A Large Scale Dataset for the Evaluation of Ontology Matching Systems

Recently, the number of ontology matching techniques and systems has increased significantly. This makes the issue of their evaluation and comparison more severe. One of the challenges of the ontology matching evaluation is in building large scale evaluation datasets. In fact, the number of possible correspondences between two ontologies grows quadratically with respect to the numbers of entities in these ontologies. This often makes the manual construction of the evaluation datasets demanding to the point of being infeasible for large scale matching tasks. In this paper we present an ontology matching evaluation dataset composed of thousands of matching tasks, called TaxME2. It was built semi-automatically out of the Google, Yahoo and Looksmart web directories. We evaluated TaxME2 by exploiting the results of almost two dozen of state of the art ontology matching systems. The experiments indicate that the dataset possesses the desired key properties, namely it is error-free, incremental, discriminative, monotonic, and hard for the state of the art ontology matching systems. The paper has been accepted for publication in "The Knowledge Engineering Review", Cambridge Universty Press (ISSN: 0269-8889, EISSN: 1469-8005)

Towards information profiling: data lake content metadata management

There is currently a burst of Big Data (BD) processed and stored in huge raw data repositories, commonly called Data Lakes (DL). These BD require new techniques of data integration and schema alignment in order to make the data usable by its consumers and to discover the relationships linking their content. This can be provided by metadata services which discover and describe their content. However, there is currently a lack of a systematic approach for such kind of metadata discovery and management. Thus, we propose a framework for the profiling of informational content stored in the DL, which we call information profiling. The profiles are stored as metadata to support data analysis. We formally define a metadata management process which identifies the key activities required to effectively handle this.We demonstrate the alternative techniques and performance of our process using a prototype implementation handling a real-life case-study from the OpenML DL, which showcases the value and feasibility of our approach.Peer ReviewedPostprint (author's final draft

Automated schema matching techniques: an exploratory study

Manual schema matching is a problem for many database applications that use multiple data sources including data warehousing and e-commerce applications. Current research attempts to address this problem by developing algorithms to automate aspects of the schema-matching task. In this paper, an approach using an external dictionary facilitates automated discovery of the semantic meaning of database schema terms. An experimental study was conducted to evaluate the performance and accuracy of five schema-matching techniques with the proposed approach, called SemMA. The proposed approach and results are compared with two existing semi-automated schema-matching approaches and suggestions for future research are made

XML Matchers: approaches and challenges

Schema Matching, i.e. the process of discovering semantic correspondences between concepts adopted in different data source schemas, has been a key topic in Database and Artificial Intelligence research areas for many years. In the past, it was largely investigated especially for classical database models (e.g., E/R schemas, relational databases, etc.). However, in the latest years, the widespread adoption of XML in the most disparate application fields pushed a growing number of researchers to design XML-specific Schema Matching approaches, called XML Matchers, aiming at finding semantic matchings between concepts defined in DTDs and XSDs. XML Matchers do not just take well-known techniques originally designed for other data models and apply them on DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical structure of a DTD/XSD) to improve the performance of the Schema Matching process. The design of XML Matchers is currently a well-established research area. The main goal of this paper is to provide a detailed description and classification of XML Matchers. We first describe to what extent the specificities of DTDs/XSDs impact on the Schema Matching task. Then we introduce a template, called XML Matcher Template, that describes the main components of an XML Matcher, their role and behavior. We illustrate how each of these components has been implemented in some popular XML Matchers. We consider our XML Matcher Template as the baseline for objectively comparing approaches that, at first glance, might appear as unrelated. The introduction of this template can be useful in the design of future XML Matchers. Finally, we analyze commercial tools implementing XML Matchers and introduce two challenging issues strictly related to this topic, namely XML source clustering and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them