Semantic-driven matchmaking of web services using case-based reasoning

With the rapid proliferation of Web services as the medium of choice to securely publish application services beyond the firewall, the importance of accurate, yet flexible matchmaking of similar services gains importance both for the human user and for dynamic composition engines. In this paper, we present a novel approach that utilizes the case based reasoning methodology for modelling dynamic Web service discovery and matchmaking. Our framework considers Web services execution experiences in the decision making process and is highly adaptable to the service requester constraints. The framework also utilises OWL semantic descriptions extensively for implementing both the components of the CBR engine and the matchmaking profile of the Web services

Hypermedia-based discovery for source selection using low-cost linked data interfaces

Evaluating federated Linked Data queries requires consulting multiple sources on the Web. Before a client can execute queries, it must discover data sources, and determine which ones are relevant. Federated query execution research focuses on the actual execution, while data source discovery is often marginally discussed-even though it has a strong impact on selecting sources that contribute to the query results. Therefore, the authors introduce a discovery approach for Linked Data interfaces based on hypermedia links and controls, and apply it to federated query execution with Triple Pattern Fragments. In addition, the authors identify quantitative metrics to evaluate this discovery approach. This article describes generic evaluation measures and results for their concrete approach. With low-cost data summaries as seed, interfaces to eight large real-world datasets can discover each other within 7 minutes. Hypermedia-based client-side querying shows a promising gain of up to 50% in execution time, but demands algorithms that visit a higher number of interfaces to improve result completeness

Business Process Retrieval Based on Behavioral Semantics

This paper develops a framework for retrieving business processes considering search requirements based on behavioral semantics properties; it presents a framework called "BeMantics" for retrieving business processes based on structural, linguistics, and behavioral semantics properties. The relevance of the framework is evaluated retrieving business processes from a repository, and collecting a set of relevant business processes manually issued by human judges. The "BeMantics" framework scored high precision values (0.717) but low recall values (0.558), which implies that even when the framework avoided false negatives, it prone to false positives. The highest pre- cision value was scored in the linguistic criterion showing that using semantic inference in the tasks comparison allowed to reduce around 23.6 % the number of false positives. Using semantic inference to compare tasks of business processes can improve the precision; but if the ontologies are from narrow and specific domains, they limit the semantic expressiveness obtained with ontologies from more general domains. Regarding the perform- ance, it can be improved by using a filter phase which indexes business processes taking into account behavioral semantics propertie

Higher-Order Process Modeling: Product-Lining, Variability Modeling and Beyond

We present a graphical and dynamic framework for binding and execution of business) process models. It is tailored to integrate 1) ad hoc processes modeled graphically, 2) third party services discovered in the (Inter)net, and 3) (dynamically) synthesized process chains that solve situation-specific tasks, with the synthesis taking place not only at design time, but also at runtime. Key to our approach is the introduction of type-safe stacked second-order execution contexts that allow for higher-order process modeling. Tamed by our underlying strict service-oriented notion of abstraction, this approach is tailored also to be used by application experts with little technical knowledge: users can select, modify, construct and then pass (component) processes during process execution as if they were data. We illustrate the impact and essence of our framework along a concrete, realistic (business) process modeling scenario: the development of Springer's browser-based Online Conference Service (OCS). The most advanced feature of our new framework allows one to combine online synthesis with the integration of the synthesized process into the running application. This ability leads to a particularly flexible way of implementing self-adaption, and to a particularly concise and powerful way of achieving variability not only at design time, but also at runtime.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455

Semantic data mining and linked data for a recommender system in the AEC industry

Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations

From Artifacts to Aggregations: Modeling Scientific Life Cycles on the Semantic Web

In the process of scientific research, many information objects are generated, all of which may remain valuable indefinitely. However, artifacts such as instrument data and associated calibration information may have little value in isolation; their meaning is derived from their relationships to each other. Individual artifacts are best represented as components of a life cycle that is specific to a scientific research domain or project. Current cataloging practices do not describe objects at a sufficient level of granularity nor do they offer the globally persistent identifiers necessary to discover and manage scholarly products with World Wide Web standards. The Open Archives Initiative's Object Reuse and Exchange data model (OAI-ORE) meets these requirements. We demonstrate a conceptual implementation of OAI-ORE to represent the scientific life cycles of embedded networked sensor applications in seismology and environmental sciences. By establishing relationships between publications, data, and contextual research information, we illustrate how to obtain a richer and more realistic view of scientific practices. That view can facilitate new forms of scientific research and learning. Our analysis is framed by studies of scientific practices in a large, multi-disciplinary, multi-university science and engineering research center, the Center for Embedded Networked Sensing (CENS).Comment: 28 pages. To appear in the Journal of the American Society for Information Science and Technology (JASIST

Conceptual biology, hypothesis discovery, and text mining: Swanson's legacy

Innovative biomedical librarians and information specialists who want to expand their roles as expert searchers need to know about profound changes in biology and parallel trends in text mining. In recent years, conceptual biology has emerged as a complement to empirical biology. This is partly in response to the availability of massive digital resources such as the network of databases for molecular biologists at the National Center for Biotechnology Information. Developments in text mining and hypothesis discovery systems based on the early work of Swanson, a mathematician and information scientist, are coincident with the emergence of conceptual biology. Very little has been written to introduce biomedical digital librarians to these new trends. In this paper, background for data and text mining, as well as for knowledge discovery in databases (KDD) and in text (KDT) is presented, then a brief review of Swanson's ideas, followed by a discussion of recent approaches to hypothesis discovery and testing. 'Testing' in the context of text mining involves partially automated methods for finding evidence in the literature to support hypothetical relationships. Concluding remarks follow regarding (a) the limits of current strategies for evaluation of hypothesis discovery systems and (b) the role of literature-based discovery in concert with empirical research. Report of an informatics-driven literature review for biomarkers of systemic lupus erythematosus is mentioned. Swanson's vision of the hidden value in the literature of science and, by extension, in biomedical digital databases, is still remarkably generative for information scientists, biologists, and physicians. © 2006Bekhuis; licensee BioMed Central Ltd