The paper studies the application of automated machine learning approaches (AutoML) for addressing the problem of Entity Matching (EM).This would make the existing, highly effective, Machine Learning (ML) and Deep Learning based approaches for EM usable also by non-expert users, who do not have the expertise to train and tune such complex systems.Our experiments show that the direct application of AutoML systems to this scenario does not provide high quality results.To address this issue, we introduce a new component, the EM adapter, to be pipelined with standard AutoML systems, that preprocesses the EM datasets to make them usable by automated approaches.The experimental evaluation shows that our proposal obtains the same effectiveness as the state-of-the-art EM systems, but it does not require any skill on ML to tune it. INTRODUCTIONMachine Learning (ML) has significantly advanced over the past ten years [1].On one side, the research on Big Data let emerge new challenges and made available scenarios and datasets where to experiment and improve ML techniques.On the other side, the increase of computer processing power, thanks in particular to the use of graphic processing units, enabled ML approaches running in commodity hardware.This led to the development of new ML algorithms and their implementations through frameworks and libraries is extensive and growing [17].Thus the ML technology moved from an R&D phase, for the exclusive use of specialized laboratories, to a mature phase where it can be adopted in business applications.Mature technologies have to be easy to use for both nonexperts and professionals.One of the main bottlenecks towards a large use of the ML technology is related to the configuration of the systems, where experts are typically needed to set the large number of hyper-parameters.Furthermore, the selection of the algorithm that best performs in a given ML task is based on an experimental evaluation in which the performances of competing approaches are compared.This requires a time-consuming and expensive iterative process in which multiple alternative solutions are tested until an optimal result is achieved.To address these issues, automated machine learning (AutoML) tools have been proposed.These are user-friendly and easy-touse systems that provide a unified interface for the automatic selection of the most appropriate ML model/algorithm for a given task and its automatic configuration.Some examples are Auto-WEKA [12], AutoSklearn [9], AutoGluon [8], Auto-Keras [11], H20 AutoML [10], and many other.This paper analyzes the application of AutoML systems to Entity Matching (EM), i.e. the task of identifying which records in a dataset refer to the same real-world entity [5].Applications

The adoption of business intelligence technology in industries is growing rapidly. Business managers are not satisfied with ad hoc and static reports and they ask for more flexible and easy to use data analysis tools. Recently, application interfaces that expand the range of operations available to the user, hiding the underlying complexity, have been developed. The paper presents eLog, a business intelligence solution designed and developed in collaboration between the database group of the University of Modena and Reggio Emilia and eBilling, an Italian SME supplier of solutions for the design, production and automation of documentary processes for top Italian companies. eLog enables business managers to define OLAP reports by means of a web interface and to customize analysis indicators adopting a simple meta-language. The framework translates the user's reports into MDX queries and is able to automatically select the data cube suitable for each query. Over 140 medium and large companies have exploited the technological services of eBilling S.p.A. to manage their documents flows. In particular, eLog services have been used by the major media and telecommunications Italian companies and their foreign annex, such as Sky, Media set, H3G, Tim Brazil etc. The largest customer can provide up to 30 millions mail pieces within 6 months (about 200 GB of data in the relational DBMS). In a period of 18 months, eLog could reach 150 millions mail pieces (1 TB of data) to handle.

In this article, we present MELIS (Meaning Elicitation and Lexical Integration System), a method and a software tool for enabling an incremental process of automatic annotation of local schemas (e.g. relational database schemas, directory trees) with lexical information. The distinguishing and original feature of MELIS is the incremental process: the higher the number of schemas which are processed, the more background/ domain knowledge is cumulated in the system (a portion of domain ontology is learned at every step), the better the performance of the systems on annotating new schemas. MELIS has been tested as a component of the MOMIS-Ontology Builder, a framework able to create a domain ontology representing a set of selected data sources, described with a standard W3C language wherein concepts and attributes are annotated according to the lexical reference database. We describe the MELIS component within the MOMIS-Ontology Builder framework and provide some experimental results of MELIS as a standalone tool and as a component integrated in MOMIS.

In this paper we present TUCUXI, an intelligent hunter agent that replaces traditional keywords-based queries on the Web with a user-provided domain ontology, where meanings to be searched are not ambiguous. TUCUXI judges the relevance of the retrieved pages by matching the domain ontology against a simplified, but semantically rich, document representation (Map of Meanings). The Map of Meanings extraction involves the Lexical Chaining technique, from the Natural Language Processing (NLP) research field.

The increasing of globalization and flexibility required to the companies has generated, in the last decade, new issues, related to the managing of large scale projects within geographically distributed networks and to the cooperation of enterprises. ICT support systems are required to allow enterprises to share information, guarantee data-consistency and establish synchronized and collaborative processes. In this paper, we present a collaborative project management system that integrates data coming from aerospace industries with two main goals: avoiding inconsistencies generated by updates at the sources' level and minimizing data replications. The proposed system is composed of a collaborative project management component supported by a Web interface, a multiagent data integration component, which supports information sharing and querying, and SOAP enabled Web-services which ensure the whole interoperability of the software components. The system was developed by the University of Modena and Reggio Emilia, Gruppo Formula S.p.A. and Alenia Spazio S.p.A. within the EU WINK Project (Web-linked Integration of Network based Knowledge - IST-2000-28221).

Information overloading introduced by the large amount of data that is spread over the Internet must be faced in an appropriate way. The dynamism and the uncertainty of the Internet, along with the heterogeneity of the sources of information are the two main challenges for today's technologies related to information management. In the area of information integration, this paper proposes an approach based on mobile software agents integrated in the MOMIS (Mediator envirOnment for Multiple Information Sources) infrastructure, which enables semi-automatic information integration to deal with the integration and query of multiple, heterogeneous information sources (relational, object, XML and semi-structured sources). The exploitation of mobile agents in MOMIS can significantly increase the flexibility of the system. In fact, their characteristics of autonomy and adaptability well suit the distributed and open environments, such as the Internet. The aim of this paper is to show the advantages of the introduction in the MOMIS infrastructure of intelligent and mobile software agents for the autonomous management and coordination of integration and query processing over heterogeneous data sources.

ODB-QOPTIMIZER is a ODMG 93 compliant tool for the schema validation and semantic query optimization. The approach is based on two fundamental ingredients. The first one is the OCDL description logics (DLs) proposed as a common formalism to express class descriptions, a relevant set of integrity constraint rules (IC rules) and queries. The second one are DLs inference techniques, exploited to evaluate the logical implications expressed by IC rules and thus to produce the semantic expansion of a given query.