For increasingly sophisticated use cases end users often need to extract, combine, and aggregate information from various (often dynamically generated) web pages from multiple websites. Current search engines do not focus on combining information from various web pages in order to answer the overall information need of the user. Semantic Web and Linked Data usually take a static view on the data and rely on providers' cooperation. In this paper, we present a novel approach that enables end users to easily extract data from web pages while they browse, store it locally in their browser as well as structure, integrate and search such data. We propose Datalog rules for integrating and searching the extracted data. We show how cleaning steps and integration rules can be reused to accelerate the cleaning and integration of extracted data. The proposed approach is implemented as a browser plugin. We present its implementation details and report on our evaluation of the plugin concerning user experience and browsing time saving.

Information search in the Web can become cumbersome if the desired information is scattered across multiple websites. For instance, even though there exist pages listing track chairs of the past ICWS conferences and web accessible bibliography databases, compiling the list of recent journal publications of the ICWS track chairs with the help of existing search engines is still a time consuming task. It is even harder to find information from the Deep Web as it requires user interactions that are hard to simulate by automatic crawlers. Semantic search based on structured data aims at efficiently answering information needs but relies on the cooperation of providers to be able to access their data. We aim at providing an alternative solution by introducing web browsing recipes that are goal-oriented end user browsing processes containing instructions for accessing, extracting, and merging (dynamic) information from various websites. Browsing recipes can be shared and reused to allow users to benefit from the browsing efforts of others. In order to achieve this goal, the development of efficient search techniques is the main prerequisite for effectively sharing and reusing recipes. In this paper we propose an efficient search technique for finding browsing recipes from large recipe repositories. Our search technique necessitates a structured query specifying the required information along with constraints on the structure of the browsing processes. We augment explicit state representation based model checking technique by indexing structures tailored to the requirements of information search based on the recipes. The performance evaluation of our approach reveals the impact of the indexing structures on the overall recipe search efficiency.

In the Web there are a large number of (business) services with complex behavior, such as e-commerce Web sites that require multiple interactions with the user, as well as an increasing number of Web automation scripts to coordinate the execution of multiple complex services. However, while there are a quite a few search techniques for atomic Web services, search techniques for complex services are still rare and only foundational. In this paper, we present \textit{behavior classes} that have formal semantics as well as human comprehensible names in order to foster usability of specification of constraints, and efficiency of search for complex services and processes. Our approach enables automatic methods for (i) assigning behavior classes to complex behavior descriptions, (ii) checking consistency of such a classification, and (iii) computing behavior class hierarchies. Furthermore, human comprehensible names for the behavior classes increase usability by allowing for shorter service descriptions and requests. Our evaluation results show that a behavior class hierarchy can be exploited as an indexing structure to gain performance of search.

Large amounts of data are being produced daily as detailed records of Web usage behavior, but the task of deriving actionable knowledge from them remains a challenge. Investigations of user browsing behavior at multiple websites, while more beneficial than studies restricted to a single site, still need to tackle the problems of information heterogeneity and mapping usage logs to meaningful events from the application domain. Focusing on the problem of modeling cross-site browsing behavior, we present a formalization approach based on a Web browsing Activity Model (WAM). We introduce a novel two-staged approach for the semantic enrichment of usage logs with domain knowledge, bringing together Semantic Web technologies and Machine Learning techniques. For learning the semantic types of logs, we present a supervised multi-class classification formulation, deploying structural Support Vector Machines with new sequential input features. We provide an implementation of these approaches and show the results of evaluation with real-world data.

We present a formal underpinning for Web service classes by viewing them as a set of services that fulfill a logical combination of constraints on functional and non-functional properties. A hierarchy of service classes is automatically derived by their formal definition and can be exploited for an efficient service retrieval. In addition, we show in this paper how service classes can be used (i) to create service descriptions without specifying precise property values and (ii) to create service requests that can use service classes to express ranges of desired property values.

Most of today's business processes are complex and consist of more than one party or single step procedures. In the Web, this is reflected by the existence of billions of Web sites, which may be regarded as complex processes, and on the other side only a few thousands of publicly available WSDL files that present single services. The availability of semantic descriptions of services and processes in the Web facilitates their discovery, as well as their composition into more complex workflows. It also facilitates the automatic execution of such workflows despite their heterogeneity. However, the deficit of semantic descriptions of Web processes deprives the users from using such sophisticated automatic techniques. The scope of our research is to fill this gap by providing semiautomatic techniques for the acquisition of a large number of semantic process descriptions on the (deep) Web. We model the data found in the online sources using ontologies, mine the process a user follows through the Web forms and generate a semantic description of this process. We present in this paper the implementation of our algorithms for the acquisition of process descriptions. We also provide a Web-based editor for manual annotation of new processes and refinement of the automatically-generated descriptions.

In order to find suitable web services in a large market of web services, automatic support is needed to filter out web services semantically. Existing matchmaking approaches mainly consider only the types of the input and output parameters, which is not sufficient in practical scenarios. In this paper, we present formalisms for modeling functional and non-functional properties of web services and for specifying user goals. We show how expressive web service descriptions can be checked for satisfiability of the user goal.

In order to find suitableWeb services from a large collection of Web services, automatic support is needed to filter out Web services relevant according to some criteria specified by the user. In real business scenarios constraints on the types of input and output parameters are often not sufficient. Rather one wishes to specify constraints on relationships of input and output parameters, interaction pattern and non-functional properties ofWeb services. Therefore, there is a need for a more expressive goal specification language. Current goal specification techniques for matchmaking and composition of Web services either lack expressivity to support real business scenarios or formal semantics to enable development of automatic algorithms. In this paper, we present a goal specification language that allow specifying constraints on functional and nonfunctional properties of Web services. The language is a novel combination of an expressive temporal logic ì- calculus and an expressive description logic SHIQ(D).

Web services help in achieving increased automation across organizational boundaries. In this paper, we present an approach for annotating WSDL documents with semantically rich descriptions. We also present an algorithm that considers such annotations in addition to just the types of input and output parameters. Our matchmaking algorithm not only returns match/no-match answers but in case of a match a set of conditions under which a Web service offers the desired functionality

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In this paper, we present a novel approach for specification of invocable semantic Web resources in a unified manner. Our approach enables specification of Web services, composite Web services, Web pages and Web sites with one single process description language in a unified manner. The basic idea behind our work is that different types of invocable Web resources differ mainly in the communication protocol and message formats. The language abstracts from various communication protocols and message formats and hence enables modeling of business logic without worrying much about technical details. Our approach in contrast to existing annotation techniques does not enforce the providers to modify their Web resources.

In this paper, we present an approach to enable access control for semantic Web services. Our approach builds on the idea of autonomous granting of access rights, decision making based on independent trust structures and respects privacy requirements of the users. Our framework allows the specification and computation of complex access control policies in a manageable and efficient way. Therefore, our approach is useful not only in Web services based applications (typically client-server architecture) but also in peer-to-peer and agent-based applications.