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Last week, I conferenced! I attended the 16th International Semantic Web Conference (ISWC 2017) in Vienna at the beginning of the week and then headed up to FORCE 2017 in Berlin for the back half of the week. For the last several ISWC, I’ve been involved in the organizing committee, but this year I got to relax. It was a nice chance to just be an attendee and see what was up. This was made even nicer by the really tremendous job Axel, Jeff and their team did  in organizing both the logistics and program. The venues were really amazing and the wifi worked!

Before getting into what I thought were the major themes of the conference, lets do some stats:

  • 624 participants
  • Papers
    • Research track: 197 submissions – 44 accepted – 23% acceptance rate
    • In-use: 27 submissions – 9  accepted – 33% acceptance rate
    • Resources: 76 submissions – 23 accepted – 30% acceptance rate
  • 46 posters & 61 demos
  • Over 1000 reviews were done excluding what was done for the workshop / demos / posters. Just a massive amount of work in helping work get better.

This year they expanded the number of best reviewers and I was happy to be one of them:

You can find all the papers online as preprints.

The three themes I took away from the conference were:

  1. Ecosystems for knowledge engineering
  2. Learn from everything
  3. More media

Ecosystems for knowledge engineering

This was a hard theme to find a title for but there were several talks about how to design and engineer the combination of social and technical processes to build knowledge graphs. Deborah McGuinness in her keynote talked about how it took a village to create effective knowledge driven systems. These systems are the combination of experts, knowledge specialists, systems that do ML, ontologies, and data sources. Summed up by the following slide:

My best idea is that this would fall under the rubric of knowledge engineering. Something that has always been part of the semantic web community. What I saw though was the development of more extensive ideas and guidelines about how to create and put into practice not just human focused systems but entire social-techical ecosystems that leveraged all manner of components.

Some examples: Gil et al.’s paper on  creating a platform for high-quality ontology development and data annotation explicitly discusses the community organization along with the platform used to enable it. Knoblock et al’s paper on creating linked data for the American Art Collaborative discusses not only the technology for generating linked data from heterogenous sources but the need for a collaborative workflow facilitated by a shared space (Github) but also the need for tools used to do expert review.  In one of my favorite papers, Piscopo et al evaluated the the provenance of Wikidata statements and also developed machine learning models that could judge authoritativeness & relevance of potential source material. This could provide a helpful tool in allowing Wikidata editors to garden the statements automatically added by bots. As a last example, Jamie Taylor in his keynote discussed how at Google they have a Knowledge Graph Schema team that is there to support a developers in creating interlocking data structures. The team is focused on supporting and maintaining quality of the knowledge graph.

A big discussion area was the idea coming out of the US for a project / initiative around an Open Knowledge Network introduced by Guha. Again, I’ll put this under the notion of how to create these massive social-technical knowledge systems.

I think more work needs to be done in this space not only with respect to the dynamics of these ecosystems as Michael Lauruhn and I discussed in a recent paper but also from a reuse perspective as Pascal Hitzler has been talking about with ontology design patterns.

Learn from everything

The second theme for me was learning from everything. Essentially, this is the use of the combination of structured knowledge and unstructured data within machine learning scenarios to achieve better results. A good example of this was presented by Achim Rettinger on using cross modal embeddings to improve semantic similarity and type prediction tasks:

Likewise, Nada Lavrač discussed in her keynote how to different approaches for semantic data mining, which also leverages different sources of information for learning. In particular, what was interesting is the use of network analysis to create a smaller knowledge network to learn from.

A couple of other examples include:

It’s worth calling out the winner of the renewed  Semantic Web Challenge from IBM, which used deep learning in combination with sources such as dbpedia, geonames and background assumptions for relation learning.

2017-10-23 20.44.14.jpg

Socrates – Winner SWC

(As an aside, I think it’s pretty cool that the challenge was won by IBM on data provided by Thomson Reuters with an award from Elsevier. Open innovation at its best.)

For a more broad take on the complementarity between deep learning and the semantic web, Dan Brickley’s paper is a fun read. Indeed, as we start to potentially address common sense knowledge we will have to take more opportunity to learn from everywhere.

More media

Finally, I think we saw an increase in the number of works dealing with different forms of media. I really enjoyed the talk on Improving Visual Relationship Detection using Semantic Modeling of Scene Descriptions given by Stephan Brier. Where they used a background knowledge base to improve relation prediction between portions of images:

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There was entire session focused on multimodal linked data including talks on audio ( MIDI LOD cloud, the Internet Music Archive as linked data) and images IMGPedia content analyzed linked data descriptions of Wikimedia commons.  You can even mash-up music with the SPARQL-DJ.

Conclusion

DBpedia won the 10 year award paper. 10 years later semantic technologies and in particular the notion of a knowledge graph are mainstream (e.g. Thomson Reuters has a 100 billion node knowledge graph). While we may still be focused too much on the available knowledge graphs  for our research work, it seems to me that the community is branching out to begin to answer a range new questions (how to build knowledge ecosystems?, where does learning fit?, …) about the intersection of semantics and the web.

Random Notes:

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Earlier this week, I attended the SNN Symposium –  Intelligent Machines. SNN is the Dutch foundation for Neural Networks, which coordinates the Netherlands national platform on machine learning, which connects most of the ML groups in the Netherlands.

It’s not typical for a 1 day Dutch specific academic symposium to sell out – but this one did. This is a combination of the topic (machine learning is hot!) but also the speakers. The organizers put together a great line-up:

It’s not typical to get essentially 4 keynotes in one day. Instead of going through each talk in turn, I’ll try to draw some of the major items that I took away from across the talks.

The Case for Probability Theory

Both Prof. Ghahramani and Dr. Herbrich made strong arguments for probability as the core way to think about machine learning/intelligence and in particular a bayesian view of the world . Herberich summarized the argument to use probability as:

  • Probability is a calculus of uncertainty (argued using the “naturalness” of Cox Axioms)
  • It maps well to computational systems – (factor graphs allow for computational distribution )
  • It decouples inference, prediction and decision

Factor Graphs!

For me, it was a nice reminder to think of optimization as an approximation for computing probabilities. More generally, coming back to a simplified high-level framework makes understanding the complexities of the algorithms easier. Ghahramani did a great job of connecting this framework with the underlying mathematics. Slides from his ML course are here – unfortunately without the lecturer himself.

The Rise of Reinforcement Learning

The presentations by Daan Wierstra and Sethu Vijayakumar both featured pretty amazing demos. Dr. Wierstra work at was on the team that developed algorithms that can learn to play Atari games purely from pixels and a knowledge of the game score. This uses reinforcement learning to train a convolutional neural network. The key invention here was to keep around the past experience when providing input back into the neural network.

Likewise, Prof. Vijayakumar showed how robots can also learn via reinforcement. Here’s an example of a robot arm learning to balance a pole.

Reinforcement learning can help attack the problem of data efficiency that’s faced by machine learning. Essentially, it’s hard to get enough training data, let alone labelled training data. We’ve seen the rise of unsupervised methods to take advantage of the data we do have. (Side note: unsupervised approaches just keep getting better) But by situating the agent in an environment, it it’s easier to provide the sort of training necessary. Instead of examples, one needs to provide the appropriate feedback environment. From Wienstra’s talk, again the apparent difficulty for reinforcement learning is temporal abstraction – using knowledge from past to learn. Both the Atari and Robot example receive fairly immediate reinforcement on their tasks.

This takes us back to the classic ideas of situated cognition and of course the work of Luc Steels.

Good Task Formulation

Sometimes half the battle in research is coming up with a good task formulation. This is obvious but it’s actually quite difficult. What struck me was each of the speakers was good at formulating their problem and the metrics by which they can test it. For example, Prof. Ghahramani was able to articulate his goals and measure of success for the development of the Automatic Statistician – a system for finding a good model of a given data and providing a nifty human readable and transparent report. Here’s one for affairs 🙂 

(Side note: the combination of parameter search and search through components reminds of work on the Wings Workflow environment.)

Likewise, Dr. Herbrich was good at translating the various problems faced within Amazon into specific ML tasks. For example, here’s his definition for Content Linkage:

image1

 

He then broke this down into the specific well defined tasks through the rest of talk. The important thing here is to keep coming back to these core tasks and having well defined evaluation criteria. (See also Watson’s approach)

Attacking General AI?

Deep Mind - general AI

One thing that stood out to me was the audacious of the Google Deep Mind goal – to solve General AI. Essentially, designing “AI that can operate over a wide range of tasks”. Why now? Wierstra emphasized the available compute power and advances in different algorithms. I thought the interesting comment was that they have something like a 30 year time horizon within a company. Of course, funding may not last long, but articulating that goal and demonstrable attacking it is something that I would expect more from academia. Indeed, I wonder if we are not thinking enough  They already have very impressive results. The atari example but also their DRAW algorithm for learning to generate images :

I also like their approach of Neural Turing Machines – using recurrent neural network to create a computer itself. By adding memory to neural networks there trying to tackle the “memory” problem discussed above.

Overall, it was an invigorating day.

Random thoughts:

  • Robots demos are cool!

  • Text Kernel and Postdam’s use of word2vec for entity extraction in CVs was interesting.
  •  (click to see the full size poster)IMG_0019
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