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|Abstract=The Resource Description Framework (RDF) has become
+
|Abstract=The Resource Description Framework (RDF) has
an accepted standard for describing entities on the Web.
+
become an accepted standard for describing entities on the Web. Many such RDF descriptions are text-rich – besides structured data, they also feature large portions of unstructured text. As a result, RDF data is frequently queried using predicates matching structured data, combined with string predicates for textual constraints: hybrid queries. Evaluating hybrid queries requires accu-
At the same time, many RDF descriptions today are text-
+
rate means for selectivity estimation. Previous works on selectivity estimation, however, suffer from inherent drawbacks, reflected in efficiency and effective issues. In this paper, we present a general framework for hybrid selectivity estimation. Based on its requirements, we study the applicability of existing approaches. Driven by our findings, we propose a novel estimation approach, TopGuess, exploiting topic models as data synopsis. This enables us to capture correlations between structured and unstructured data in a uniform and scalable manner. We study TopGuess in theorical manner, and show TopGuess to guarantee a linear space
rich – besides structured data, they also feature large portions of unstructured text. Such semi-structured data is frequently queried using predicates matching structured data, combined with string predicates for textual constraints: hybrid queries. Evaluating hybrid queries efficiently requires effective means for selectivity estimation. Previous works on selectivity estimation, however, target either structured or unstructured data alone. In contrast, we study the prob-
+
complexity w.r.t. text data size, and a selectivity estimation time complexity independent from its synopsis size. In experiments on real-world data, TopGuess allowed for great improvements in estimation accuracy, without scarifying runtime performance.
lem in a uniform manner by exploiting a topic model as
 
data synopsis, which enables us to accurately capture correlations between structured and unstructured data. Relying on this synopsis, our novel topic-based approach (TopGuess) uses as small, fine-grained query-specific Bayesian network (BN). In experiments on real-world data we show that the query-specific BN allows for great improvements in estimation accuracy. Compared to a baseline relying on PRMs we could achieve a gain of 20%. In terms of efficiency TopGuess
 
performed comparable to our baselines.
 
 
|Download=Paper-vldb-selectivityestimation tr.pdf
 
|Download=Paper-vldb-selectivityestimation tr.pdf
 
|Projekt=IZEUS
 
|Projekt=IZEUS

Version vom 23. Juli 2013, 11:32 Uhr


Topic-based Selectivity Estimation for Hybrid Queries over RDF Graphs




Published: 2013 Mai
Institution: Institute AIFB, KIT
Erscheinungsort / Ort: Karlsruhe
Archivierungsnummer:3039

BibTeX



Kurzfassung
The Resource Description Framework (RDF) has become an accepted standard for describing entities on the Web. Many such RDF descriptions are text-rich – besides structured data, they also feature large portions of unstructured text. As a result, RDF data is frequently queried using predicates matching structured data, combined with string predicates for textual constraints: hybrid queries. Evaluating hybrid queries requires accu- rate means for selectivity estimation. Previous works on selectivity estimation, however, suffer from inherent drawbacks, reflected in efficiency and effective issues. In this paper, we present a general framework for hybrid selectivity estimation. Based on its requirements, we study the applicability of existing approaches. Driven by our findings, we propose a novel estimation approach, TopGuess, exploiting topic models as data synopsis. This enables us to capture correlations between structured and unstructured data in a uniform and scalable manner. We study TopGuess in theorical manner, and show TopGuess to guarantee a linear space complexity w.r.t. text data size, and a selectivity estimation time complexity independent from its synopsis size. In experiments on real-world data, TopGuess allowed for great improvements in estimation accuracy, without scarifying runtime performance.

Download: Media:Paper-vldb-selectivityestimation tr.pdf

Projekt

IZEUS



Forschungsgruppe

Wissensmanagement


Forschungsgebiet

Semantische Suche