Knowledge Graphs (KGs) are an emerging, highly flexible and Web-friendly technology for integrating, representing, and querying semi-structured data in a semantically rich model formalized by an Ontology. KGs may be built using specialized data management software (e.g., triplestores) or, by leveraging suitable mappings and query rewriting techniques, as “Virtual Knowledge Graph” (VKG) views over some legacy data source, such as a relational database. In this talk, we provide background information on VKGs and their underlying technologies, with particular emphasis on the open-source Ontop VKG engine, and we discuss ongoing research and development efforts towards their extension to Web APIs as a non-relational data source of practical relevance. This extension, supported by the HIVE and OntoCRM projects, would also enable transparent access to both static relational data and dynamically-computed Web API data as part of a regular VKG query.
We’re used to thinking of technology as always progressing, and in terms of hardware that’s definitely true: CPUs are getting more powerful, memory cheaper, networks faster, and so on, forever. But why does it feel like actually using computers is getting slower and shittier? Abundant computing resources and a culture of optimizing for developer productivity above all else have led to a situation where most hardware improvements are instantly negated by software inefficiency. This is a problem known from other domains (such as traffic flow), where increasing capacity is paradoxically not reducing but rather increasing the load on the system, resulting in a worse overall outcome. This talk discusses how we could approach this problem as a field, and makes some provocative recommendations — such as getting a worse computer.