Adding structure to unstructured data

Buneman, Peter; Davidson, Susan B.; Fernández, Mary; Suciu, Dan

doi:10.1007/3-540-62222-5_55

Cited by 229 publications

(159 citation statements)

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“…The ability to represent data whose structure is less rigid and strict than in conventional databases is indeed considered a crucial aspect in modern approaches to data modeling, and is important in many application areas, such as biological databases, digital libraries, and data integration [1,9,15,32,35]. Following [1], semi-structured data can be defined as data that is neither raw, nor strictly typed as in conventional database systems.…”

Section: Discussionmentioning

confidence: 99%

Representing and reasoning on XML documents: a description logic approach

Calvanese

Giacomo²,

Lenzerini³

1999

Journal of Logic and Computation

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Recent proposals to improve the quality of interaction with the World Wide Web suggest considering the Web as a huge semistructured database, so that retrieving information can be supported by the task of database querying. Under this view, it is important to represent the form of both the network, and the documents placed in the nodes of the network. However, the current proposals do not pay sufficient attention to represent document structures and reasoning about them. In this paper, we address these problems by providing a framework where Document Type Definitions (DTDs) expressed in the eXtensible Markup Language (XML) are formalized in an expressive Description Logic equipped with sound and complete inference algorithms. We provide methods for verifying conformance of a document to a DTD in polynomial time, and structural equivalence of DTDs in worst case deterministic exponential time, improving known algorithms for this problem which were double exponential. We also deal with parametric versions of conformance and structural equivalence, and investigate other forms of reasoning on DTDs. Finally, we show how to take advantage of the reasoning capabilities of our formalism in order to perform several optimization steps in answering queries posed to a document base.

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Section: Discussionmentioning

confidence: 99%

Representing and reasoning on XML documents: a description logic approach

Calvanese

Giacomo²,

Lenzerini³

1999

Journal of Logic and Computation

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“…Structure Index for Graph Structured Data. Structure indexes have been widely used for semi-structured and XML data [4,13,6]. A well-known concept is the dataguide [9], which is a structural description for rooted data graphs.…”

Section: Algorithm 1: Approximate Matching Based On Neighborhood Joinmentioning

confidence: 99%

Approximate and Incremental Processing of Complex Queries against the Web of Data

Tran

Ladwig

Wagner

2011

Lecture Notes in Computer Science

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Abstract. The amount of data on the Web is increasing. Current exact and complete techniques for matching complex query pattern against graph-structured web data have limits. Considering web scale, exactness and completeness might have to be traded for responsiveness. We propose a new approach, allowing an affordable computation of an initial set of (possibly inexact) results, which can be incrementally refined as needed. It is based on approximate structure matching techniques, which leverage the notion of neighborhood overlap and structure index. For exact and complete result computation, evaluation results show that our incremental approach compares well with the state of the art. Moreover, approximative results can be computed in much lower response time, without compromising too much on precision.

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“…They are structural summaries for semistructured data, useful for formulating queries, storing statistics about paths and nodes, and enabling query optimization. In [10], graph schemas are introduced to formulate, optimize and decompose queries for semistructured data. These approaches do not provide a direct solution to the problem of structural inconsistencies and differences in data sources that we address here.…”

Section: Related Workmentioning

confidence: 99%

Querying Tree-Structured Data Using Dimension Graphs

Theodoratos

Dalamagas

2005

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

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Abstract. Tree structures provide a popular means to organize the information on the Web. Taxonomies of thematic categories, concept hierarchies, e-commerce product catalogs are examples of such structures. Querying multiple data sources that use tree structures to organize their data is a challenging issue due to name mismatches, structural differences and structural inconsistencies that occur in such structures, even for a single knowledge domain. In this paper, we present a method to query tree-structured data. We introduce dimensions which are sets of semantically related nodes in tree structures. Based on dimensions, we suggest dimension graphs. Dimension graphs can be automatically extracted from trees and abstract their structural information. They are semantically rich constructs that provide query guidance to pose and evaluate queries on trees. We design a query language to query treestructured data. A key feature of this language is that queries are not restricted by the structure of the trees. We present a technique for evaluating queries and we provide necessary and sufficient conditions for checking query unsatisfiability. We also show how dimension graphs can be used to query multiple trees in the presence of structural differences and inconsistencies.

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Adding structure to unstructured data

Cited by 229 publications

References 10 publications

Representing and reasoning on XML documents: a description logic approach

Representing and reasoning on XML documents: a description logic approach

Approximate and Incremental Processing of Complex Queries against the Web of Data

Querying Tree-Structured Data Using Dimension Graphs

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