Paper Info
Title
A Graph-Based Approach to Learn Semantic Descriptions of Data Sources
Abstract
Semantic models of data sources and services provide support to automate many tasks such as source discovery, data integration, and service composition, but writing these semantic descriptions by hand is a tedious and time-consuming task. Most of the related work focuses on automatic annotation with classes or properties of source attributes or input and output parameters. However, constructing a source model that includes the relationships between the attributes in addition to their semantic types remains a largely unsolved problem. In this paper, we present a graph-based approach to hypothesize a rich semantic description of a new target source from a set of known sources that have been modeled over the same domain ontology. We exploit the domain ontology and the known source models to build a graph that represents the space of plausible source descriptions. Then, we compute the top k candidates and suggest to the user a ranked list of the semantic models for the new source. The approach takes into account user corrections to learn more accurate semantic descriptions of future data sources. Our evaluation shows that our method produces models that are twice as accurate than the models produced using a state of the art system that does not learn from prior models.
Year
DOI
Venue
2013
10.1007/978-3-642-41335-3_38
International Semantic Web Conference (1)
Keywords
Field
DocType
semantic model,source modeling,source description,semantic description,semantic web
Semantic similarity,Data mining,Semantic integration,Information retrieval,Semantic Web Stack,Computer science,Semantic interoperability,Semantic equivalence,Semantic grid,Semantic computing,Database,Semantic data model
Conference
Volume
ISSN
Citations 
8218
0302-9743
7
PageRank 
References 
Authors
0.58
16
4
Name
Order
Citations
PageRank
Mohsen Taheriyan121314.05
Craig A. Knoblock25229680.57
Pedro Szekely31217179.80
José Luis Ambite4958110.89