Maurizio Vincini

Semantic integration of semistructured and structured data sources

Providing an integrated access to multiple heterogeneous sources is a challenging issue in global information systems for cooperation and interoperability. In this context, two fundamental problems arise. First, how to determine if the sources contain semantically related information, that is, information related to the same or similar real-world concept(s). Second, how to handle semantic heterogeneity to support integration and uniform query interfaces. Complicating factors with respect to conventional view integration techniques are related to the fact that the sources to be integrated already exist and that semantic heterogeneity occurs on the large-scale, involving terminology, structure, and context of the involved sources, with respect to geographical, organizational, and functional aspects related to information use. Moreover, to meet the requirements of global, Internet-based information systems, it is important that tools developed for supporting these activities are semi-automatic and scalable as much as possible. The goal of this paper is to describe the MOMIS [4, 5] (Mediator envirOnment for Multiple Information Sources) approach to the integration and query of multiple, heterogeneous information sources, containing structured and semistructured data. MOMIS has been conceived as a joint collaboration between University of Milano and Modena in the framework of the INTERDATA national research project, aiming at providing methods and tools for data management in Internet-based information systems. Like other integration projects [1, 10, 14], MOMIS follows a “semantic approach” to information integration based on the conceptual schema, or metadata, of the information sources, and on the following architectural elements: i) a common object-oriented data model, defined according to the ODL<subscrpt><italic>I</italic><supscrpt>3</supscrpt></subscrpt> language, to describe source schemas for integration purposes. The data model and ODL<subscrpt><italic>I</italic><supscrpt>3</supscrpt></subscrpt> have been defined in MOMIS as subset of the ODMG-93 ones, following the proposal for a standard mediator language developed by the <italic>I</italic><supscrpt>3</supscrpt>/POB working group [7]. In addition, ODL<subscrpt><italic>I</italic><supscrpt>3</supscrpt></subscrpt> introduces new constructors to support the semantic integration process [4, 5]; ii) one or more wrappers, to translate schema descriptions into the common ODL<subscrpt><italic>I</italic><supscrpt>3</supscrpt></subscrpt> representation; iii) a mediator and a query-processing component, based on two pre-existing tools, namely ARTEMIS [8] and ODB-Tools [3] (available on Internet at http://sparc20.dsi.unimo.it/), to provide an <italic>I</italic><supscrpt>3</supscrpt> architecture for integration and query optimization. In this paper, we focus on capturing and reasoning about semantic aspects of schema descriptions of heterogeneous information sources for supporting integration and query optimization. Both semistructured and structured data sources are taken into account [5]. A Common Thesaurus is constructed, which has the role of a shared ontology for the information sources. The Common Thesaurus is built by analyzing ODL<subscrpt><italic>I</italic><supscrpt>3</supscrpt></subscrpt> descriptions of the sources, by exploiting the Description Logics OLCD (Object Language with Complements allowing Descriptive cycles) [2, 6], derived from KL-ONE family [17]. The knowledge in the Common Thesaurus is then exploited for the identification of semantically related information in ODL<subscrpt><italic>I</italic><supscrpt>3</supscrpt></subscrpt> descriptions of different sources and for their integration at the global level. Mapping rules and integrity constraints are defined at the global level to express the relationships holding between the integrated description and the sources descriptions. ODB-Tools, supporting OLCD and description logic inference techniques, allows the analysis of sources descriptions for generating a consistent Common Thesaurus and provides support for semantic optimization of queries at the global level, based on defined mapping rules and integrity constraints.

Semantic integration of heterogeneous information sources

Abstract Developing intelligent tools for the integration of information extracted from multiple heterogeneous sources is a challenging issue to effectively exploit the numerous sources available on-line in global information systems. In this paper, we propose intelligent, tool-supported techniques to information extraction and integration from both structured and semistructured data sources. An object-oriented language, with an underlying Description Logic, called ODLI3, derived from the standard ODMG is introduced for information extraction. ODLI3 descriptions of the source schemas are exploited first to set a Common Thesaurus for the sources. Information integration is then performed in a semiautomatic way by exploiting the knowledge in the Common Thesaurus and ODLI3 descriptions of source schemas with a combination of clustering techniques and Description Logics. This integration process gives rise to a virtual integrated view of the underlying sources for which mapping rules and integrity constraints are specified to handle heterogeneity. Integration techniques described in the paper are provided in the framework of the MOMIS system based on a conventional wrapper/mediator architecture.

Synthesizing an integrated ontology

The Mediator Environment for Multiple Information Sources (Momis) supports semiautomatic building, annotation, and extension of domain ontologies.

An information integration framework for e-commerce

One of the main challenges for e-commerce infrastructure designers is to retrieve data from different sources and create a unified view that overcomes contradictions and redundancies. Virtual catalogs, such as the Momis project, can help synthesize data and present it in a unified manner to the customer.

ODB-Tools: A Description Logics Based Tool for Schema Validation and Semantic Query Optimization in Object Oriented Databases

ODB-Tools is a integrated environment for the object oriented database (OODB) validation, preserving taxonomy coherence and performing taxonomic inferences, and semantic query optimization. Semantic query optimization uses problem-specific knowledge (e.g. integrity constraints) for transforming a query into an equivalent one (i.e. with the same answer set) that may be answered more efficiently. The approach of the tool is based on two fundamental ingredients. The first one is the OCDL description logics proposed as a common formalism to express class descriptions, a relevant set of integrity constraints rules (IC rules) and queries. The second one are Description Logics inference techniques, exploited to evaluate the logical implications expressed by IC rules and thus to produce the semantic expansion of a given query. The optimizer tentatively applies all the possible transformations and delays the choice of beneficial transformation till the end. ODB-Tools is a ODMG 93 [1] compliant tool, both for the schema definition (ODL language) and for the query language (OQL), The tool is available in internet at http://sparc20.dsi.unimo.it and supports an on-line graphical interface developed in Java language.

A semantic approach to ETL technologies

Data warehouse architectures rely on extraction, transformation and loading (ETL) processes for the creation of an updated, consistent and materialized view of a set of data sources. In this paper, we support these processes by proposing a tool that: (1) allows the semi-automatic definition of inter-attribute semantic mappings, by identifying the parts of the data source schemas which are related to the data warehouse schema, thus supporting the extraction process; and (2) groups the attribute values semantically related thus defining a transformation function for populating with homogeneous values the data warehouse. Our proposal couples and extends the functionalities of two previously developed systems: the MOMIS integration system and the RELEVANT data analysis system. The system has been experimented within a real scenario concerning the creation of a data warehouse for enterprises working in the beverage and food logistic area. The results showed that the coupled system supports effectively the extraction and transformation processes.

An Incremental Method for the Lexical Annotation of Domain Ontologies

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.

Querying a super-peer in a schema-based super-peer network

We propose a novel approach for defining and querying a super-peer within a schema-based super-peer network organized into a two-level architecture: the low level, called the peer level (which contains a mediator node), the second one, called super-peer level (which integrates mediators peers with similar content). We focus on a single super-peer and propose a method to define and solve a query, fully implemented in the SEWASIE project prototype. The problem we faced is relevant as a super-peer is a two-level data integrated system, then we are going beyond traditional setting in data integration. We have two different levels of Global as View mappings: the first mapping is at the super-peer level and maps several Global Virtual Views (GVVs) of peers into the GVV of the super-peer; the second mapping is within a peer and maps the data sources into the GVV of the peer. Moreover, we propose an approach where the integration designer, supported by a graphical interface, can implicitly define mappings by using Resolution Functions to solve data conflicts, and the Full Disjunction operator that has been recognized as providing a natural semantics for data merging queries.

An Intelligent Data Integration Approach for Collaborative Project Management in Virtual Enterprises

The increasing globalization and flexibility required by companies has generated new issues in the last decade related to the managing of large scale projects and to the cooperation of enterprises within geographically distributed networks. ICT support systems are required to help enterprises 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 a main goal: to facilitate the activity of assembling, integration and the verification of a multi-enterprise project. The main achievement of the system from a data management perspective is to avoid inconsistencies generated by updates at the sources’ level and minimizes data replications. The developed system is composed of a collaborative project management component supported by a web interface, a multi-agent data integration system, which supports information sharing and querying, and web-services that ensure the interoperability of the software components.The system was developed by the University of Modena and Reggio Emilia, Gruppo Formula S.p.A. and tested by Alenia Spazio S.p.A. within the EU WINK Project (Web-linked Integration of Network based Knowledge—IST-2000-28221).

A peer-to-peer information system for the semantic web

Data integration, in the context of the web, faces new problems, due in particular to the heterogeneity of sources, to the fragmentation of the information and to the absence of a unique way to structure and view information. In such areas, the traditional paradigms, on which database foundations are based (i.e. client server architecture, few sources containing large information), have to be overcome by new architectures. The peer-to-peer (P2P) architecture seems to be the best way to fulfill these new kinds of data sources, offering an alternative to traditional client/server architecture. In this paper we present the SEWASIE system that aims at providing access to heterogeneous web information sources. An enhancement of the system architecture in the direction of P2P architecture, where connections among SEWASIE peers rely on exchange of XML metadata, is described.