hotels: minimize price (x-axis) and distance to beach (y-axis)

Skyline of buildings

D. Kossmann et al. - Institute for Information Systems - DCS ETH Zurich

Find a good hotel



Access multimedia data – red – inherently fuzzy

Compound queries – red and round – aggregate

NY restaurant – good, inexpensive, close

- Zagat-Review web site gives rating

- NYT web site gives prices

- MapQuest gives distances … aggregate

Relevant answers wrt users intention – best, top-k


R. Fagin. Combining fuzzy information: an Overview. SIGMOD Record 31,2002Güntzer et al. Optimizing Multi-Feature Queries for Image Databases, VLDB’00

Fuzzy …


xx*

@

xx

*@

xx /

4

*3@ 1 closecheapuser

hotelgood


Combining fuzzy…


21,@ BBH

Continuity, fixpoint, completeness, similarity,

PV. Fuzzy logic programming. Fuzzy Sets and Systems. 124,3 (2001) 361-370

LPrbCH

rBHbBDB

I

)),(,(

),(),,(

BH BH Need not be equivalent to

Our solution

1BH

2BH Disjunction or aggregation?

Logic programming in extensional fuzzy logic in narrow sense



Resource Representation

Hotel, restaurant, product, service,… Object - attributeMultimedia, pictures,… color spectra, texture,…Texts vector, dependency tree,….

User QueryContext, classification, preference ideal object, text, …. Models - what is the bestDatabases - small, medium, large model-proof theoretic semanticsMetric - closer is betterVectors - smaler angle is betterBayes - more probable is betterMulticriterial decisionPrice function - optimizationFuzzy logic - comparative notion of truth – higher TV is better - semantics

Same data – different problems – different solutions



Deduction

Abduction

Induction

Learning how to combine particular attribute score Into global score FILP

Other inductive methods – on same data??

ObservatExplanatB

ExampleHypothesisB

Declarative query languages

ExampleHypothesisB /



Gradually classified data – input for learning – synthetic data



Add to background knowledge

p(d) ← d<c & p(c)

Graded ILP problem formulation

• E(e1) < E(e2) THEN fm (e1) ≤ fm (e2) and• E(e1) > E(e2) THEN fm (e1) ≥ fm (e2)

P. Vojtáš, T. Horváth, S. Krajči, R. Lencses. An ILP model for a monotone graded classification problem. Kybernetika 40,3 (2004) 317-332

Learning with qualitative constraint – monotonicity and the problem formulation




The aggregation function


Unicode

RDF + RDFSchema

ontology

logic

proof

URI

XML + name spaces + XMLSchema

optimisation

security

trust

Semantic web according to Tim Berners-Lee et al

Machine processing




F. Baader, S. Brandt, C. Lutz. Pushing the EL Envelope, 2005

Description logic FL0 …allows for value restriction ∀r.C…when clarifying the logical status of property arcs in semantic networks and slots in frames

– the decision was taken –

that arcs/slots should be read as value restrictions rather than existential restrictions ∃r.C

tractability turned out to be unattainable

DL – value or existential restriction



F. Baader, S. Brandt, C. Lutz. Pushing the EL Envelope, 2005

Recently, the choice of value restriction as a sine qua non of DL has been reconsidered…EL – allowing conjunction and existential restrictions…Better algorithmic properties

In applications the expressive power of EL sufficient

DL – value or existential restriction


good hotel

neither ∃ price.cheap distance.close⊓ ∃ nor ∃ price.cheap ⊔ distance.close∃

We need an extension

@( price.cheap, distance.close)∃ ∃


Fuzzy EL Description logic with @


PAGE CONTENT RANK

categories of Web data mining (Pal et al, 2002): • Web Structure Mining (WSM), • Web Usage Mining (WUM),• Web Content Mining (WCM).

Here: WCM refers broadly to the process of uncovering interesting and potentially useful knowledge from web contents/documents



The PCR method in sketch

Principle: PCR enables to classify pages from a set Rq of pages retrieved as the result of a conjunctive Boolean query q.

Relevance measure computation:– vector models: comparing the query vector with the page

vector– here: it is derived only from importances of terms that the page

containsThe core of PCR is based on an evaluation of terms from Rq according

to their occurrences in pages and their semantic properties. Final term evaluation is calculated from the parameters gained by an adapted backpropagation neural network.

A set of training examples (terms) is obtained from the set Rq0, where q0 is a starting query. The relevance of the terms to the given topic has been assigned manually.

Pokorný, Smižanský. IASTED AC 2005



PCR Concluded

To validate the hypothesis• PCR identifies pages which are more significant with

respect to their content and better explains given topic than the PageRank algorithm.

we need more experiments.Issues: time complexity of obtaining Rq,n

Solution: PCR module on the side of the search engine Development: • ranking clusters• continuous adaptability of the system depending on

user reactions • more semantics - phrases



Galois Tukey connections

P. Vojtáš. Gen. Galois-Tukey connections Israel Math.Conf.Proc. 6 (1993) 619-643A. Blass. Questions and answers, Proc. Linear Logic, J.Y. Girard, LMSLN 222, 1995

Questions1

Questions2

Answers1

Answers2

Correct1wrt KB1

Correct2wrt KB2

f(q1) Correct2 a2 then q1 Correct1 g(a2)

f g



Questions1

Questions2

Answers1

Answers2

_Correct1_

_Correct2_

(h(q1) C3 a3) and (f(q1,a3) Correct2 a2) then q1 Correct1 g(a2)

Q3 _C3_ A3

h

f

g

Galois-Tukey connections, iterated forcing and querying with advice




Marie-Christine Rousset: Small can be beautiful in the semantic web, ISWC 2004

We need modeling tools for- Ordering of results- Aggregation of particular attribute score into global

Conclusions

Optimisation Proof Logic Ontology RDF + RDFSXML … User dependent


Thank you


Documents

Extensions of Semantic web modeling towards ordering of query results J. Pokorný (Charles University, Prague), P. Vojtáš (Šafárik University, Košice)