Big Structure: At The Nexus of Knowledge Bases, the Semantic Web and Artificial Intelligence | AI3:::Adaptive Information(About) Neither the semantic Web nor linked data has developed the practices, tooling or experience to actually interoperate data across the Web... Where it is compliant and from authoritative information sources, linked data can be a gold standard in data publishing. But, linked data is neither necessary nor essential, and may even be a diversion if it sucks the air from the room for what is more broadly useful.
Interoperability requires reference structures, what we are calling Big Structure.
Most analysts see firms that are actively pursuing data integration innovations as forward-thinking and more competitive.
The semantic Web, in our view, is properly understood as a sub-domain of artificial intelligence. Semantic technologies mesh smoothly with natural language tasks and objectives. But, as we noted in a recent review article, artificial intelligence is itself undergoing a renaissance. These advances are coming about because of the use of knowledge-based AI (KBAI), which combines knowledge bases with machine learning and other AI approaches. Natural language and spoken interfaces combined with background knowledge and a few machine-language utilities are what underlie Apple’s Siri, for example.
Multimedia Vocabularies on the Semantic Web(About) This document gives an overview on the state-of-the-art of multimedia metadata formats. Initially, practical relevant vocabularies for developers of Semantic Web applications are listed according to their modality scope. In the second part of this document, the focus is set on the integration of the multimedia vocabularies into the Semantic Web, that is to say, formal representations of the vocabularies are discussed.
Semantic Web Education and Outreach (SWEO) Interest Group(About) W3C is pleased to announce the launch of the Semantic Web Education and Outreach Interest Group, chaired by Susie Stephens (Oracle). The group is is chartered to collect proof-of-concept business cases, demonstration prototypes, etc, based on successful implementations of Semantic Web technologies, collect user experiences, develop and facilitate community outreach strategies, training and educational resources.
Stanford Knowledge Systems, AI Laboratory(About) KSL conducts research in the areas of knowledge representation and automated
reasoning in the Artificial Intelligence Laboratory of the Department of Computer Science at Stanford University. Current work focuses on enabling technology for the Semantic Web, hybrid reasoning, explaining answers from heterogeneous applications, deductive question-answering, representing and reasoning with multiple contexts, knowledge aggregation, ontology engineering, and knowledge-based technology for intelligence analysts and other knowledge workers.