What are the hot topics of research in Semantic Web?

Lately I have seen Provenance being more actively researched by academics in USA and Europe. I also see Big data+Linked data as the emerging area. What are the other topics/areas of research in Semantic Web?

Off the top of my head, some topics that I see as trending:

  • Scalable reasoning: reasoning has always been a hot topic in the SW community, but now there is increasing demand for reasoners that can scale into the billions of triples and handle messy data. Distributed reasoning has been a hot topic for a while, as have new languages (like the profiles of OWL), and ways to deal with provenance.
  • Live/decentralised querying: centralised public endpoints often groan under the amount of data and users they try to support. Furthermore, their local indexes can often become stale or out of date. Live querying techniques look to get data directly from sources at runtime. Sources can be another endpoint that closer to the raw data (e.g., federation), or even just documents themselves (e.g., link traversal).
  • SPARQL performance: also, there has been increased demand for high performance SPARQL engines. This continues to be a hot topic, and I would expect to see a lot of papers tackling scalability and performance aspects of SPARQL 1.1's new features.
  • Instance matching / entity matching / consolidation / linking: we now have lots of data on the Web about all sorts of instances. As such, mechanisms to (semi-)automatically link descriptions of related (or possibly even equivalent) resources is becoming more and more important (more so, I feel, than the more traditional ontology matching field).
  • Linked Data "science": How can we interact with Linked Data? How can we consume it? How can be link it? How dynamic is it? How big is it? How useful is it? How correct is it? What kind of quality can we expect? Linked Data topics are seeing more and more attention.
  • Provenance/trust: As you say, provenance gains attention. RDF often involves gathering lots of data from lots of sources, so provenance often is key.
  • Policies/authentication: Not all data is open, esp. in key areas like enterprises or "smart cities". Policies and authentication mechanisms are being proposed that use rules or RDF to represent policies, and control access to data annotated with control mechanisms.
  • Semantic sensor streams: If more and more devices are connected to the internet, how can semantic tech. help to make sense of the data streams they produce? How can we reason and query over such data? Topics include temporal reasoning, window-based querying/continuous SPARQL querying, etc.
  • Machine Learning & combining inductive/deductive methods: Perhaps a little bit more vague, but for a long time, machine learning and related techniques have been applied at large scale to make sense or extract "knowledge" from messy, unstructured corpora. With resources like Linked Data and topics like Big Data growing, there is now renewed interest in how to combine deductive reasoning with inductive/statistical/heuristic methods in various areas.