Tag Archives: #machinelearning

It’s not so frequently that you get a major international conference in your area of interest around the corner from your house. Luckily for me, that just happened. From June 30th – July 5th, SIGMOD/PODS was hosted here in Amsterdam. SIGMOD/PODS is one of the major conferences on databases and data management. Before diving into the event itself, I really wanted to thank  Peter Boncz, Stefan Manegold, Hannes Mühleisen and the whole organizing team (from @CWI_DA and the NL DB community) for getting this massive conference here:

and pulling off things like this:

Oh and really nice badges too:BKBnl49c.jpgGood job!


Surprisingly, this was the first time I’ve been at SIGMOD. While I’m pretty acquainted with the database literature, I’ve always just hung out in different spots. Hence, I had some trepidation attending wondering if I’d fit in? Who would I talk to over coffee? Would all the papers be about join algorithms or implications of cache misses on some new tree data structure variant? Now obviously this is all pretty bogus thinking, just looking at the proceedings would tell you that. But there’s nothing like attending in person to bust preconceived notions. Yes, there were papers on hardware performance and join algorithms – which were by the way pretty interesting – but there were many papers on other data management problems many of which we are trying to tackle (e.g. provenance, messy data integration).  Also, there were many colleagues that I knew (e.g. Olaf & Jeff above). Anyway, perceptions busted! Sorry DB friends you might have to put up with me some more 😀.

I was at the conference for the better part of 6 days – that’s a lot of material – so I definitely missed a lot but here are the four themes I took from the conference.

  1. Data management for machine learning
  2. Machine learning for data management
  3. New applications of provenance
  4. Software & The Data Center Computer

Data Management for Machine Learning


Matei Zaharia (Stanford/Databricks) on the need for data management for ML

The success of machine learning has rightly changed computer science as a field. In particular, the data management community writ large has reacted trying to tackle the needs of machine learning practitioners with data management systems. This was a major theme at SIGMOD.

There were a number of what I would term holistic systems that helped manage and improve the process of building ML pipelines including using data. Snorkel DryBell provides a holistic system that lets engineers employ external knowledge (knowledge graphs, dictionaries, rules) to reduce the number of needed training examples needed to create new classifiers. Vizier provides a notebook data science environment backed fully by a provenance data management environment that allows data science pipelines to be debugged and reused.  Apple presented their in-house system for helping data management specifically designed for machine learning – from my understanding all their data is completely provenance enabled – ensuring that ML engineers know exactly what data they can use for what kinds of model building tasks.

I think the other thread here is the use of real world datasets to drive these systems. The example that I found the most compelling was Alpine Meadow++ to use knowledge about ML datasets (e.g. Kaggle) to improve the suggestion on new ML pipelines in an AutoML setting. rsfZ2iZO.jpeg

On a similar note, I thought the work of Suhail Rehman from the University of Chicago on using over 1 million juypter  notebooks to understand data analysis workflows was particularly interesting. In general, the notion is that we need to taking a looking at the whole model building and analysis problem in a holistic sense inclusive of data management . This was emphasized by the folks doing the Magellan entity matching project in their paper on Entity Matching Meets Data Science.


Machine Learning for Data Management

On the flip side, machine learning is rapidly influencing data management itself. The aforementioned Megellan project has developed a deep learning entity matcher. Knowledge graph construction and maintenance is heavily reliant on ML. (See also the new work from Luna Dong & colleagues which she talked about at SIGMOD). Likewise, ML is being used to detect data quality issues (e.g. HoloDetect).

ML is also impacting even lower levels of the data management stack.


Tim Kraska list of algorithms that are or are being MLified

I went to the tutorial on Learned Data-intensive systems from Stratos Idreos and Tim Kraska. They overviewed how machine learning could be used to replace parts or augment of the whole database system and when that might be useful.

KbYGVEA2.jpegIt was quite good, I hope they put the slides up somewhere. The key notion for me is this idea of instance optimality: by using machine learning we can tailor performance to specific users and applications whereas in the past this was not cost effective because the need for programmer effort. They suggested 4 ways to create instance optimized algorithms and data structures:

  1. Synthesize traditional algorithms using a model
  2. Use a CDF model of the data in your system to tailor the algorithm
  3. Use a prediction model as part of your algorithm
  4. Try to to learn the entire algorithm or data structure

They had quite the laundry list of recent papers tackling this approach and this seems like a super hot topic.

Another example was SkinnerDb which uses reinforcement learning to on the fly to learn optimal join ordering. I told you there were papers on joins that were interesting.


New Provenance Applications

There was an entire session of SIGMOD devoted to provenance, which was cool.  What I liked about the papers was that that they had several new applications of provenance or optimizations for applications beyond auditing or debugging.

In addition to these new applications, I saw some nice new provenance capture systems:

Software & The Data Center Computer

This is less of a common theme but something that just struck me. Microsoft discussed their upgrade or overhaul of the database as a service that they offer in Azure. Likewise, Apple discussed FoundationDB – the mult-tenancy database that underlines CloudKit.

LI03R_uu.jpeg discussed their new file system to deal with containers and ML workloads across clusters with tens of thousands of servers. These are not applications that are hosted in the cloud but instead they assume the data center. These applications are fundamentally designed with the idea that they will be executed on a big chunk of an entire data center. I know my friends at super computing have been doing this for ages but I always wonder how to change one’s mindset to think about building applications that big and not only building them but upgrading & maintaining them as well.


Overall, this was a fantastic conference. Beyond the excellent technical content, from a personal point of view, it was really eye opening to marinate in the community. From the point of view of the Amsterdam tech community, it was exciting to have an Amsterdam Data Science Meetup with over 500 people.

If you weren’t there, video of much of the event is available.

Random Notes



About two weeks ago, I had the pleasure of attending the 1st Conference on Automated Knowledge Base Construction held in Amherst, Massachusetts. This conference follows up on a number of successful workshops held at venues like NeurIPS and NAACL. Why a conference and not another workshop? The general chair and host of the conference (and he really did feel like a host), Andrew McCallum articulated this as coming from three drivers: 1) the community spans a number of different research areas but was getting its own identity; 2) the workshop was outgrowing typical colocation opportunities and 3) the motivation to have a smaller event where people could really connect in comparison to some larger venues.

I don’t know the exact total but I think there was just over 110 people at the conference. Importantly, there were top people in the field and they stuck around and hung out. The size, the location, the social events (a lovely group walk in the forest in MA), all made it so that the conference achieved the goal of having time to converse in depth. It reminded me a lot of our Provenance Week events in the scale and depth of conversation.

Oh and Amherst is a terribly cute college town:

2019-05-19 16.56.06.jpg

Given that the conference subject is really central to my research, I found it hard to boil down everything into a some themes but I’ll give it a shot:

  • Representational polyglotism
  • So many datasets so little time
  • The challenges of knowledge (graph) engineering
  • There’s lots more to do!

Representational polyglotism

Untitled 2.png

One of the main points that came up frequently both in talks and in conversation was around what one should use as representation language for knowledge bases and for what purpose. Typed graphs have clearly shown their worth over the last 10 years but with the rise of knowledge graphs in a wide variety of industries and applications. The power of the relational approach especially in its probabilistic form  was shown in excellent talks by Lise Getoor on PSL and by Guy van den Broeck. For efficient query answering and efficiency in data usage, symbolic solutions work well. On the other hand, the softness of embedding or even straight textual representations enables the kind of fuzziness that’s inherent in human knowledge. Currently, our approach to unify these two views is often to encode the relational representation in an embedding space, reason about it geometrically, and then through it back over the wall into symbolic/relational space.  This was something that came up frequently and Van den Broek took this head on in his talk.

Then there’s McCallum’s notion of text as a knowledge graph. This approach was used frequently to different degrees, which is to be expected given that much of the contents of KGs is provided through information extraction. In her talk, Laura Dietz, discussed her work where she annotated the edges of a knowledge graph with paragraph text to improve entity ranking in search.  Likewise, the work presented by Yejin Choi around common sense reasoning used natural language as the representational “formalism”. She discussed the ATOMIC (paper) knowledge graph  which represents a crowed sourced common sense knowledge as natural language text triples (e.g. PersonX finds ___ in the literature).  She then described transformer based, BERT-esque, architectures  (COMET: Commonsense Transformers for Knowledge Graph Construction) that perform well on common sense reasoning tasks based on these kinds of representations.

The performance of BERT style language models on all sorts of tasks, led to Sebastian Riedel considering whether one should treat these models as the KB:


It turns out that out-of-the box BERT performs pretty well as a knowledge base for single tokens that have been seen frequently by the model. That’s pretty amazing. Is storing all our knowledge in the parameters of a model the way to go? Maybe not but surely it’s good to investigate the extent of the possibilities here. I guess I came away from the event thinking that we are moving toward an environment where KBs will maintain heterogenous representations and that we are at a point where we need to embrace this range of representations to produce results in order face the challenges of the fuzzy. For example, the challenge of reasoning:

or of disagreement around knowledge as discussed by Chris Welty:


So many datasets so little time

Progress in this field is driven by data and there were a lot of new datasets presented at the conference. Here’s my (probably incomplete) list:

  • OPIEC – from the makers of the MINIE open ie system – 300 million open information extracted triples with a bunch of interesting annotations;
  • TREC CAR dataset – cool task, auto generate articles for a search query;
  • HAnDS – a new dataset for fined grained entity typing  to support thousands of types;
  • HellaSwag – a new dataset for common sense inference designed to be hard for state-of-the-art transformer based architectures (BERT);
  • ShARC – conversational question answering dataset focused on follow-up questions
  • Materials Synthesis annotated data for extraction of material synthesis recipes from text. Look up in their GitHub repo for more interesting stuff
  • MedMentions – annotated corpora of UMLs mentions in biomedical papers from CZI
  • A bunch of datasets that were submitted to EMNLP so expect those to come soon – follow @nlpmattg.

The challenges of knowledge (graph) engineering

Juan Sequeda has been on this topic for a while – large scale knowledge graphs are really difficult to engineer. The team at DiffBot – who were at the conference – are doing a great job of supplying this engineering as a service through their knowledge graph API.  I’ve been working with another start-up SeMI who are also trying to tackle this challenge. But this is still complicated task as underlined for me when talking to Francois Scharffe who organized the recent industry focused Knowledge Graph Conference. The complexity of KG (social-technical) engineering was one of the main themes of that conference. An example of the need to tackle this complexity at AKBC was the work presented about the knowledge engineering going on for the KG behind Apple’s Siri. Xiao Ling emphasized that they spent a lot of their time thinking about and implementing systems for knowledge base construction developer workflow:

Thinking about these sorts of challenges was also behind several of the presentations in  the Open Knowledge Network workshop: Vicki Tardif from the Google Knowledge Graph discussed these issues in particular with reference to the muddiness of knowledge representation (e.g. how to interpret facets of a single entity? or how to align the inconsistencies of people with that of machines?). Jim McCusker and Deborah McGuinness’ work on the provenance/nanopublication driven WhyIs framework for knowledge graph construction is an important in that their software views a knowledge graph not as an output but as a set of tooling for engineering that graph.

The best paper of the conference Alexandria: Unsupervised High-Precision Knowledge Base Construction using a Probabilistic Program was also about how to lower the barrier to defining knowledge base construction steps using a simple probabilistic program. Building a KB from a single seed fact is impressive but then you need the engineering effort to massively scale probabilistic inference.

Alexandra Meliou’s work on using provenance to help diagnose these pipelines was particularly relevant to this issue. I have now added a bunch of her papers to the queue.

There’s lots more to do

One of the things I most appreciated was that many speakers had a set of research challenges at the end of their presentations. So here’s a set of things you could work on in this space curated from the event. Note these may be paraphrased.

  • Laura Dietz:
    • General purpose schema with many types
    • High coverage/recall (40%?)
    • Extraction of complex relations (not just triples + coref)
    • Bridging existing KGs with text
    • Relevant information extraction
    • Query-specific knowledge graphs
  • Fernando Pereira
    • combing source correlation and grounding
  • Guy van den Broeck
    • Do more than link predication
    • Tear down the wall between query evaluation and knowledge base completion
    • The open world assumption – take it seriously
  • Waleed Ammar
    • Bridge sentence level and document level predictions
    • Summarize published results on a given problem
    • Develop tools to facilitate peer review
    • How do we crowd source annotations for a specialized domain
    • What are leading indicators of a papers impact?
  • Sebastian Riedel
    • Determine what BERT actually knows or what it’s guessing
  • Xian Ren
    • Where can we source complex rules that help AKBC?
    • How do we induce transferrable latent structures from pre-trained models?
    • Can we have modular neural networks for modeling compositional rules?
    • Ho do we model “human effort” in the objective function during training?
  • Matt Gardner
    • Make hard reading datasets by baking required reasoning into them

Finally, I think the biggest challenge that was laid down was from Claudia Wagner, which is how to think a bit more introspectively about the theory behind our AKBC methods and how we might even bring the rigor of social science methodology to our technical approaches:

I left AKBC 2019 with a fountain of ideas and research questions, which I count as a success. This is a community to watch.  AKBC 2020 is definitely on my list of events to attend next year.

Random Pointers


%d bloggers like this: