On Evaluating Schema Matching and Mapping

Bellahsène, Zohra; Bonifati, Angela; Duchateau, Fabien; Velegrakis, Yannis

doi:10.1007/978-3-642-16518-4_9

Cited by 107 publications

(156 citation statements)

References 17 publications

Supporting

Mentioning

156

Contrasting

Order By: Relevance

“…We apply this idea by sorting all sets of Steiner nodes (M atchedSet in line 20) based on how coherent each set is. For example, considering the four possible mappings we showed earlier for s 4 , r 1 and r 4 will be ranked higher than r 2 and r 3 , as their matched nodes are part of the same pattern. Once all the node sets are sorted, we pick the top k ones and compute the Steiner tree algorithm only over these sets to generate k candidate models for the new sourceŝ.…”

Section: : End For Return Candidatesmentioning

confidence: 87%

See 1 more Smart Citation

A Graph-Based Approach to Learn Semantic Descriptions of Data Sources

Taheriyan

Knoblock

Szekely

et al. 2013

Advanced Information Systems Engineering

View full text Add to dashboard Cite

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.

show abstract

Section: : End For Return Candidatesmentioning

confidence: 87%

“…Now, suppose that the first three sources (s 1 , s 2 , and s 3 ) have already been modeled and the other two (s 4 and s 5 ) are new sources not modeled yet. The goal is to automatically infer the source descriptions for s 4 and s 5 given the ontology and the models for s 1 , s 2 , and s 3 .…”

Section: Motivating Examplementioning

confidence: 99%

A Graph-Based Approach to Learn Semantic Descriptions of Data Sources

Taheriyan

Knoblock

Szekely

et al. 2013

Advanced Information Systems Engineering

View full text Add to dashboard Cite

show abstract

“…For the subproblem of ontology matching such evaluations are now quite common [1] [3] and there is also a benchmark for determining schema mappings [2]. Typically the quality of a match algorithm is determined by evaluating it on some match problems for which a manually defined perfect match result is provided for comparison.…”

Section: Motivationmentioning

confidence: 99%

Towards a Benchmark for Ontology Merging

Raunich

Rahm

2012

On the Move to Meaningful Internet Systems: OTM 2012 Workshops

View full text Add to dashboard Cite

Abstract. Benchmarking approaches for ontology merging is challenging and has received little attention so far. A key problem is that there is in general no single best solution for a merge task and that merging may either be performed symmetrically or asymmetrically. As a first step to evaluate the quality of ontology merging solutions we propose the use of general metrics such as the relative coverage of the input ontologies, the compactness of the merge result as well as the degree of introduced redundancy. We use these metrics to evaluate three merge approaches for different merge scenarios. MotivationOntologies and taxonomies are increasingly used to semantically categorize or annotate information, e.g., for e-commerce or e-science. For example, product catalogs of online shops or comparison portals categorize products to help users and applications finding relevant information. Since many ontologies refer to the same domain and to the same objects, there is a growing need to integrate or merge such related ontologies. The goal is to create a merged ontology providing a unified view on two or more input ontologies.Ontology merging is a challenging problem especially for large and heterogeneous ontologies and require semi-automatic approaches to reduce the manual effort. Several such merge approaches have already been proposed, however their relative quality is largely unknown. One increasingly adopted and promising idea is to decompose the complex integration problem into match and merge subtasks and leverage the advances made for automatic ontology and schema matching [13] to solve the first subproblem. The merge subtask can then utilize a match mapping identifying corresponding concepts in the input ontologies that should be merged. Such a match-based merging is followed in [11] [7]) are most common and aim at completely integrating all input ontologies with the same priority. Asymmetric approaches, by contrast, take one of the input ontologies as the target and merge the other input ontologies into this target [11] [15] [6] thereby giving preference to the target ontology.Given the different merge approaches we see an increasing need to quantitatively evaluate their quality and performance. For the subproblem of ontology matching such evaluations are now quite common [1] [3] and there is also a benchmark for determining schema mappings [2]. Typically the quality of a match algorithm is determined by evaluating it on some match problems for which a manually defined perfect match result is provided for comparison. While a similar approach for evaluating merge approaches has been advocated for in [4] we argue that there is in general no single perfect merge

show abstract

“…Then it integrates the form into the database by discovering valid correspondences and extending the database. For element correspondences, we employ an element matching function δ(FT , D) (e.g., a schema matching program [8]) which returns a set of correspondences M = {FT :P/e D:d}, which relates an between every pair of elements in the tree and the database. We set up a high threshold value (e.g., 0.99) and take all pairs of elements that have similarity above the threshold as an initial set of correspondences.…”

Section: Mapping and Integrating Form Treesmentioning

confidence: 99%

Automatically Mapping and Integrating Multiple Data Entry Forms into a Database

An¹,

Khare²,

Song³

et al. 2011

Conceptual Modeling – ER 2011

View full text Add to dashboard Cite

Abstract. Forms are a standard way of gathering data into a database. Many applications need to support multiple users with evolving data gathering requirements. It is desirable to automatically link dynamic forms to the back-end database. We have developed the FormMapper system, a fully automatic solution that accepts user-created data entry forms, and maps and integrates them into an existing database in the same domain. The solution comprises of two components: tree extraction and form integration. The tree extraction component leverages a probabilistic process, Hidden Markov Model (HMM), for automatically extracting a semantic tree structure of a form. In the form integration component, we develop a merging procedure that maps and integrates a tree into an existing database and extends the database with desired properties. We conducted experiments evaluating the performance of the system on several large databases designed from a number of complex forms. Our experimental results show that the FormMapper system is promising: It generated databases that are highly similar (87% overlapped) to those generated by the human experts, given the same set of forms.

show abstract

On Evaluating Schema Matching and Mapping

Cited by 107 publications

References 17 publications

A Graph-Based Approach to Learn Semantic Descriptions of Data Sources

A Graph-Based Approach to Learn Semantic Descriptions of Data Sources

Towards a Benchmark for Ontology Merging

Automatically Mapping and Integrating Multiple Data Entry Forms into a Database

Contact Info

Product

Resources

About