Many current web pages include structured data which can directly be processed and used. Search engines, in particular, gather that structured data and provide question answering capabilities over the integrated data with an entity-centric presentation of the results. Due to the decentralized nature of the web, multiple structured data sources can provide similar information about an entity. But data from different sources may involve different vocabularies and modeling granularities, which makes integration difficult. We present FusE, an approach that identifies similar entity-specific data across sources, independent of the vocabulary and data modeling choices. We apply our method along the scenario of a trustable knowledge panel, conduct experiments in which we identify and process entity data from web sources, and compare the output to a competing system. The results underline the advantages of the presented entity-centric data fusion approach.

An increasing amount of applications build their functionality on the utilisation and manipulation of web resources. Consequently REST gains popularity with a resource-centric interaction architecture that draws its flexibility from links between resources. Linked Data offers a uniform data model for REST with self-descriptive resources that can be leveraged to avoid a manual ad-hoc development of web-based applications. For declaratively specifying interactions between web resources we introduce Data-Fu, a lightweight declarative rule language with state transition systems as formal grounding. Data-Fu enables the development of data-driven applications that facilitate the RESTful manipulation of read/write Linked Data resources. Furthermore, we describe an interpreter for Data-Fu as a general purpose engine that allows to perform described interactions with web resources by orders of magnitude faster than a comparable Linked Data processor.

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.

Wikipedia provides an interesting amount of text for more than hundred languages. This also includes languages where no reference corpora or other linguistic resources are easily available. We have extracted background language models built from the content of Wikipedia in various languages. The models generated from Simple and English Wikipedia are compared to language models derived from other established corpora. The differences between the models in regard to term coverage, term distribution and correlation are described and discussed. We provide access to the full dataset and create visualizations of the language models that can be used exploratory. The paper describes the newly released dataset for 33 languages, and the services that we provide on top of them.

The Web as a global information space is developing from a Web of documents to a Web of data. This development opens new ways for addressing complex information needs. Search is no longer limited to matching keywords against documents, but instead complex information needs can be expressed in a structured way, with precise answers as results. In this paper, we demonstrate Hermes, an infrastructure for data web search. To provide an end-user oriented interface, we support expressive keyword search by translating user information needs into structured queries. We integrate heterogeneous web data sources with automatically computed mappings. Schema-level mappings are exploited in constructing structured queries against the integrated schema. These structured queries are decomposed into queries against the local web data sources, which are then processed in a distributed way.

A key challenge for dynamic Web service selection is that Web services are typically highly configurable and service requesters often have dynamic preferences on service configurations. Current approaches, such as WS-Agreement, describe Web services by enumerating the various possible service configurations, an inefficient approach when dealing with numerous service attributes with large value spaces. We model Web service configurations and associated prices and preferences more compactly using utility function policies, which also allows us to draw from multi-attribute decision theory methods to develop an algorithm for optimal service selection. In this paper, we present an OWL ontology for the specification of configurable Web service offers and requests, and a flexible and extensible framework for optimal service selection that combines declarative logic-based matching rules with optimization methods, such as linear programming. Assuming additive price/preference functions, experimental results indicate that our algorithm introduces an overhead of only around 2 sec. compared to random service selection, while giving optimal results. The overhead, as percentage of total time, decreases as the number of offers and configurations increase.

In recent years, electronic markets have gained much attention as institutions to allocate goods and services efficiently between buyers and sellers. However, calculating suitable allocations between buyers and sellers in such markets can easily become very tricky, particularly if the services and goods involved are complex and described by multiple attributes. Since such trading objects typically provide various configurations with different corresponding prices, complex pricing functions and purchase preferences have to be taken into account when computing allocations. In this paper, we present a policy description framework that draws from utility theory to capture configurable products with multiple attributes. The framework thus allows the declarative description of seller pricing policies as well as buyer preferences over these configurations. As part of the framework, we present a machine-processible representation language for the policies and a method to evaluate different trading object configurations based on these policies. In addition, we show how policies can be aggregated and how the framework can be applied in a Web service selection scenario.

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

Wikipedia is the world's largest collaboratively edited source of encyclopaedic knowledge. But in spite of its utility, its contents are barely machine-interpretable. Structural knowledge, e.,g. about how concepts are interrelated, can neither be formally stated nor automatically processed. Also the wealth of numerical data is only available as plain text and thus can not be processed by its actual meaning.We provide an extension to be integrated in Wikipedia, that allows the typing of links between articles and the specification of typed data inside the articles in an easy-to-use manner.Enabling even casual users to participate in the creation of an open semantic knowledge base, Wikipedia has the chance to become a resource of semantic statements, hitherto unknown regarding size, scope, openness, and internationalisation. These semantic enhancements bring to Wikipedia benefits of today's semantic technologies: more specific ways of searching and browsing. Also, the RDF export, that gives direct access to the formalised knowledge, opens Wikipedia up to a wide range of external applications, that will be able to use it as a background knowledge base.In this paper, we present the design, implementation, and possible uses of this extension.

The Semantic Web augments the current WWW by giving information a well-defined meaning, better enabling computers and people to work in cooperation. This is done by adding machine understandable content to Web resources. Such added content is called metadata, whose semantics is provided by referring to an ontology---a domain's conceptualization agreed upon by a community. The Semantic Web relies on the complex interaction of several technologies involving ontologies. Therefore, sophisticated Semantic Web applications typically comprise more than one software module. Instead of coming up with proprietary solutions, developers should be able to rely on a generic infrastructure for application development in this context. We call such an infrastructure Application Server for the Semantic Web whose design and development are based on existing Application Servers. However, we apply and augment their underlying concepts for use in the Semantic Web and integrate semantic technology within the server itself. The article discusses requirements and design issues of such a server, presents our implementation KAON SERVER and demonstrates its usefulness by a detailed scenario.

In this paper we present a comprehensive approach for the refinement of ontology-based queries, which is based on incrementally (step-by-step) and interactively tailoring a query to the current information needs of a user, whereas these needs are implicitly and on-line elicited by analysing the user's behaviour during the searching process. The gap between a user's need and his query is quantified by measuring several types of query ambiguities. Consequently, in the refinement process a user is provided with a ranked list of refinements, which leads to a decrease of some of these ambiguities. Moreover, by exploiting the ontology background, the approach supports finding "similar" results that can help a user to satisfy his information need.

The Semantic Web aims at easy integration and usage of content by building on a semi-structured data model where data semantics are explicitly specified through ontologies. The use of ontologies in real-world applications such as community portals has shown that a new level of data independence is required for ontology-based applications. For example, the customization of information towards the needs of specific user communities is often needed. This paper extends previous work (2003) on this issue and presents a view language for the fundamental data models of the Semantic Web, viz. RDF and RDFS, and how it can be implemented. The basic novelty of the view language is the semantically appropriate classification of views into inheritance taxonomies based on query semantics. Additionally, the underlying distinction between unary predicates (classes) and binary predicates (properties) taken in RDF/S is maintained in the view language. So-called external ontologies allow the integration of multiple source databases, offer control over the publishing of data and enable the generation of views spanning across databases.

In this paper we present an approach to model ontology evolution as reconfiguration-design problem solving. The problem is reduced to a graph search where the nodes are evolving ontologies and the edges represent the changes that transform the source node into the target node. The search is guided by the constraints provided partially by a user and partially by a set of rules defining ontology consistency. In this way we allow a user to specify an arbitrary request declaratively and ensure its resolving. The approach is implemented in the KAON framework and the evaluation study shows its benefits.

The vision of the Semantic Web can only be realized through proliferation of well-known ontologies describing different domains. To enable interoperability in the Semantic Web, it will be necessary to break these ontologies down into smaller, well-focused units that may be reused. Currently, three problems arise in that scenario. Firstly, it is difficult to locate ontologies to be reused, thus leading to many ontologies modeling the same thing. Secondly, current tools do not provide means for reusing existing ontologies while building new ontologies. Finally, ontologies are rarely static, but are being adapted to changing requirements. Hence, an infrastructure for management of ontology changes, taking into account dependencies between ontologies is needed. In this paper we present such an infrastructure addressing the aforementioned problems.

The core idea of the Semantic Web is to make information accessible to human and software agents on a semantic basis. Hence, Web sites may feed directly from the Semantic Web exploiting the underlying structures for human and machine access. We have developed a domain-independent approach for developing semantic portals, viz. SEAL (SEmantic portAL), that exploits semantics for providing and accessing information at a portal as well as constructing and maintaining the portal. In this paper we focus on semantics-based means that make semantic Web sites accessible from the outside, i.e. semantics-based browsing, semantic querying, querying with semantic similarity, and machine access to semantic information. In particular, we focus on methods for acquiring and structuring community information as well as methods for sharing information.As a case study we refer to the AIFB portal - a place that is increasingly driven by Semantic Web technologies. We also discuss lessons learned from the ontology development of the AIFB portal..

Ontobroker applies Artificial Intelligence techniques to improve access to heterogeneous, distributed and semi-structured information sources as they are presented in the World Wide Web or organization-wide intranets. It relies on the use of ontologies to annotate web pages, formulate queries and derive answers. In this paper we will briefly sketch Ontobroker. Then we will discuss its main shortcomings, i.e. we will share the lessons we learned from our exercise. We will also show how On2broker overcomes these limitations. Most important is the separation of the query and inference engines and the integration of new web standards like XML and RDF.

A central research topic in the area of knowledge engineering is the reuse of problem-solving methods for developing knowledge based systems. For being able to reuse a problem-solving method it is important to know under which restrictions a problem-solving method is appropriate to solve a given problem. This paper describes the problem-solving method propose-and-revise as well as the way this problem-solving method searches in its problem space for a solution. A quantitative analysis of the efficiency of this search process is given. Additionally, task and domain specific properties and restrictions and their influence on the efficiency of the search process are considered. For these purposes an instance of the problem-solving method is transformed to a corresponding instance of a Stanford Research Institute Problem Solver (STRIPS) planning system. Then the problem-solving method is considered as an additional control strategy for such a planning system. By this way the various insights and analysis results which are available in the area of planning systems may be exploited for the analysis of problem-solving methods. ©1999 John Wiley & Sons, Inc.

The Knowledge Acquisition and Representation Language (KARL) combines a description of a knowledge based system at the conceptual level (a so called model of expertise) with a description at a formal and executable level. Thus, KARL allows the precise and unique specification of the functionality of a knowledge based system independent of any implementation details. A KARL model of expertise contains the description of domain knowledge, inference knowledge, and procedural control knowledge. For capturing these different types of knowledge, KARL provides corresponding modeling primitives based on Frame Logic and Dynamic Logic. A declarative semantics for a complete KARL model of expertise is given by a combination of these two types of logic. In addition, an operational definition of this semantics, which relies on a fixpoint approach, is given. This operational semantics defines the basis for the implementation of the KARL interpreter, which includes appropriate algorithms for efficiently executing KARL specifications. This enables the evaluation of KARL specifications by means of testing.

The logic-based knowledge representation language L/sub LILOG/, which is used to represent both the semantic background knowledge as well as the information extracted from German texts within the LILOG project, is discussed. L/sub LILOG/ integrates frame-like features-value descriptions used in computational linguistics into an order-sorted predicated logic framework. The basis design principles of L/sub LILOG/ and examples of how L/sub LILOG/ is used to model real world knowledge are presented. The implementation of the first LILOG prototype is described. A formal semantics definition is provided.>

For capturing various time aspects of an application within a conceptual model of information systems so-called Timed THM-Nets are proposed. THM-Nets integrate modeling concepts from semantic data models with modeling concepts from predicate/transition nets and provide a uniform framework for specifying static and dynamic aspects of an application. By introducing the notion of time into THM-Nets various time aspects may be modeled. Two of these time aspects are discussed and formally defined: the occurrence time of events and the handling of historical information.