The observational roots of reference of the semantic web

Shared reference is an essential aspect of meaning. It is also indispensable for the semantic web, since it enables to weave the global graph, i.e., it allows different users to contribute to an identical referent. For example, an essential kind of referent is a geographic place, to which users may contribute observations. We argue for a human-centric, operational approach towards reference, based on respective human competences. These competences encompass perceptual, cognitive as well as technical ones, and together they allow humans to inter-subjectively refer to a phenomenon in their environment. The technology stack of the semantic web should be extended by such operations. This would allow establishing new kinds of observation-based reference systems that help constrain and integrate the semantic web bottom-up

Semantic model-driven framework for validating quality requirements of Internet of Things streaming data

The rise of Internet of Things has provided platforms mostly enhanced by real-time data-driven services for reactive services and Smart Cities innovations. However, IoT streaming data are known to be compromised by quality problems, thereby influencing the performance and accuracy of IoT-based reactive services or Smart applications. This research investigates the suitability of the semantic approach for the run-time validation of IoT streaming data for quality problems. To realise this aim, Semantic IoT Streaming Data Validation with its framework (SISDaV) is proposed. The novel approach involves technologies for semantic query and reasoning with semantic rules defined on an established relationship with external data sources with consideration for specific run-time events that can influence the quality of streams. The work specifically targets quality issues relating to inconsistency, plausibility, and incompleteness in IoT streaming data. In particular, the investigation covers various RDF stream processing and rule-based reasoning techniques and effects of RDF Serialised formats on the reasoning process. The contributions of the work include the hierarchy of IoT data stream quality problem, lightweight evolving Smart Space and Sensor Measurement Ontology, generic time-aware validation rules and, SISDaV framework- a unified semantic rule-based validation system for RDF-based IoT streaming data that combines the popular RDF stream processing the system with generic enhanced time-aware rules. The semantic validation process ensures the conformance of the raw streaming data value produced by the IoT node(s) with IoT streaming data quality requirements and the expected value. This is facilitated through a set of generic continuous validation rules, which has been realised by extending the popular Jena rule syntax with a time element. The comparative evaluation of SISDaV is based on its effectiveness and efficiency based on the expressivity of the different serialised RDF data formats. The results are interpreted with relevant statistical estimations and performance metrics. The results from the evaluation approve of the feasibility of the framework in terms of containing the semantic validation process within the interval between reads of sensor nodes as well as provision of additional requirements that can enhance IoT streaming data processing systems which are currently missing in most related state-of-art RDF stream processing systems. Furthermore, the approach can satisfy the main research objectives as identified by the study

Cognitive Approaches for the Semantic Web (Dagstuhl Seminar 12221)

A major focus in the design of Semantic Web ontology languages used to be on finding a suitable balance between the expressivity of the language and the tractability of reasoning services defined over this language. This focus mirrors the original vision of a Web composed of machine readable and understandable data. Similarly to the classical Web a few years ago, the attention is recently shifting towards a user-centric vision of the Semantic Web. Essentially, the information stored on the Web is from and for humans. This new focus is not only reflected in the fast growing Linked Data Web but also in the increasing influence of research from cognitive science, human computer interaction, and machine-learning. Cognitive aspects emerge as an essential ingredient for future work on knowledge acquisition, representation, reasoning, and interactions on the Semantic Web. Visual interfaces have to support semantic-based retrieval and at the same time hide the complexity of the underlying reasoning machinery from the user. Analogical and similarity-based reasoning should assist users in browsing and navigating through the rapidly increasing amount of information. Instead of pre-defined conceptualizations of the world, the selection and conceptualization of relevant information has to be tailored to the user\u27s context on-the-fly. This involves work on ontology modularization and context-awareness, but also approaches from ecological psychology such as affordance theory which also plays an increasing role in robotics and AI. During the Dagstuhl Seminar 12221 we discussed the most promising ways to move forward on the vision of bringing findings from cognitive science to the Semantic Web, and to create synergies between the different areas of research. While the seminar focused on the use of cognitive engineering for a user-centric Semantic Web, it also discussed the reverse direction, i.e., how can the Semantic Web work on knowledge representation and reasoning feed back to the cognitive science community

Cognitive Approaches for the Semantic Web

A major focus in the design of Semantic Web ontology languages used to be on finding a suitable balance between the expressivity of the language and the tractability of reasoning services defined over this language. This focus mirrors the original vision of a Web composed of machine readable and understandable data. Similarly to the classical Web a few years ago, the attention is recently shifting towards a user-centric vision of the Semantic Web. Essentially, the information stored on the Web is from and for humans. This new focus is not only reflected in the fast growing Linked Data Web but also in the increasing influence of research from cognitive science, human computer interaction, and machine-learning. Cognitive aspects emerge as an essential ingredient for future work on knowledge acquisition, representation, reasoning, and interactions on the Semantic Web. Visual interfaces have to support semantic-based retrieval and at the same time hide the complexity of the underlying reasoning machinery from the user. Analogical and similarity-based reasoning should assist users in browsing and navigating through the rapidly increasing amount of information. Instead of pre-defined conceptualizations of the world, the selection and conceptualization of relevant information has to be tailored to the user\u27s context on-the-fly. This involves work on ontology modularization and context-awareness, but also approaches from ecological psychology such as affordance theory which also plays an increasing role in robotics and AI. During the Dagstuhl Seminar 12221 we discussed the most promising ways to move forward on the vision of bringing findings from cognitive science to the Semantic Web, and to create synergies between the different areas of research. While the seminar focused on the use of cognitive engineering for a user-centric Semantic Web, it also discussed the reverse direction, i.e., how can the Semantic Web work on knowledge representation and reasoning feed back to the cognitive science community