Computing the semantic similarity between pairs of terms plays a vital role within a myriad of shared data applications, such as data integration and ontology evolution. A first step towards building such applications is to determine which terms are semantically similar to each other. One feasible way to compute the similarity of two terms is to assess their word similarity by exploiting different knowledge resources, e.g., ontologies or domain corpora. Recently, information-theoretic approaches have shown promising results by computing the information content of concepts from the knowledge provided by ontologies. In these methods, the Most Informative Common Subsumers MICSs of two concepts play an important role in determining their similarity. While intuitive, surprisingly, these approaches often do not mirror human judgement well. In this paper, we investigate the effect of choosing a suitable subsumer, called Consensus Common Subsumer (CCS) among all common ancestors of two concepts, on the quality of the term similarity assessment. We cast this issue as an optimization problem by adopting the Particle Swarm Optimization algorithm as one of the most capable optimization approaches. An empirical evaluation based on well-established biomedical benchmarks and ontologies illustrates the accuracy of the proposed approach compared to state-of-the-art approaches.

The increasing volumes of data produced by high-throughput instruments coupled with advanced computational infrastructures for scientific computing have enabled what is often called a Fourth Paradigm for scientific research based on the exploration of large datasets. Current scientific research is often interdisciplinary, making data integration a critical technique for combining data from different scientific domains. Research data management is a critical part of this paradigm, through the proposition and development of methods, techniques, and practices for managing scientific data through their life cycle. Research on microbial communities follows the same pattern of production of large amounts of data obtained, for instance, from sequencing organisms present in environmental samples. Data on microbial communities can come from a multitude of sources and can be stored in different formats. For example, data from metagenomics, metatranscriptomics, metabolomics, and biological imaging are often combined in studies. In this article, we describe the design and current state of implementation of an integrative research data management framework for the Cluster of Excellence Balance of the Microverse aiming to allow for data on microbial communities to be more easily discovered, accessed, combined, and reused. This framework is based on research data repositories and best practices for managing workflows used in the analysis of microbial communities, which includes recording provenance information for tracking data derivation.

Reproducibility is a topic that has received significant attention in recent years. Despite being considered a fundamental factor in the scientific process, recent surveys have shown the difficulty of reproducing already published works, which impacts scientists’ ability to verify, validate, and reuse research findings. Recording provenance data is one of the approaches that can help to mitigate the challenges involved in the reproducibility process. When semantically well defined, provenance can describe the entire process involved in producing a given result. Additionally, the use of semantic web technologies can allow for the provenance data to be machine-actionable. With a focus on computational experiments, this work presents a package for collecting and describing provenance data from R scripts using the REPRODUCE-ME ontology to describe the path taken to produce results. We describe the package implementation process and demonstrate how it can help describe the story of experiments defined as R scripts to support reproducibility.

Dataset Retrieval is gaining importance due to a large amount of research data and the great demand for reusing scientific data. Dataset Retrieval is mostly based on metadata, structured information about the primary data. Enriching these metadata with semantic annotations based on Linked Open Data (LOD) enables datasets, publications and authors to be connected and expands the search on semantically related terms. In this work, we introduce the BiodivTagger, an ontology-based Information Extraction pipeline, developed for metadata from biodiversity research. The system recognizes biological, physical and chemical processes, environmental terms, data parameters and phenotypes as well as materials and chemical compounds and links them to concepts in dedicated ontologies. To evaluate our pipeline, we created a gold standard of 50 metadata files (QEMP corpus) selected from five different data repositories in biodiversity research. To the best of our knowledge, this is the first annotated metadata corpus for biodiversity research data. The results reveal a mixed picture. While materials and data parameters are properly matched to ontological concepts in most cases, some ontological issues occurred for processes and environmental terms.

Over the last few years, constructing knowledge graphs for new domains and linking them to existing ones has gained significant attention, especially in domains which have experienced a tremendous increase in available data such as biodiversity research. To this end, in this paper, we introduce a new semantic data mining-based approach to support the (semi-)automatic generation of a biodiversity knowledge graph. The proposed approach exploits and links information from several biodiversity-related resources, including the Encyclopedia of Life (EOL), the Global Biodiversity Information Facility (GBIF), and the Global Biotic Interactions (GLOBI). In particular, we adopt a data mining technique to extract association rules that support the construction of an initial species interactions knowledge graph. We then make use of available biodiversity resources to enrich the knowledge graph. We believe that this graph will support scientists from the biodiversity domain to gain new insights and enrich the data interoperability.

In an age where science is often interdisciplinary, it is frequently necessary to combine scientific data from different (sub-)disciplines and thus from different sources. Ontologies can play an important role in this integration process. However, existing ontologies will either cover just a part of the domain of interest or competing ontologies modeling the domain from different viewpoints exist. Therefore, before being able to leverage the power of ontologies, they themselves need to be integrated. The core of ontology integration is a mapping operation to identify corresponding concepts. This is a challenging task. To this end, we present a high-level integration workflow as a clear guideline for the whole process of ontology integration. We then analyze the mapping sub-workflow in more detail. We identify open issues in this integration step and discuss the ensuing challenges.

Existing data integration frameworks are poorly suited for the special requirements of scientists. To answer a specific research question, often, excerpts of data from different sources need to be integrated. The relevant parts and the set of underlying sources may differ from query to query. The analyses also oftentimes involve frequently changing data and exploratory querying. Additionally, The data sources not only store data in different formats, but also provide inconsistent data access functionality. The classic Extract-Transform-Load (ETL) approach seems too complex and time-consuming and does not fit well with interest and expertise of the scientists. With QUIS (QUery In-Situ), we provide a solution for this problem. QUIS is an open source heterogeneous in-situ data querying system. It utilizes a federated query virtualization approach that is built upon plugged-in adapters. QUIS takes a user query and transforms appropriate portions of it into the corresponding computation model on individual data sources and executes it. It complements the segments of the query that the target data sources can not execute. Hence, it guarantees full syntax and semantic support for its language on all data sources. QUIS's in-situ querying facility almost eliminates the time to prepare the data while maintaining a competitive performance and steady scalability. The present demonstration illustrates interesting features of the system: virtual Schemas, heterogeneous joins, and visual query results. We provide a realistic data processing scenario to examine the system's features. Users can interact with QUIS using its desktop workbench, command line interface, or from any R client including RStudio Server.

In complex and collaborative ecosystems like business networks, different partners need to share their respective expert knowledge in order to be successful. Due to the diversity of business applications and highly customized application suites used by business partners, knowledge exchange and the establishment of interoperability between enterprise applications is extremely difficult. Different representations and meanings of enterprise data lead to incomprehensibility between partners inside collaborative environments. This paper presents an model-driven approach to support sustainable interoperability for enterprise applications in collaborative environments. Based on an event-driven architecture, Knowledge Links enable dynamic modelling of knowledge transformations between knowledge domains. They keep background consistency between the connected domains, thus making enterprise applications interoperable. Knowledge Links can be created and modified at any time, which enables sustainable interoperability. Business partners are able to rely on their enterprise applications and don't need to switch to another system. Sensitive data stays covert due to the nature of modular ontologies. The presented approach is exemplified in the context of the OSMOSE Project and will be evaluated by the proof-of-concept scenarios in this Project.

We outline a blueprint for the development of new computer science approaches for the management and analysis of big data problems for biodiversity science. Such problems are characterized by a combination of different data sources each of which owns at least one of the typical characteristics of big data (volume, variety, velocity, or veracity). For these problems, we envision a solution that covers different aspects of integrating data sources and algorithms for their analysis on one of the following three layers: At the data layer, there are various data archives of heterogeneous, unstructured, and uncertain data. At the functional layer, the data are analyzed for each archive individually. At the meta-layer, multiple functional archives are combined for complex analysis.

Personalization nowadays is a commodity in a broad spectrum of computer systems. Examples range from online shops recommending products identified based on the user's previous purchases to web search engines sorting search hits based on the user browsing history. The aim of such adaptive behavior is to help users to find relevant content easier and faster. However, there are a number of negative aspects of this behavior. Adaptive systems have been criticized for violating the usability principles of direct manipulation systems, namely controllability, predictability, transparency, and unobtrusiveness. In this paper, we propose an approach to controlling adaptive behavior in recommender systems. It allows users to get an overview of personalization effects, view the user profile that is used for personalization, and adjust the profile and personalization effects to their needs and preferences. We present this approach using an example of a personalized portal for biochemical literature, whose users are biochemists, biologists and genomicists. Also, we report on a user study evaluating the impacts of controllable personalization on the usefulness, usability, user satisfaction, transparency, and trustworthiness of personalized systems.

Social learning has confirmed its value in enhancing the learning outcomes across a wide spectrum. To support social learning, a visual approach is a common technique to represent and organize multiple students' data in an informative way. This paper presents a design of comparative social visualization for E-learning, which encourages information discovery and social comparisons. Classroom studies confirmed the motivational impact of personalized social guidance provided by the visualization in the target context. The visualization encouraged students to do some work ahead of the course schedule. Moreover, class leaders provided an implicit social guidance for the rest of the class and successfully led the way to discover the most relevant resources creating good trails for the rest of the class. We summarized the evidence of students' engagement and performance through the social visualization interface.

The number of scientific publications available in multiple repositories is huge and rapidly growing. Accessing this information is of critical importance to conducting research and designing experiments. However, retrieving data of particular interest for a specific research field in such a large volume of publications is often like looking for a needle in a haystack. We present a web platform that supports researchers in navigating and curating biochemical literature. Our platform provides a single-point of access to abstracts of publications harvested from multiple databases and supports further analysis of these abstracts. It also allows users to obtain a personalized view of the literature and its semantic analysis results.

This paper presents Progressor, a visualization of open student models intended to increase the student's motivation to progress on educational content. The system visualizes not only the user's own model, but also the peers' models. It allows sorting the peers' models using a number of criteria, including the overall progress and the progress on a specific topic. Also, in this paper we present results of a classroom study confirming our hypothesis that by showing a student the peers' models and ranking them by progress it is possible to increase the student's motivation to compete and progress in e-learning systems.

In today's online markets, consumers need support in finding providers that offer the products or services they need and that are trustworthy. While Semantic Web Services (SWS) research addresses the first problem (discovering functionally suitable service providers), it neglects the second. Hence, several attempts have been made to complement service retrieval techniques based on semantic matchmaking with trust-establishing techniques that leverage collaborative consumer feedback. However, the diversity and multi-faceted nature of SWS impose special requirements on the underlying feedback mechanism, in particular w.r.t. their flexibility and expressiveness. Existing approaches only partially meet those requirements. In this paper, we will therefore propose a trust-establishing mechanism for Semantic Web Services that allows to assess a service provider's trustworthiness with respect to various service aspects and is flexible enough to adjust to various kinds of services and consumer requirements.

The Service Distribution Protocol (SDP) for Mobile Ad Hoc Networks (MANETs) is a service replication protocol which aims to increase the service availability despite the challenging nature of MANETs. SDP is an interest-based protocol which can achieve an efficient service replication process and produce a close to optimal service distribution over the network based on a variant service popularity. In this work, the assumptions made by the original authors of SDP in order to elaborate their protocol, are being discussed and deprecated from the perspective of utilizing the resources of servers. Moreover, the need to add new features for SDP to enable it to operate under more realistic assumptions - in particular limited server resources - are mentioned and analyzed. A new interest-based mechanism to manage the load of the SDP servers (providers) is introduced. Finally, based on a detailed simulation using the Opnet® Modeler® Wireless simulator, the proposed load balancing mechanism has shown promising results.

High service availability is difficult to achieve in mobile ad hoc networks. The service distribution protocol for mobile ad hoc networks increases the mobile service availability by employing coupled replication and hibernation mechanisms. In this paper, different hibernation behaviors are studied against the general performance of this protocol, especially the replica allocation correctness.

In recent years, a huge amount of research effort and funding has been devoted to the area of Semantic Web services (SWS). This has resulted in the proposal of numerous competing approaches to facilitate the automation of mediation, choreography, and discovery for Web services using semantic annotations. However, despite a wealth of theoretical work, too little effort has been spent toward the comparative experimental evaluation of the competing approaches so far. Progress in scientific development and industrial adoption is thereby hindered. An established evaluation methodology and standard benchmarks that allow the comparative evaluation of different frameworks are thus needed for the further advancement of the field. To this end, a criteria model for SWS evaluation is presented, and the existing approaches toward SWS evaluation are comprehensively analyzed. Their shortcomings are discussed in order to identify the fundamental issues of SWS evaluation. Based on this discussion, a research agenda toward agreed upon evaluation methodologies is proposed.

Automated matching of semantic service descriptions is the key to automatic service discovery and binding. But when trying to find a match for a certain request it may often happen, that the request cannot be serviced by a single offer but could be handled by combining existing offers. In this case automatic service composition is needed. Although automatic composition is an active field of research it is mainly viewed as a planning problem and treated separatedly from service discovery. In this paper we argue that an integrated approach to the problem is better than seperating these issues as is usually done. We propose an approach that integrates service composition into service discovery and matchmaking to match service requests that ask for multiple connected effects, discuss general issues involved in describing and matching such services and present an efficient algorithm implementing our ideas.

Handling highly dynamic scenarios as they arise in emergency situations requires lots of semantic information about the situation and an extremely flexible, selforganizing IT infrastructure that provides services that can be used to manage the situation. We show that a distributed meta level architecture is particularly suited for the implementation of such a self-organizing grid of services. This architecture (SOGOS) distinguishes between an object level and a meta level. The middleware processes of the grid are running on the object level. The meta level defines an explicitly and declaratively represented dynamic meta model that provides the semantics for the object level processes. Additionally, this level runs processes that plan, supervise and control mobile agents on the object level. The levels are linked together by reflection processes that ensure that relevant changes on the object level are reflected in the meta model and vice versa. The corresponding reflection principles provide the basis for the implementation of the selforganizing mechanisms that govern the overall system.

In this paper, we argue why service-orientation is the appropriate computing paradigm to use in mobile and wireless environments. We explain how the limitations of mobile devices can be overcome by functionality sharing via service-orientation. We identify some key areas that need to be addressed in order for this vision to become a reality. We survey existing approaches in these areas and discuss what is still missing.

Web services will only have advantages over existing technologies if the service binding can be performed dynamically. However, existing service description languages do not contain enough information for a computer agent to do the selection automatically during runtime on behalf of the user. This results from the fact that in most approaches the offer description language doubles as a re-guest language, which prevents the requestor from a precise formulation of requests and preferences. Therefore, in this paper, we emphasize the need for a distinguished service request language that allows to capture all of the requestor's preferences. We present a concrete technique to represent such preference-containing requests, which is based on fuzzy object sets.