Paper Info
Title
Semantic Query Answering with Time-Series Graphs
Abstract
Statistical graphs are ubiquitous mechanisms for data visualization such that most, if not all, enterprises communicate information through them. However, many graphs are stored as unstructured images or proprietary binary objects, making them difficult to work with beyond the reports in which they are embedded. While graphs can be mapped to more common XML representations, these lack expressive semantics to discover new knowledge about them or to answer queries at various levels of granularity. This paper describes an OWL ontology that facilitates the representation, exchange, reasoning and query answering of statistical graph data. We illustrate the advantages of using an ontological approach to discover and query about time-series statistical graphs.
Year
DOI
Venue
2007
10.1109/EDOCW.2007.28
EDOCW
Keywords
DocType
ISSN
time-series graphs,ontological approach,common xml representation,time-series statistical graph,semantic query answering,statistical graph data,new knowledge,proprietary binary object,owl ontology,lack expressive semantics,query answering,statistical graph,semantic web,biology,ontologies,knowledge discovery,transform coding,natural languages,data mining,knowledge representation,owl,pattern matching,time measurement,business,mathematical model,time series,design methodology,data structures,displays,terminology,algebra,government,writing,xml,publishing,graph theory,data visualisation,length measurement,encoding,measurement units,user interfaces,graphics,organizations,data visualization,grounding,cognition,taxonomy,databases,data models,ubiquitous computing,computer science,time series analysis
Conference
2325-6583
Citations 
PageRank 
References 
6
0.56
9
Authors
3
Name
Order
Citations
PageRank
Leo Ferres113518.48
Michel Dumontier289893.35
Natalia Villanueva-Rosales315916.42