The availability of vast amounts of visual data with diverse and fruitful features is a key factor for developing, verifying, and benchmarking advanced computer vision (CV) algorithms and architectures. Most visual datasets are created and curated for specific tasks or with limited data distribution for very specific fields of interest, and there is no unified approach to manage and access them across diverse sources, tasks, and taxonomies. This not only creates unnecessary overheads when building robust visual recognition systems, but also introduces biases into learning systems and limits the capabilities of data-centric AI. To address these problems, we propose the Vision Knowledge Graph (VisionKG), a novel resource that interlinks, organizes and manages visual datasets via knowledge graphs and Semantic Web technologies. It can serve as a unified framework facilitating simple access and querying of state-of-the-art visual datasets, regardless of their heterogeneous formats and taxonomies. One of the key differences between our approach and existing methods is that VisionKG is not only based on metadata but also utilizes a unified data schema and external knowledge bases to integrate, interlink, and align visual datasets. It enhances the enrichment of the semantic descriptions and interpretation at both image and instance levels and offers data retrieval and exploratory services via SPARQL and natural language empowered by Large Language Models (LLMs). VisionKG currently contains 617 million RDF triples that describe approximately 61 million entities, which can be accessed at https://vision.semkg.org and through APIs. With the integration of 37 datasets and four popular computer vision tasks, we demonstrate its usefulness across various scenarios when working with computer vision pipelines.

Anomaly detection is a fundamental problem that consists of identifying irregular patterns that do not conform to the expected behavior of a system or the generated data. Many anomaly detection techniques have been proposed for time series data. However, selecting the most suitable detection method remains challenging as the proposed techniques widely vary in performance. The appropriate choice of a detection method impacts many properties of mission-critical applications such as in monitoring a patient's health, where anomalies are inevitable but need to be detected securely. In this demo, we present a new evaluator that allows to peruse the performance of several anomaly detection techniques and supports practitioners in understanding the behavior and (dis-)advantages of each technique for a given dataset. In a simple and well-structured way, practitioners can specify the desired anomaly detection setup, and our system would tune the parameters of each technique and analyze their properties in an easily understandable report. The tool also allows recommending the most appropriate technique for each anomaly type and evaluation metric.

The timely, traceable and provenance-aware publication of Linked Open Data (LOD) is crucial for its success and to fulfill the vision of a global, decentralized, and machine-readable database of knowledge. Yet, the access to LOD is still fragmented and mainly centralized aggregations are being used, relying on complex harvesting mechanisms. As a remedy, we propose a blockchain-based approach enabling an integrated, traceable, and timely view on LOD. We use a blockchain to meet the organizational requirements of publishing LOD in a decentralized fashion while still supporting the sovereignty of the data providers and supporting provenance and proper integration into a harmonized knowledge graph. We present an approach and an implemented system that fulfills the requirements regarding volume and throughput and can be used as the foundation for practical deployments. We use Linked Open Government Data (LOGD) as our case study to demonstrate the feasibility of our approach. We developed a prototype to address the specific requirements of LOGD publication and apply the Practical Byzantine Fault Tolerance algorithm at its core to enable a robust state replication.

Abstract The publication and (re)utilization of Open Data is still facing multiple barriers on technical, organizational and legal levels. This includes limitations in interfaces, search capabilities, provision of quality information and the lack of definite standards and implementation guidelines. Many Semantic Web specifications and technologies are specifically designed to address the publication of data on the web. In addition, many official publication bodies encourage and foster the development of Open Data standards based on Semantic Web principles. However, no existing solution for managing Open Data takes full advantage of these possibilities and benefits. In this paper, we present our solution “Piveau”, a fully-fledged Open Data management solution, based on Semantic Web technologies. It harnesses a variety of standards, like RDF, DCAT, DQV, and SKOS, to overcome the barriers in Open Data publication. The solution puts a strong focus on assuring data quality and scalability. We give a detailed description of the underlying, highly scalable, service-oriented architecture, how we integrated the aforementioned standards, and used a triplestore as our primary database. We have evaluated our work in a comprehensive feature comparison to established solutions and through a practical application in a production environment, the European Data Portal. Our solution is available as Open Source.

The proliferation of heterogeneous Linked Data on the Web poses new challenges to database systems. In particular, the capacity to store, track, and query provenance data is becoming a pivotal feature of modern triplestores. We present methods extending a native RDF store to efficiently handle the storage, tracking, and querying of provenance in RDF data. We describe a reliable and understandable specification of the way results were derived from the data and how particular pieces of data were combined to answer a query. Subsequently, we present techniques to tailor queries with provenance data. We empirically evaluate the presented methods and show that the overhead of storing and tracking provenance is acceptable. Finally, we show that tailoring a query with provenance information can also significantly improve the performance of query execution.

The Semantic Web represents an evolution of the World Wide Web towards one of entities and their relationships, rather than pages and links. Such a progression makes it possible to represent, integrate, query and reason about structured online data. Recent years have witnessed tremendous growth of mobile computing, represented by the widespread adoption of smart phones and tablets. The versatility of such smart devices and the capabilities of semantic technologies form a great foundation for a ubiquitous Semantic Web that will contribute to further realising the true potential of both disciplines. In this paper, the authors argue for values provided by the ubiquitous Semantic Web using a mobile service discovery scenario. They also provide a brief overview of state-of-the-art research in this emerging area. Finally, the authors conclude with a summary of challenges and important research problems.

Despite the services of sophisticated search engines, there are interesting information sources which are useful but largely inaccessible to current web users. These sources are often ad-hoc, location-specific and only useful for users over short periods of time, or relate to tacit knowledge of users or crowds. The solution presented in this paper introduces an integrated concept of "location" and "presence" across the physical and virtual worlds enabling ad-hoc socializing of users looking for similar information. While the definition of presence in the physical world is straightforward their definitions in the virtual world are neither obvious nor trivial. We provide an integrated spatial model spanning both worlds which enables us to define presence of users in a unified way. This integrated model allows us to enable ad-hoc socializing of users browsing the Web with users in the physical world specific to their joint information needs and allows us to unlock the untapped information sources mentioned above. We describe our proof-of-concept implementation and provide an empirical analysis based on real-world experiments.

The investigation of the browsing behavior of users has been a topic of active research since the Web started. However, new online services changed the meaning behind "browsing the Web" and require a fresh look at the problem. Platforms such as YouTube or last. Fm have started to replace the traditional media channels (cinema, television, radio) and media distribution formats (CD, DVD, Blu-ray). Particularly social networks (e.g., Facebook) attracted whole new, particularly less tech-savvy audiences. Advances in mobile technologies made browsing "on-the-move" the norm and changed the user behavior, often being influenced by the user's location and context in the physical world. Commonly used datasets, such as web server access logs or search engines transaction logs, are inherently not capable of capturing the browsing behavior of users in all these facets. DOBBS (DERI Online Behavior Study) is an effort to create such a dataset in a non-intrusive, completely anonymous and privacy-preserving way. DOBBS provides a browser add-on which keeps track of users' browsing behavior. In this paper, we outline the motivation behind DOBBS, describe the add-on and dataset, and present some first results to highlight the strengths of DOBBS.

The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations — the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.

In this paper we present a replication strategy to improve data availability in P2P Networks. The focus of the paper is to replicate data to nodes which are highly available and complement one another in terms of uptimes. This would decrease the replication management overhead until the number of replicas falls to a certain threshold. Replication to reliable node would improve the cost of replication by avoiding irregular nodes of the network. We run Monte Carlo simulation based on past traces of Kad, OverNet, Bittorrent and PlanetLab, to present how our replication keeps data sustainable in the network. In our evaluation we show that a life pattern along with the availability of nodes improves overall data availability. We perform our evaluation on a Kademlia network, and show that our approach reduces over head compared to existing approaches to data availability.

We identify the gap that currently exists between enterprise and consumer-focused mashup tools. We describe how Sqwelch, a semantically-enabled mashup maker, addresses this gap during the design of mashups and in their execution. Sqwelch enables the composition of mashups based on the concept of trust explicitly specified by users through a visual interface. Taxonomies are used to enable lightweight mediation of payloads delivered through a publish/subscribe mechanism. We demonstrate the use of Sqwelch as a proof of concept in the remote delivery of healthcare, and how we have used Sqwelch to address areas of trust and collaboration in the delivery of telehealth services.

The use of RDF data published on the Web for applications is still a cumbersome and resource-intensive task due to the limited software support and the lack of standard programming paradigms to deal with everyday problems such as combination of RDF data from dierent sources, object identifier consolidation, ontology alignment and mediation, or plain querying and filtering tasks. In this paper we present a framework, Semantic Web Pipes, that supports fast implementation of Semantic data mash-ups while preserving desirable properties such as abstraction, encapsulation, component-orientation, code re-usability and maintainability which are common and well supported in other application areas.

Structured P2P overlays supporting standard database functionalities are a popular choice for building large-scale distributed data management systems. In such systems, estimating the number of answers for structured queries can help approximating query completeness, but is especially challenging. In this paper, we propose to use routing graphs in order to achieve this. We introduce the general approach and briefly discuss further aspects like overhead and guarantees.

A crucial prerequisite for the deployment and success of Peer-to-Peer data management applications is the availability of metadata in a way that makes it easy to access and combine data from different sources and domains.

Control and data dependencies are important information in business processes that supports process modeling, analysis, and execution. However, sequencing constraints, which are prescribed by control structures, obfuscate the true sources of dependencies. In addition, most work improperly equalizes sequencing constraint and control dependency, and regards data dependencies as a flow of data processing relying on sequencing constraints.In this paper, business activities are described with a semantic description that defines precondition, effect, input, and output. Based on which we specify what control and data dependencies are. Control dependencies are related to the precondition and the effect. Mandatory data dependencies are related to the input and the output, while optional data dependencies are derived from possible conditions on business activities. All control and data dependencies are optimized into a minimal dependency graph which captures essential dependencies to be preserved. A sequencing constraint is possibly due to control and/or data dependencies. A clear view on the relation and the difference between sequencing constraint and control/data dependency is crucial to better support process modeling, analysis, and execution.

The idea of collecting and combining large public data sets and services became more and more popular. The special characteristics of such systems and the requirements of the participants demand for strictly decentralized solutions. However, this comes along with several ambitious challenges a corresponding system has to overcome. In this demonstration paper, we present a lightweight distributed universal storage capable of dealing with those challenges, and providing a powerful and flexible way of building Internet-scale public data management systems. We introduce our approach based on a triple storage on top of a distributed hash table (DHT) overlay system, based on the ideas of a universal relation model and the resource description framework (RDF), and outline solved challenges as well as open issues.

We present an extensible and customizable framework for the autonomous discovery of Semantic Web services based on their QoS properties. Using semantic technologies, users can specify the QoS matching model and customize the ranking of services flexibly according to their preferences. The formal modeling of the discovery process as a query execution plan facilitates the introduction of different discovery algorithms and the automatic generation of parallelized matchmaking evaluations. This enables adapting our approach to unpredictable arrival rates of user queries and scales up to high numbers of published service descriptions.

With the price of wireless sensor technologies diminishing rapidly we can expect large numbers of autonomous sensor networks being deployed in the near future. These sensor networks will typically not remain isolated but the need of interconnecting them on the network level to enable integrated data processing will arise, thus realizing the vision of a global "sensor Internet." This requires a flexible middleware layer which abstracts from the underlying, heterogeneous sensor network technologies and supports fast and simple deployment and addition of new platforms, facilitates efficient distributed query processing and combination of sensor data, provides support for sensor mobility, and enables the dynamic adaption of the system configuration during runtime with minimal (zero-programming) effort. This paper describes the global sensor networks (GSN) middleware which addresses these goals. We present GSN's conceptual model, abstractions, and architecture, and demonstrate the efficiency of the implementation through experiments with typical high-load application profiles. The GSN implementation is available from http://gsn.sourceforge.net/.

Different ontologies used in semantic web services fields raise numerous interoperation and communication problems with respect to service discovery, composition, and execution. The current approaches for ontology mediation often failed due to their lack of sufficient semantic expressiveness and reasoning capability. In this paper1, we present a novel approach allowing ontologies to provide self-contained semantics for service applications. We show how desired application ontologies can be generated using a new merging algorithm for service ontologies. We also show some experimental results and compare them to the output of the PROMPT ontology merging tool.

Identification is an essential building block for many services in distributed information systems. The quality and purpose of identification may differ, but the basic underlying problem is always to bind a set of attributes to an identifier in a unique and deterministic way. Name/directory services, such as DNS, X.500, or UDDI, are a well-established concept to address this problem in distributed information systems. However, none of these services addresses the specific requirements of peer-to-peer systems with respect to dynamism, decentralization, and maintenance. We propose the implementation of directories using a structured peer-to-peer overlay network and apply this approach to support self-contained maintenance of routing tables with dynamic IP addresses in structured P2P systems. Thus, we keep routing tables intact without affecting the organization of the overlay networks, making it logically independent of the underlying network infrastructure. Even though the directory is self-referential, since it uses its own service to maintain itself, we show that it is robust due to a self-healing capability. For security, we apply a combination of PGP-like public key distribution and a quorum-based query scheme. We describe the algorithm as implemented in the P-Grid P2P lookup system (http:// www.p-grid.org/) and give a detailed analysis and simulation results demonstrating the efficiency and robustness of our approach.

This paper describes a novel approach for obtaining semantic interoperability among data sources in a bottom-up, semi-automatic manner without relying on pre-existing, global semantic models. We assume that large amounts of data exist that have been organized and annotated according to local schemas. Seeing semantics as a form of agreement, our approach enables the participating data sources to incrementally develop global agreement in an evolutionary and completely decentralized process that solely relies on pair-wise, local interactions: Participants provide translations between schemas they are interested in and can learn about other translations by routing queries (gossiping). To support the participants in assessing the semantic quality of the achieved agreements we develop a formal framework that takes into account both syntactic and semantic criteria. The assessment process is incremental and the quality ratings are adjusted along with the operation of the system. Ultimately, this process results in global agreement, i.e., the semantics that all participants understand. We discuss strategies to efficiently find translations and provide results from a case study to justify our claims. Our approach applies to any system which provides a communication infrastructure (existing websites or databases, decentralized systems, P2P systems) and offers the opportunity to study semantic interoperability as a global phenomenon in a network of information sharing parties.