Semantic Query Answering with Time-Series Graphs is a research paper published in 2007 Eleventh International IEEE EDOC Conference Workshop (2007). On theSindex it has a DataRank of 0.208. It has been cited 3 times.
Statistical graphs are ubiquitous mechanisms for data visualization such that most, if not all, enterprises communicate information through their. 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.
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Base Score Contribution
0.208
From this paper's citation signal
Citation Network Contribution
0
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Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.