Abstract This paper introduces an evolving cybersecurity knowledge graph that integrates and links critical information on real-world vulnerabilities, weaknesses and attack patterns from various publicly available sources. Cybersecurity constitutes a particularly interesting domain for the development of a domain-specific public knowledge graph, particularly due to its highly dynamic landscape characterized by time-critical, dispersed, and heterogeneous information. To build and continually maintain a knowledge graph, we provide and describe an integrated set of resources, including vocabularies derived from well-established standards in the cybersecurity domain, an ETL workflow that updates the knowledge graph as new information becomes available, and a set of services that provide integrated access through multiple interfaces. The resulting semantic resource offers comprehensive and integrated up-to-date instance information to security researchers and professionals alike. Furthermore, it can be easily linked to locally available information, as we demonstrate by means of two use cases in the context of vulnerability assessment and intrusion detection.

The growth of Linked Data has created a promising environment for data exploration and a growing number of tools allow users to interactively integrate data from various sources. Eliciting the reliability of the results of such ad-hoc integration processes, consistently recreating those results, and identifying changes upon re-execution, however, can be difficult. Automated process provenance trail creation can provide major benefits in this context, because (i) it enables users to trace the contribution of individual sources and processing steps to the final outcome and judge whether the result can be trusted; (ii) it ensures repeatability and raises the trustworthiness of results; (iii) it ideally enables reconstruction of Linked Data integration processes from the provenance information embedded in the final result. In this paper, we present a provenance model that facilitates automatic generation of semantic provenance information for generic Linked Data integration processes. We implement the generic model in a collaborative mashup environment and evaluate it by means of an example application. We find that the model provides a solid foundation for verifiability and contributes towards making Linked Data integration processes more open, transparent, and reusable, which is crucial in domains where the origin of data is essential, such as, for instance, statistical analyses, scientific research, and data journalism.

Despite an abundance of data available on the web today, satisfying users' complex information needs intelligently by automatically integrating and processing data from various sources remains challenging. In recent years, a large stream of research into mashups as a paradigm of end user development has emerged. These mashups foster combination and reuse of data and services and thereby allow end users to create novel applications. Developing such mashups efficiently and effectively, however, is still difficult for users that lack technical expertise. To address this issue, we extend a mashup platform with automatic mashup composition mechanisms and an agent that assists users in mashup design. To this end, we leverage semantics to simplify the mashup composition process on multiple levels. We associate each widget (i.e., mashup component) with a semantic model of inputs and outputs. These semantic models are helpful for identifying appropriate widgets in a given context and allow us to validate the links between widgets in a mashup. These validations provide the foundation for an advanced composition algorithm that automatically creates meaningful mashups from a given set of widgets. Finally, we develop an agent that leverages the semantic annotations to allow users to automatically compose mashups by entering natural-language text.

Today, public statistical data plays an increasingly important role both in public policy formation and as a facilitator for informed decision-making in the private sector. In line with to the increasing adoption of open data policies, the amount of data published by governments and organizations on the web is growing rapidly. To make such data more useful, the W3C has developed the RDF Data Cube Vocabulary to facilitate the publication of data in a more structured and interlinked manner. Although important first steps toward building a web of statistical linked datasets have been made, however, providing adequate facilities for end users to interactively explore and make use of the published data largely remains an unresolved challenge. This paper presents a widget-based approach to deal with this issue. In particular, we introduce a mashup platform that allows users without advanced skills and knowledge of Semantic Web technologies to interactively analyze datasets through widget compositions and visualizations. Furthermore, we provide mechanisms for the interconnection of datasets to support sophisticated knowledge extraction.

Whereas the number of open and accessible data sources on the web is growing rapidly, data becomes more heterogeneous and is difficult to use or reuse. Even though many national and international organizations have published their data according to the Linked Data principles, a considerable number of data sources is still available in traditional formats, e.g., HTML, XML, CSV, JSON. This results in a challenge of data aggregation and integration. To address this issue, we apply the visual programming paradigm to develop an open web platform. The platform is based on Semantic Web technologies and aims at encouraging and facilitating use of heterogeneous Open Data sources. We define Linked Widgets as user-driven modules which support users in accessing, processing, integrating, and visualizing different kinds of data. By connecting Linked Widgets from different developers, users without programming skills can compose and share ad-hoc applications that combine Open Data sources in a creative manner.