Improving Semantic Web Services Discovery Using SPARQL-Based Repository Filtering

Semantic Web Services discovery is commonly a heavyweight task, which has scalability issues when the number of services or the ontology complexity increase, because most approaches are based on Description Logics reasoning. As a higher number of services becomes available, there is a need for solutions that improve discovery performance. Our proposal tackles this scalability problem by adding a preprocessing stage based on two SPARQL queries that filter service repositories, discarding service descriptions that do not refer to any functionality or non-functional aspect requested by the user before the actual discovery takes place. This approach fairly reduces the search space for discovery mechanisms, consequently improving the overall performance of this task. Furthermore, this particular solution does not provide yet another discovery mechanism, but it is easily applicable to any of the existing ones, as our prototype evaluation shows. Moreover, proposed queries are automatically generated from service requests, transparently to the user. In order to validate our proposal, this article showcases an application to the OWL-S ontology, in addition to a comprehensive performance analysis that we carried out in order to test and compare the results obtained from proposed filters and current discovery approaches, discussing the benefits of our proposal

On the Combination of Textual and Semantic Descriptions for Automated Semantic Web Service Classification

Abstract Semantic Web services have emerged as the solution to the need for automating several aspects related to service-oriented architectures, such as service discovery and composition, and they are realized by combining Semantic Web technologies and Web service standards. In the present paper, we tackle the problem of automated classification of Web services according to their application domain taking into account both the textual description and the semantic annotations of OWL-S advertisements. We present results that we obtained by applying machine learning algorithms on textual and semantic descriptions separately and we propose methods for increasing the overall classification accuracy through an extended feature vector and an ensemble of classifiers

Enhanced matching engine for improving the performance of semantic web service discovery

Web services are the means to realize the Service Oriented Architecture (SOA) paradigm. One of the key tasks of the Web services is discovery also known as matchmaking. This is the act of locating suitable Web services to fulfill a specific goal and adding semantic descriptions to the Web services is the key to enabling an automated, intelligent discovery process. Current Semantic Web service discovery approaches are primarily classified into logic-based, non-logic-based and hybrid categories. An important challenge yet to be addressed by the current approaches is the use of the available constructs in Web service descriptions to achieve a better performance in matchmaking. Performance is defined in terms of precision and recall as well-known metrics in the information retrieval field. Moreover, when matchmaking a large number of Web services, maintaining a reasonable execution time becomes a crucial challenge. In this research, to address these challenges, a matching engine is proposed. The engine comprises a new logic-based and nonlogic- based matchmaker to improve the performance of Semantic Web service discovery. The proposed logic-based and non-logic-based matchmakers are also combined as a hybrid matchmaker for further improvement of performance. In addition, a pre-matching filter is used in the matching engine to enhance the execution time of matchmaking. The components of the matching engine were developed as prototypes and evaluated by benchmarking the results against data from the standard repository of Web services. The comparative evaluations in terms of performance and execution time highlighted the superiority of the proposed matching engine over the existing and prominent matchmakers. The proposed matching engine has been proven to enhance both the performance and execution time of the Semantic Web service discovery

Semantic web service search: a brief survey

Scalable means for the search of relevant web services are essential for the development of intelligent service-based applications in the future Internet. Key idea of semantic web services is to enable such applications to perform a high-precision search and automated composition of services based on formal ontology-based representations of service semantics. In this paper, we briefly survey the state of the art of semantic web service search

WSMO-Lite and hRESTS: lightweight semantic annotations for Web services and RESTful APIs

Service-oriented computing has brought special attention to service description, especially in connection with semantic technologies. The expected proliferation of publicly accessible services can benefit greatly from tool support and automation, both of which are the focus of Semantic Web Service (SWS) frameworks that especially address service discovery, composition and execution. As the first SWS standard, in 2007 the World Wide Web Consortium produced a lightweight bottom-up specification called SAWSDL for adding semantic annotations to WSDL service descriptions. Building on SAWSDL, this article presents WSMO-Lite, a lightweight ontology of Web service semantics that distinguishes four semantic aspects of services: function, behavior, information model, and nonfunctional properties, which together form a basis for semantic automation. With the WSMO-Lite ontology, SAWSDL descriptions enable semantic automation beyond simple input/output matchmaking that is supported by SAWSDL itself. Further, to broaden the reach of WSMO-Lite and SAWSDL tools to the increasingly common RESTful services, the article adds hRESTS and MicroWSMO, two HTML microformats that mirror WSDL and SAWSDL in the documentation of RESTful services, enabling combining RESTful services with WSDL-based ones in a single semantic framework. To demonstrate the feasibility and versatility of this approach, the article presents common algorithms for Web service discovery and composition adapted to WSMO-Lite

Semantics-aware planning methodology for automatic web service composition

Service-Oriented Computing (SOC) has been a major research topic in the past years. It is based on the idea of composing distributed applications even in heterogeneous environments by discovering and invoking network-available Web Services to accomplish some complex tasks when no existing service can satisfy the user request. Service-Oriented Architecture (SOA) is a key design principle to facilitate building of these autonomous, platform-independent Web Services. However, in distributed environments, the use of services without considering their underlying semantics, either functional semantics or quality guarantees can negatively affect a composition process by raising intermittent failures or leading to slow performance. More recently, Artificial Intelligence (AI) Planning technologies have been exploited to facilitate the automated composition. But most of the AI planning based algorithms do not scale well when the number of Web Services increases, and there is no guarantee that a solution for a composition problem will be found even if it exists. AI Planning Graph tries to address various limitations in traditional AI planning by providing a unique search space in a directed layered graph. However, the existing AI Planning Graph algorithm only focuses on finding complete solutions without taking account of other services which are not achieving the goals. It will result in the failure of creating such a graph in the case that many services are available, despite most of them being irrelevant to the goals. This dissertation puts forward a concept of building a more intelligent planning mechanism which should be a combination of semantics-aware service selection and a goal-directed planning algorithm. Based on this concept, a new planning system so-called Semantics Enhanced web service Mining (SEwsMining) has been developed. Semantic-aware service selection is achieved by calculating on-demand multi-attributes semantics similarity based on semantic annotations (QWSMO-Lite). The planning algorithm is a substantial revision of the AI GraphPlan algorithm. To reduce the size of planning graph, a bi-directional planning strategy has been developed

Linked Open Data - Creating Knowledge Out of Interlinked Data: Results of the LOD2 Project

Database Management; Artificial Intelligence (incl. Robotics); Information Systems and Communication Servic

An evaluation methodology and framework for semantic web services technology

Software engineering has been driven over decades by the trend towards component based development and loose coupling. Service oriented architectures and Web Services in particular are the latest product of this long-reaching development. Semantic Web Services (SWS) apply the paradigms of the Semantic Web to Web Services to allow more flexible and dynamic service usages. Numerous frameworks to realize SWS have been put forward in recent years but their relative advantages and general maturity are not easy to assess. This dissertation presents a solution to this issue. It defines a general methodology and framework for SWS technology evaluation as well as concrete benchmarks to assess the functional scope and performance of various approaches. The presented benchmarks have been executed within international evaluation campaign. The thesis thus comprehensively covers theoretical, methodological as well as practical results regarding the evaluation and assessment of SWS technologies

Semantic search and composition in unstructured peer-to-peer networks

This dissertation focuses on several research questions in the area of semantic search and composition in unstructured peer-to-peer (P2P) networks. Going beyond the state of the art, the proposed semantic-based search strategy S2P2P offers a novel path-suggestion based query routing mechanism, providing a reasonable tradeoff between search performance and network traffic overhead. In addition, the first semantic-based data replication scheme DSDR is proposed. It enables peers to use semantic information to select replica numbers and target peers to address predicted future demands. With DSDR, k-random search can achieve better precision and recall than it can with a near-optimal non-semantic replication strategy. Further, this thesis introduces a functional automatic semantic service composition method, SPSC. Distinctively, it enables peers to jointly compose complex workflows with high cumulative recall but low network traffic overhead, using heuristic-based bidirectional haining and service memorization mechanisms. Its query branching method helps to handle dead-ends in a pruned search space. SPSC is proved to be sound and a lower bound of is completeness is given. Finally, this thesis presents iRep3D for semantic-index based 3D scene selection in P2P search. Its efficient retrieval scales to answer hybrid queries involving conceptual, functional and geometric aspects. iRep3D outperforms previous representative efforts in terms of search precision and efficiency.Diese Dissertation bearbeitet Forschungsfragen zur semantischen Suche und Komposition in unstrukturierten Peer-to-Peer Netzen(P2P). Die semantische Suchstrategie S2P2P verwendet eine neuartige Methode zur Anfrageweiterleitung basierend auf Pfadvorschlägen, welche den Stand der Wissenschaft übertrifft. Sie bietet angemessene Balance zwischen Suchleistung und Kommunikationsbelastung im Netzwerk. Außerdem wird das erste semantische System zur Datenreplikation genannt DSDR vorgestellt, welche semantische Informationen berücksichtigt vorhergesagten zukünftigen Bedarf optimal im P2P zu decken. Hierdurch erzielt k-random-Suche bessere Präzision und Ausbeute als mit nahezu optimaler nicht-semantischer Replikation. SPSC, ein automatisches Verfahren zur funktional korrekten Komposition semantischer Dienste, ermöglicht es Peers, gemeinsam komplexe Ablaufpläne zu komponieren. Mechanismen zur heuristischen bidirektionalen Verkettung und Rückstellung von Diensten ermöglichen hohe Ausbeute bei geringer Belastung des Netzes. Eine Methode zur Anfrageverzweigung vermeidet das Feststecken in Sackgassen im beschnittenen Suchraum. Beweise zur Korrektheit und unteren Schranke der Vollständigkeit von SPSC sind gegeben. iRep3D ist ein neuer semantischer Selektionsmechanismus für 3D-Modelle in P2P. iRep3D beantwortet effizient hybride Anfragen unter Berücksichtigung konzeptioneller, funktionaler und geometrischer Aspekte. Der Ansatz übertrifft vorherige Arbeiten bezüglich Präzision und Effizienz

Semantic search and composition in unstructured peer-to-peer networks

This dissertation focuses on several research questions in the area of semantic search and composition in unstructured peer-to-peer (P2P) networks. Going beyond the state of the art, the proposed semantic-based search strategy S2P2P offers a novel path-suggestion based query routing mechanism, providing a reasonable tradeoff between search performance and network traffic overhead. In addition, the first semantic-based data replication scheme DSDR is proposed. It enables peers to use semantic information to select replica numbers and target peers to address predicted future demands. With DSDR, k-random search can achieve better precision and recall than it can with a near-optimal non-semantic replication strategy. Further, this thesis introduces a functional automatic semantic service composition method, SPSC. Distinctively, it enables peers to jointly compose complex workflows with high cumulative recall but low network traffic overhead, using heuristic-based bidirectional haining and service memorization mechanisms. Its query branching method helps to handle dead-ends in a pruned search space. SPSC is proved to be sound and a lower bound of is completeness is given. Finally, this thesis presents iRep3D for semantic-index based 3D scene selection in P2P search. Its efficient retrieval scales to answer hybrid queries involving conceptual, functional and geometric aspects. iRep3D outperforms previous representative efforts in terms of search precision and efficiency.Diese Dissertation bearbeitet Forschungsfragen zur semantischen Suche und Komposition in unstrukturierten Peer-to-Peer Netzen(P2P). Die semantische Suchstrategie S2P2P verwendet eine neuartige Methode zur Anfrageweiterleitung basierend auf Pfadvorschlägen, welche den Stand der Wissenschaft übertrifft. Sie bietet angemessene Balance zwischen Suchleistung und Kommunikationsbelastung im Netzwerk. Außerdem wird das erste semantische System zur Datenreplikation genannt DSDR vorgestellt, welche semantische Informationen berücksichtigt vorhergesagten zukünftigen Bedarf optimal im P2P zu decken. Hierdurch erzielt k-random-Suche bessere Präzision und Ausbeute als mit nahezu optimaler nicht-semantischer Replikation. SPSC, ein automatisches Verfahren zur funktional korrekten Komposition semantischer Dienste, ermöglicht es Peers, gemeinsam komplexe Ablaufpläne zu komponieren. Mechanismen zur heuristischen bidirektionalen Verkettung und Rückstellung von Diensten ermöglichen hohe Ausbeute bei geringer Belastung des Netzes. Eine Methode zur Anfrageverzweigung vermeidet das Feststecken in Sackgassen im beschnittenen Suchraum. Beweise zur Korrektheit und unteren Schranke der Vollständigkeit von SPSC sind gegeben. iRep3D ist ein neuer semantischer Selektionsmechanismus für 3D-Modelle in P2P. iRep3D beantwortet effizient hybride Anfragen unter Berücksichtigung konzeptioneller, funktionaler und geometrischer Aspekte. Der Ansatz übertrifft vorherige Arbeiten bezüglich Präzision und Effizienz