Remi Coletta

A Flexible Approach for Planning Schema Matching Algorithms

Most of the schema matching tools are assembled from multiple match algorithms, each employing a particular technique to improve matching accuracy and making matching systems extensible and customizable to a particular domain. The solutions provided by current schema matching tools consist in aggregating the results obtained by several match algorithms to improve the quality of the discovered matches. However, aggregation entails several drawbacks. Recently, it has been pointed out that the main issue is how to select the most suitable match algorithms to execute for a given domain and how to adjust the multiple knobs (e.g. threshold, performance, quality, etc.). In this article, we present a novel method for selecting the most appropriate schema matching algorithms. The matching engine makes use of a decision tree to combine the most appropriate match algorithms. As a first consequence of using the decision tree, the performance of the system is improved since the complexity is bounded by the height of the decision tree. Thus, only a subset of these match algorithms is used during the matching process. The second advantage is the improvement of the quality of matches. Indeed, for a given domain, only the most suitable match algorithms are used. The experiments show the effectiveness of our approach w.r.t. other matching tools.

A SAT-based version space algorithm for acquiring constraint satisfaction problems

Constraint programming is rapidly becoming the technology of choice for modelling and solving complex combinatorial problems. However, users of this technology need significant expertise in order to model their problems appropriately. The lack of availability of such expertise is a significant bottleneck to the broader uptake of constraint technology in the real world. We present a new SAT-based version space algorithm for acquiring constraint satisfaction problems from examples of solutions and non-solutions of a target problem. An important advantage is the ease with which domain-specific knowledge can be exploited using the new algorithm. Finally, we empirically demonstrate the algorithm and the effect of exploiting domain-specific knowledge on improving the quality of the acquired constraint network.

Not) yet another matcher

Discovering correspondences between schema elements is a crucial task for data integration. Most schema matching tools are semi-automatic, e.g. an expert must tune some parameters (thresholds, weights, etc.). They mainly use several methods to combine and aggregate similarity measures. However, their quality results often decrease when one requires to integrate a new similarity measure or when matching particular domain schemas. This paper describes YAM (Yet Another Matcher), which is a schema matcher factory. Indeed, it enables the generation of a dedicated matcher for a given schema matching scenario, according to user inputs. Our approach is based on machine learning since schema matchers can be seen as classifiers. Several bunches of experiments run against matchers generated by YAM and traditional matching tools show how our approach is able to generate the best matcher for a given scenario.

YAM: a schema matcher factory

In this paper, we present YAM, a schema matcher factory. YAM (Yet Another Matcher) is not (yet) another schema matching system as it enables the generation of a la carte schema matchers according to user requirements. These requirements include a preference for recall or precision, a training data set (schemas already matched) and provided expert correspondences. YAM uses a knowledge base that includes a (possibly large) set of similarity measures and classifiers. Based on the user requirements, YAM learns how to best apply these tools (similarity measures and classifiers) in concert to achieve the best matching quality. In our demonstration, we will let users apply YAM to build the best schema matcher for different user requirements.

Semi-automatic modeling by constraint acquisition

Constraint programming is a technology which is now widely used to solve combinatorial problems in industrial applications. However, using it requires considerable knowledge and expertise in the field of constraint reasoning. This paper introduces a framework for automatically learning constraint networks from sets of instances that are either acceptable solutions or non-desirable assignments of the problem we would like to express. Such an approach has the potential to be of assistance to a novice who is trying to articulate her constraints. By restricting the language of constraints used to build the network, this could also assist an expert to develop an efficient model of a given problem. [1] contains a long version of this paper. The collaboration between LIRMM and the Cork Constraint Computation Centre is supported by a Ulysses Travel Grant from Enterprise Ireland, the Royal Irish Academy and CNRS (Grant Number FR/2003/022). This work has also received support from Science Foundation Ireland under Grant 00/PI.1/C075.

A generic approach for combining linguistic and context profile metrics in ontology matching

Ontology matching is needed in many application domains. In this paper, we present a machine learning approach for combining metrics, which exploits various linguistic and context profiles features in order to discover mappings between entities of different ontologies. Our approach has been implemented and the experimental results over Benchmark and Conference test cases on OAEI 2010 campaign demonstrate its effectiveness and efficiency in terms of quality of matching and flexibility.

Public data integration with WebSmatch

Integrating open data sources can yield high value information but raises major problems in terms of metadata extraction, data source integration and visualization of integrated data. In this paper, we describe WebSmatch, a flexible environment for Web data integration, based on a real, end-to-end data integration scenario over public data from Data Publica. WebSmatch supports the full process of importing, refining and integrating data sources and uses third party tools for high quality visualization. We use a typical scenario of public data integration which involves problems not solved by currents tools: poorly structured input data sources (XLS files) and rich visualization of integrated data.

A Flexible System for Ontology Matching

Most of the solutions provided by current ontology matching tools lack flexibility and extensibility namely for adding new matchers and dealing with users’ requirements. In this paper, we present a system YAM++, which supports self-configuration, flexibility and extensibility in combining individual matchers. Additionally, it is more human-centered approach since it allows users to express their preference between precision and recall. A set of experiments over OAEI Benchmark datasets demonstrate its effectiveness and efficiency in terms of quality of matching and flexibility of the system.

Modeling view selection as a constraint satisfaction problem

Using materialized views can highly speed up the query processing time. This paper deals with the view selection issue, which consists in finding a set of views to materialize that minimizes the expected cost of evaluating the query workload, given a limited amount of resource such as total view maintenance cost and/or storage space. However, the solution space is huge since it entails a large number of possible combinations of views. For this matter, we have designed a solution involving constraint programming, which has proven to be a powerful approach for modeling and solving combinatorial problems. The efficiency of our method is evaluated using workloads consisting of queries over the schema of the TPC-H benchmark. We show experimentally that our approach provides an improvement in the solution quality (i.e., the quality of the obtained set of materialized views) in term of cost saving compared to genetic algorithm in limited time. Furthermore, our approach scales well with the query workload size.

Adaptive Parameterized Consistency

State-of-the-art constraint solvers uniformly maintain the same level of local consistency usually arc consistency on all the instances. We propose parameterized local consistency, an original approach to adjust the level of consistency depending on the instance and on which part of the instance we propagate. We do not use as parameter one of the features of the instance, as done for instance in portfolios of solvers. We use as parameter the stability of values, which is a feature based on the state of the arc consistency algorithm during its execution. Parameterized local consistencies choose to enforce arc consistency or a higher level of local consistency on a value depending on whether the stability of the value is above or below a given threshold. We also propose a way to dynamically adapt the parameter, and thus the level of local consistency, during search. This approach allows us to get a good trade-off between the number of values pruned and the computational cost. We validate our approach on various problems from the CSP competition.