Avigdor Gal

A framework for modeling and evaluating automatic semantic reconciliation

Abstract.The introduction of the Semantic Web vision and the shift toward machine understandable Web resources has unearthed the importance of automatic semantic reconciliation. Consequently, new tools for automating the process were proposed. In this work we present a formal model of semantic reconciliation and analyze in a systematic manner the properties of the process outcome, primarily the inherent uncertainty of the matching process and how it reflects on the resulting mappings. An important feature of this research is the identification and analysis of factors that impact the effectiveness of algorithms for automatic semantic reconciliation, leading, it is hoped, to the design of better algorithms by reducing the uncertainty of existing algorithms. Against this background we empirically study the aptitude of two algorithms to correctly match concepts. This research is both timely and practical in light of recent attempts to develop and utilize methods for automatic semantic reconciliation.

Managing uncertainty in schema matching with top-k schema mappings

In this paper, we propose to extend current practice in schema matching with the simultaneous use of top-K schema mappings rather than a single best mapping. This is a natural extension of existing methods (which can be considered to fall into the top-1 category), taking into account the imprecision inherent in the schema matching process. The essence of this method is the simultaneous generation and examination of K best schema mappings to identify useful mappings. The paper discusses efficient methods for generating top-K methods and propose a generic methodology for the simultaneous utilization of top-K mappings. We also propose a concrete heuristic that aims at improving precision at the cost of recall. We have tested the heuristic on real as well as synthetic data and anlyze the emricial results. The novelty of this paper lies in the robust extension of existing methods for schema matching, one that can gracefully accommodate less-than-perfect scenarios in which the exact mapping cannot be identified in a single iteration. Our proposal represents a step forward in achieving fully automated schema matching, which is currently semi-automated at best.

Automatic ontology matching using application semantics

We propose the use of application semantics to enhance the process of semantic reconciliation. Application semantics involves those elements of business reasoning that affect the way concepts are presented to users: their layout, and so on. In particular, we pursue in this article the notion of precedence, in which temporal constraints determine the order in which concepts are presented to the user. Existing matching algorithms use either syntactic means (such as term matching and domain matching) or model semantic means, the use of structural information that is provided by the specific data model to enhance the matching process. The novelty of our approach lies in proposing a class of matching techniques that takes advantage of ontological structures and application semantics. As an example, the use of precedence to reflect business rules has not been applied elsewhere, to the best of our knowledge. We have tested the process for a variety of web sites in domains such as car rentals and airline reservations, and we share our experiences with precedence and its limitations.

Complex event processing over uncertain data

In recent years, there has been a growing need for active systems that can react automatically to events. Some events are generated externally and deliver data across distributed systems, while others are materialized by the active system itself. Event materialization is hampered by uncertainty that may be attributed to unreliable data sources and networks, or the inability to determine with certainty whether an event has actually occurred. Two main obstacles exist when designing a solution to the problem of event materialization with uncertainty. First, event materialization should be performed efficiently, at times under a heavy load of incoming events from various sources. The second challenge involves the generation of a correct probability space, given uncertain events. We present a solution to both problems by introducing an efficient mechanism for event materialization under uncertainty. A model for representing materialized events is presented and two algorithms for correctly specifying the probability space of an event history are given. The first provides an accurate, albeit expensive method based on the construction of a Bayesian network. The second is a Monte Carlo sampling algorithm that heuristically assesses materialized event probabilities. We experimented with both the Bayesian network and the sampling algorithms, showing the latter to be scalable under an increasing rate of explicit event delivery and an increasing number of uncertain rules (while the former is not). Finally, our sampling algorithm accurately and efficiently estimates the probability space.

An authorization model for temporal and derived data: securing information portals

The term information portals refers to Web sites that serve as main providers of focused information, gathered from distributed data sources. Gathering and disseminating information through information portals introduce new security challenges. In particular, the authorization specifications, as well as the granting process, are temporal by nature. Also, more often than not, the information provided by the portal is in fact derived from more than one backend data source. Therefore, any authorization model for information portals should support access control based on temporal characteristics of the data, and also should provide tools to prevent indirect unauthorized access through the use of derived data. In this article we focus our attention on devising such an authorization model. The distinguishing features of this model include: (1) the specification of authorizations based on temporal characteristics of data, and (2) a formal framework to derive authorizations in a consistent and safe manner, based on relationships among data.

Uncertain Schema Matching

Schema matching is the task of providing correspondences between concepts describing the meaning of data in various heterogeneous, distributed data sources. Schema matching is one of the basic operations required by the process of data and schema integration, and thus has a great effect on its outcomes, whether these involve targeted content delivery, view integration, database integration, query rewriting over heterogeneous sources, duplicate data elimination, or automatic streamlining of workflow activities that involve heterogeneous data sources. Although schema matching research has been ongoing for over 25 years, more recently a realization has emerged that schema matchers are inherently uncertain. Since 2003, work on the uncertainty in schema matching has picked up, along with research on uncertainty in other areas of data management. This lecture presents various aspects of uncertainty in schema matching within a single unified framework. We introduce basic formulations of uncertainty and provide several alternative representations of schema matching uncertainty. Then, we cover two common methods that have been proposed to deal with uncertainty in schema matching, namely ensembles, and top-K matchings, and analyze them in this context. We conclude with a set of real-world applications.

Efficient Processing of Uncertain Events in Rule-Based Systems

There is a growing need for systems that react automatically to events. While some events are generated externally and deliver data across distributed systems, others need to be derived by the system itself based on available information. Event derivation is hampered by uncertainty attributed to causes such as unreliable data sources or the inability to determine with certainty whether an event has actually occurred, given available information. Two main challenges exist when designing a solution for event derivation under uncertainty. First, event derivation should scale under heavy loads of incoming events. Second, the associated probabilities must be correctly captured and represented. We present a solution to both problems by introducing a novel generic and formal mechanism and framework for managing event derivation under uncertainty. We also provide empirical evidence demonstrating the scalability and accuracy of our approach.

Boosting Schema Matchers

Schema matching is recognized to be one of the basic operations required by the process of data and schema integration, and thus has a great impact on its outcome. We propose a new approach to combining matchers into ensembles, called Schema Matcher Boosting ( SMB ). This approach is based on a well-known machine learning technique, called boosting. We present a boosting algorithm for schema matching with a unique ensembler feature, namely the ability to choose the matchers that participate in an ensemble. SMB introduces a new promise for schema matcher designers. Instead of trying to design a perfect schema matcher that is accurate for all schema pairs, a designer can focus on finding better than random schema matchers. We provide a thorough comparative empirical results where we show that SMB outperforms, on average, any individual matcher. In our experiments we have compared SMB with more than 30 other matchers over a real world data of 230 schemata and several ensembling approaches, including the Meta-Learner of LSD. Our empirical analysis shows that SMB improves, on average, over the performance of individual matchers. Moreover, SMB is shown to be consistently dominant, far beyond any other individual matcher. Finally, we observe that SMB performs better than the Meta-Learner in terms of precision, recall and F-Measure.

A semantic approach to approximate service retrieval

Web service discovery is one of the main applications of semantic Web services, which extend standard Web services with semantic annotations. Current discovery solutions were developed in the context of automatic service composition. Thus, the “client” of the discovery procedure is an automated computer program rather than a human, with little, if any, tolerance to inexact results. However, in the real world, services which might be semantically distanced from each other are glued together using manual coding. In this article, we propose a new retrieval model for semantic Web services, with the objective of simplifying service discovery for human users. The model relies on simple and extensible keyword-based query language and enables efficient retrieval of approximate results, including approximate service compositions. Since representing all possible compositions and all approximate concept references can result in an exponentially-sized index, we investigate clustering methods to provide a scalable mechanism for service indexing. Results of experiments, designed to evaluate our indexing and query methods, show that satisfactory approximate search is feasible with efficient processing time.

A generic integration architecture for cooperative information systems

Cooperative information systems consist of existing legacy systems integrated in terms of a generic architecture which supports data integration and coordination among the integrated components. The paper presents a proposal for a generic integration architecture named CoopWARE. The architecture is presented in terms of the mechanisms it provides for data integration, and coordination. Data integration is supported by an information repository with an extensible schema, while coordination is facilitated by a rule set and an event-driven rule execution mechanism. In addition, the paper describes implementation and application experiences for the architecture in the context of a three year software engineering project.