Amy Guy

Raw Blog

Monday, July 08, 2013

[Notes] Frank van Harmelen at #SSSW2013

Semantic Web & Web of data = a more manageable mission.
Metaweb movie - got bought by Google and incorporated into Knowledge Graph.

SW Principles:
1. Give everything a name (entities).
2. Relations form graph between things.
3. Names are addresses on the Web (so we inherit properties of Web like AAA).

This becomes Giant Global Graph.  (Maybe SW should be called Giant Global Graph?)

4. Add semantics.

  • Types of things, relationships.
  • Hierarchy, constraints:
    • Inferences.  Bounding shared beliefs by sharing ontological information.  Space for confusion gets smaller and we begin to agree on interpretation of information.
Semantics = predictable inference.

Google: from just links to results, to information boxes (last May).  Can't directly address Google Knowledge Graph.
NXP (microprocessors): 26,000 products. Integrated all databases into triplestore.  Exposing subset of triplestore to customers.
BBC: 125 million triples.  Many data sources.  APIs to website.  Own ontologies.

All have the same triple-layer architecture:

Raw data
SW layer
Output / API / UI etc

DataGov: eg. air quality in cities, campaign money, if policies work.

Companies don't care about SW, but are using these technologies for their own IRL purposes.

These are all different types of use cases of SW technologies:

  • search;
  • data integration;
  • content re-use;
  • SEO;
  • data publishing.

It's important that the SW graph is so big.

  • More questions to ask.
  • Good that we no longer know how big, or how fast it is growing... Tens of billions of facts.
    • How many are really permanent?
    • Some are stable, some will disappear - just like the 'regular' Web.
      • " being a mess is the only reason why it scales."
We need to get used to the idea of SW being a mess - aka "a system so large you can no longer enforce central control" (complex system).

The LD cloud is still poorly interconnected, but good graph properties.

Heterogeneity is unavoidable.
Socio-economic, first to market - why certain systems/ontologies get used, eg., dbpedia.

LD cloud grew, nobody designed it.
Knowledge follows power curve.  This has an impact on mapping and reasoning, storage and indexing.

Web not geared for distributed SPARQL queries.  Everyone pulls in all data and queries local copy.  Not very 'webby', disadvantageous.  So subgraphs?  Query planning?  Caching?  Payload priority?

Representation, (re)construction.  Metametadata (knowledge about knowledge; uncertainty; problems with vocabs for this).
How to get from provenance to trust.

Dynamics (change).
Cool Web in 60 seconds graphic.
SW not changing this fast, but soon..

Errors and noise.
Sometimes we disagree.
Deal with by: avoid, repair or contain.  Or just deal with it - allow argumentation.
Fuzzy, rough semantics - almost, maybe.

Lots of research questions.  But not ones we could ask 10 years ago.

Information universe - "algorithms exist without us looking at them".

We should ask if things work in theory.
Scientists vs. engineers.
Discovering vs. building.
  • Is this incidental or universal?
OWL is our microscope.
We can see structure well in some domains, but not so well in others.  Maybe it's our tool that distorts, rather than a property of the domain.

Says we should change our mindset from building stuff to hypothesising and falsifying.