We have seen an increasing amount of interest in the application of Semantic Web technologies to Web services. The aim is to support automated discovery and composition of the services allowing seamless and transparent interoperability. In this paper we discuss three projects that are applying such technologies to bioinformatics: MOBY-Services and Semantic-MOBY. Through an examination of the di#erences and similarities between the solutions produced, we highlight some of the practical di#culties in developing Semantic Web services and suggest that the experiences with these projects have implications for the development of Semantic Web services as a whole.

The semantic Web Services vision requires that each service be annotated with semantic metadata. Manually creating such metadata is tedious and error-prone, and many software engineers, accustomed to tools that automatically generate WSDL, might not want to invest the additional e#ort. We therefore propose ASSAM, a tool that assists a user in creating semantic metadata for Web Services. ASSAM is intended for service consumers who want to integrate a number of services and therefore must annotate them according to some shared ontology. ASSAM is also relevant for service producers who have deployed a Web Service and want to make it compatible with an existing ontology. ASSAM's capabilities to automatically create semantic metadata are supported by two machine learning algorithms. First, we have developed an iterative relational classification algorithm for semantically classifying Web Services, their operations, and input and output messages. Second, to aggregate the data returned by multiple semantically related Web Services, we have developed a schema mapping algorithm that is based on an ensemble of string distance metrics.

Information integration systems combine data from multiple heterogeneous Web services to answer complex user queries, provided a user has semantically modeled the service first. To model a service, the user has to specify semantic types of the input and output data it uses and its functionality. As large number of new services come online, it is impractical to require the user to come up with a semantic model of the service or rely on the service providers to conform to a standard. Instead, we would like to automatically learn the semantic model of a new service. This paper addresses one part of the problem: namely, automatically recognizing semantic types of the data used by Web services. We describe a metadatabased classification method for recognizing input data types using only the terms extracted from a Web Service Definition file. We then verify the classifier’s predictions by invoking the service with some sample data of that type. Once we discover correct classification, we invoke the service to produce output data samples. We then use content-based classifiers to recognize semantic types of the output data. We provide performance results of both classification methods and validate our approach on several live Web services.

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 semiautomated at best. 1

Creating a Mashup, a web application that integrates data from multiple web sources to provide a unique service, involves solving multiple problems, such as extracting data from multiple web sources, cleaning it, and combining it together. Existing work relies on a widget paradigm where users address those problems during a Mashup building process by selecting, customizing, and connecting widgets together. While these systems claim that their users do not have to write a single line of code, merely abstracting programming methods into widgets has several disadvantages. First, as the number of widgets increases to support more operations, locating the right widget for the task can be confusing and time consuming. Second, customizing and connecting these widgets usually requires users to understand programming concepts. In this paper, we present a Mashup building approach that (a) combines most problem areas in Mashup building into a unified interactive framework that requires no widgets, and (b) allows users with no programming background to easily create Mashups by example.

Abstract. Emerging Web standards promise a network of heterogeneous yet interoperable Web Services. Web Services would greatly simplify the development of many kinds of data integration systems, information agents and knowledge management applications. Unfortunately, this vision requires that services provide substantial quantities of explicit semantic metadata “glue”. As a step to automatically generating such metadata, we present an algorithm that learns to attach semantic labels to Web forms, and evaluate our approach on a large collection real Web data. The key idea is to cast Web form classification as Bayesian learning and inference over a generative model of the Web form design process. 1

For the classification of relational data, iterative algorithms that feed back predicted labels of associated objects have been used. In this paper we show two extensions to existing approaches. First, we propose to use two separate classifiers for the intrinsic and the relational (extrinsic) attributes and vote their predictions. Second, we introduce a new way of exploiting the relational structure. When the extrinsic attributes alone are not su#cient to make a prediction, we train specialised classifiers on the intrinsic features and use the extrinsic features as a selector.

Abstract. Semantic annotations of web services can facilitate the discovery of services, as well as their composition into workflows. At present, however, the practical utility of such annotations is limited by the small number of service annotations available for general use. Resources for manual annotation are scarce, and therefore some means is required by which services can be automatically (or semi-automatically) annotated. In this paper, we show how information can be inferred about the semantics of operation parameters based on their connections to other (annotated) operation parameters within tried-and-tested workflows. In an open-world context, we can infer only constraints on the semantics of parameters, but these loose annotations are still of value in detecting errors within workflows, annotations and ontologies, as well as in simplifying the manual annotation task. 1

Emerging Semantic Web standards promise the automated discovery, composition and invocation of Web Services. Unfortunately, this vision requires that services describe themselves with large amounts of hand-crafted semantic metadata.

The Internet contains a very large number of information sources providing many types of data from weather forecasts to travel deals and financial information. These sources can be accessed via Web-forms, Web Services, RSS feeds and so on. In order to make automated use of these sources, we need to model them semantically, but writing semantic descriptions for Web Services is both tedious and error prone. In this paper we investigate the problem of automatically generating such models. We introduce a framework for learning Datalog definitions of Web sources. In order to learn these definitions, our system actively invokes the sources and compares the data they produce with that of known sources of information. It then performs an inductive logic search through the space of plausible source definitions in order to learn the best possible semantic model for each new source. In this paper we perform an empirical evaluation of the system using real-world Web sources. The evaluation demonstrates the effectiveness of the approach, showing that we can automatically learn complex models for real sources in reasonable time. We also compare our system with a complex schema matching system, showing that our approach can handle the kinds of problems tackled by the latter. 1.