Last week, I was at Dagstuhl for a seminar on knowledge graphs specifically focused on new directions for knowledge representation. Knowledge Graphs have exploded in practice since the release of Google’s Knowledge Graph in 2012. Examples include knowledge graphs at AirBnb, Zalando, and Thomson Reuters. Beyond commercial knowledge graphs, there are many successful academic/public knowledge graphs including WikiData, Yago, and Nell.

The emergence of these knowledge graphs has led to expanded research interest in constructing, producing and maintaining knowledge bases. As an indicator checkout the recent growth in papers using the term knowledge graph (~10x papers per year since 2012):

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The research in this area is found across fields of computer science ranging from the semantic web community to natural language processing and machine learning and databases. This is reflected in the recent CFP for the new Automated Knowledge Base Construction Conference.

This particular seminar primarily brought together folks who had a “home” community in the semantic web but were deeply engaged with another community. For example, Prof. Maria-Esther Vidal who is well versed in the database literature. This was nice in that there was already quite a lot of common ground but people who could effectively communicate or at least point to what’s happening in other areas. This was different than many of the other Dagstuhl seminars I’ve been to (this was my 6th), which were much more about bringing together different areas. I think both styles are useful but it felt like we could go faster as the language barrier was lower.

The broad aim of the seminar was to come with research challenges coming from the experience that we’ve had over the last 10 years. There will be a follow-up report that should summarize the thoughts of the whole group. There were a lot of sessions and a lot of amazing discussions both during the day and in the evening facilitated by cheese & wine (a benefit of Dagstuhl) so it’s hard to summarize everything even just on a personal level but I wanted to pull out the things that have stuck with me now that I’m back at home:

1) Knowledge Graphs of Everything

We are increasingly seeing knowledge graphs that cover an entire category of entities. For example, Amazon’s product graph aims to be a knowledge graph of all products in the world, one can think of Google and Apple maps as databases of every location in the world, a database of every company that has ever had a web page, or a database of everyone in India. Two things stand out. One, is that these are large sets of instance data. I would contend their focus is not deeply modeling the domain in some expressive logic ala Cyc. Second, a majority of these databases are built by private companies. I think it’s an interesting question as to whether things like Wikidata can equal these private knowledge graphs in a public way.

Once you start thinking at this scale, a number of interesting questions arise: how you keep these massive graphs up to date; can you integrate these graphs, how do you manage access control and policies (“controlled access”); what can you do with this; can we extend these sorts of graphs to the physical system (e.g. in IoT); what about a knowledge graph of happenings (ie. events). Fundamentally, I think this “everything notion” is a useful framing device for research challenges.

2) Knowledge Graphs as a communication medium

A big discussion point during the seminar was the integration of symbolic and sub-symbolic representations. I think that’s obvious given the success of deep learning and importantly in the representation space – embeddings. I liked how Michael Witbrock framed symbols as a strong prior on something being the case. Indeed, using background knowledge has been shown to improve learning performance on several tasks (e.g. Baier et al. 2018, Marino et al. 2017).

But this topic in general got us thinking about the usefulness of knowledge graphs as an exchange mechanism for machines. There’s is a bit of semantic web dogma that expressing things in a variant of logic helps for machine to machine communication. This is true to some degree but you can imagine that machines might like to consume a massive matrix of numbers instead of human readable symbols with logical operators.

Given that, then, what’s the role of knowledge graphs? One can hypothesize that it is for the exchange of large scale information between humanity and machines and vis versa. Currently, when people communicate large amounts of data they turn towards structure (i.e. libraries, websites with strong information architectures, databases). Why not use the same approach to communicate with machines then. Thus, knowledge graphs can be thought of as a useful medium of exchange between what machines are generating and what humanity would like to consume.

On a somewhat less grand note, we discussed the role of integrating different forms of representation in one knowledge graph. For example, keeping images represented as images and audio represented as audio alongside facts within the same knowledge graph. Additionally, we discussed different mechanisms for attaching semantics to the symbols in knowledge graphs (e.g. latent embeddings of symbols). I tried to capture some of that thinking in a brief overview talk.

In general, as we think of knowledge graphs as a communication medium we should think how to both tweak and expand the existing languages of expression we use for them and the semantics of those languages.

3) Knowledge graphs as social-technical processes

The final kind of thing that stuck in my mind is that at the scale we are talking about much of the issues resolve around the notions of the complex interplay between humans and machines in producing, using and maintaining knowledge graphs. This was reflected in multiple threads:

  • Juan Sequeda’s thinking emerging from his practical experience on the need for knowledge / data engineers to build knowledge graphs and the lack of tooling for them. In some sense, this was a call to revisit the work of ontology engineering but now in the light of this larger scale and extensive adoption.
  • The facts established by the work of Wouter Beek and co on empirical semantics that in large scale knowledge graphs actually how people express information differs from the intended underlying semantics.
  • The notions of how biases and perspectives are reflected in knowledge graphs and the steps taken to begin to address these. A good example is the work of wikidata community to present the biases and gaps in its knowledge base.
  • The success of schema.org and managing the overlapping needs of communities. This stood out because of the launch of Google Dataset search service based on schema.org metadata.

While not related directly to knowledge graphs during the seminar the following piece on the relationship between AI systems and humans came was circulating:

Kate Crawford and Vladan Joler, “Anatomy of an AI System: The Amazon Echo As An Anatomical Map of Human Labor, Data and Planetary Resources,” AI Now Institute and Share Lab, (September 7, 2018) https://anatomyof.ai

There is critical need for more data about the interface between the knowledge graph and its maintainers and users.

As I mentioned, there was lots more that was discussed and I hope the eventual report will capture this. Overall, it was fantastic to spend a week with the people below – both fun and thought provoking.

Random ponters:

A couple of weeks ago I was at Provenance Week 2018 – a biennial conference that brings together various communities working on data provenance. Personally, it’s a fantastic event as it’s an opportunity to see the range of work going on from provenance in astronomy data to the newest work on database theory for provenance. Bringing together these various strands is important as there is work from across computer science that touches on data provenance.

The week is anchored by the International Provenance and Annotation Workshop (IPAW) and the Theory and Practice of Provenance (TaPP) and includes events focused on emerging areas of interest including incremental re-computation , provenance-based security and algorithmic accountability. There were 90 attendees up from ~60 in the prior events and here they are:

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The folks at Kings College London, led by Vasa Curcin, did a fantastic job of organizing the event including great social outings on-top of their department building and with a boat ride along the thames. They also catered to the world cup fans as well. Thanks Vasa!

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I had the following major takeaways from the conference:

Improved Capture Systems

The two years since the last provenance week have seen a number of improved systems for capturing provenance. In the systems setting, DARPAs Transparent Computing program has given a boost to scaling out provenance capture systems. These systems use deep operating system instrumentation to capture logs over the past several years these have become more efficient and scalable e.g. Camflow, SPADE. This connects with the work we’ve been doing on improving capture using whole system record-and-replay. You  can now run these systems almost full-time although they capture significant amounts of data (3 days = ~110 GB). Indeed, the folks at Galois presented an impressive looking graph database specifically focused on working with provenance and time series data streaming from these systems.

Beyond the security use case, sciunit.run was a a neat tool using execution traces to produce reproducible computational experiments.

There were also a number of systems for improving the generation of instrumentation to capture provenance. UML2PROV automatically generates provenance instrumentation from UML diagrams and source code using the provenance templates approach. (Also used to capture provenance in an IoT setting.) Curator implements provenance capture for micro-services using existing logging libraries. Similarly, UNICORE now implements provenance for its HPC environment. I still believe structured logging is one of the under rated ways of integrating provenance capture into systems.

Finally, there was some interesting work on reconstructing provenance. In particular, I liked Alexander Rasin‘s work on reconstructing the contents of a database from its environment to answer provenance queries:2018-07-10 16.34.08.jpg

Also, the IPAW best paper looked at using annotations in a workflow to infer dependency relations:

Lastly, there was some initial work on extracting provenance of  health studies directly from published literature which I thought was a interesting way of recovering provenance.

Provenance for Accountability

Another theme (mirrored by the event noted above) was the use of provenance for accountability. This has always been a major use for provenance as pointed out by Bertram Ludäscher in his keynote:

However, I think due to increasing awareness around personal data usage and privacy the need for provenance is being recognized. See, for example, the Royal Society’s report on Data management and use: Governance in the 21st century. At Provenance Week, there were several papers addressing provenance for GDPR, see:

Also, the I was impressed with the demo from Imosphere using provenance for accountability and trust in health data:

Re-computation & Its Applications

Using provenance to determine what to recompute seems to have a number of interesting applications in different domains. Paolo Missier showed for example how it can be used to determine when to recompute in next generation sequencing pipelines.

I particular liked their notion of a re-computation front – what set of past executions do you need to re-execute in order to address the change in data.

Wrattler was a neat extension of the computational notebook idea that showed how provenance can be used to automatically propagate changes through notebook executions and support suggestions.

Marta Mattoso‘s team discussed the application of provenance to track the adjustments when performing steering of executions in complex HPC applications.

The work of Melanie Herschel‘s team on provenance for data integration points to the benefits of potentially applying recomputation using provenance to make the iterative nature of data integration speedier as she enumerated in her presentation at the recomputation worskhop.2018-07-12 15.01.42.jpg

You can see all the abstracts from the workshop here. I understand from Paolo that they will produce a report from the discussions there.

Overall, I left provenance week encouraged by the state of the community, the number of interesting application areas, and the plethora of research questions to work on.

Random Links

 

The early part of last week I attended the Web Science 2018 conference. It was hosted here in Amsterdam which was nice for me. It was nice to be at a conference where I could go home in the evening.

Web Science is an interesting research area in that it treats the Web itself as an object of study. It’s a highly interdisciplinary area that combines primarily social science with computer science. I always envision it as a loop with studies of what’s actually going on the Web leading to new interventions on the Web which we then need to study.

There were what I guess a hundred or so people there … it’s a small but fun community. I won’t give a complete rundown of the conference. You can find summaries of each day done by Cat Morgan (Workshop DayDay 1Day 2Day 3) but instead give an assortment of things that stuck out for me:

And some tweets:

I had the pleasure of attending the Web Conference 2018 in Lyon last week along with my colleague Corey Harper . This is the 27th addition of the largest conference on the World Wide Web. I have tremendous difficulty  not calling it WWW but I’ll learn! Instead of doing two trip reports the rest of this is a combo of Corey and my thoughts. Before getting to what we took away as main themes of the conference let’s look at the stats and organization:

It’s also worth pointing out that this is just the research track. There were 27 workshops,  21 tutorials, 30 demos (Paul was co-chair), 62 posters, four collocated conferences/events, 4 challenges, a developer track and programming track, a project track, an industry track, and… We are probably missing something as well. Suffice to say, even with the best work of the organizers it was hard to figure out what to see. Organizing an event with 2200+ attendees is a thing is a massive task – over 80 chairs were involved not to mention the PC and the local heavy lifting. Congrats to Fabien, Pierre-Antoine, Lionel and the whole committee for pulling it off.  It’s also great to see as well that the proceedings are open access and available on the web.

Given the breadth of the conference, we obviously couldn’t see everything but from our interests we pulled out the following themes:

  • Dealing with a Polluted Web
  • Tackling Tabular Data
  • Observational Methods
  • Scientific Content as a Driver

Dealing with a Polluted Web

The Web community is really owning it’s responsibility to help mitigate the destructive uses to which the Web is put. From the “Recoding Black Mirror” workshop, which we were sad to miss, through the opening keynote and the tracks on Security and Privacy and Fact Checking, this was a major topic throughout the conference.

Oxford professor Luciano Floridi gave an excellent first keynote  on “The Good Web” which addressed this topic head on. He introduced a number of nice metaphors to describe what’s going on:

  • Polluting agents in the Web ecosystem are like extremphiles, making the environment hostile to all but themselves
  • Democracy in some contexts can be like antibiotics: too much gives growth to antibiotic resistant bacteria.
  • His takeaway is that we need a bit of paternalism in this context now.

His talk was pretty compelling,  you can check out the full video here.

Additionally, Corey was able to attend the panel discussion that opened the “Journalism, Misinformation, and Fact-Checking” track, which included representation from the Credibility Coalition, the International Fact Checking Network, MIT, and WikiMedia. There was a discussion of how to set up economies of trust in the age of attention economies, and while some panelists agreed with Floridi’s call for some paternalism, there was also a warning that some techniques we might deploy to mitigate these risks could lead to “accidental authoritarianism.” The Credibility Coalition also provided an interesting review of how to define credibility indicators for news looking at over 16 indicators of credibility.

We were able to see parts of the “Web and Society track”, which included a number of papers related to social justice oriented themes. This included an excellent paper that showed how recommender systems in social networks often exacerbate and amplify gender and racial disparity in social network connections and engagement. Additionally, many papers addressed the relationship between the mainstream media and the web. (e.g. political polarization and social media, media and public attention using the web).

Some more examples: The best demo was awarded to a system that automatically analyzed privacy policies of websites and summarized them with respect to GDPR and:

More generally, it seems the question is how do we achieve quality assessment at scale?

Tackling Tabular Data

Knowledge graphs and heterogenous networks (there was a workshop on that) were a big part of the conference. Indeed the test of time paper award went to the original Yago paper. There were a number of talks about improving knowledge graphs for example for improving on question answering tasks, determining attributes that are needed to complete a KG or improving relation extraction. While tables have always been an input to knowledge graph construction (e.g. wikpedia infoboxes), an interesting turn was towards treating tabular data as a focus area.

As Natasha Noy from Google noted in her  keynote at the SAVE-SD workshop,  this is an area with a number of exciting research challenges:img_0034_google_savesd.jpg

There was a workshop on data search with a number of papers on the theme. In that workshop, Maarten de Rijke gave a keynote on the work his team has been doing in the context of data search project with Elsevier.

In the main track, there was an excellent talk on Ad-Hoc Table Retrieval using Semantic Similarity. They looked at finding semantically central columns to provide a rank list of columns. More broadly they are looking at spreadsheet compilation as the task (see smarttables.cc and the dataset for that task.) Furthermore, the paper Towards Annotating Relational Data on the Web with Language Models looked at enriching tables through linking into a knowledge graph.

Observational Methods

Observing  user behavior has been a part of research on the Web, any web search engine is driven by that notion. What did seem to be striking is the depth of the observational data being employed. Prof. Lorrie Cranor gave an excellent keynote on the user experience of web security (video here). Did you know that if you read all the privacy policies of all the sites you visit it wold take 244 hours per year? Also, the idea of privacy as nutrition labels is pretty cool:

But what was interesting was her labs use of an observatory of 200 participants who allowed their Windows home computers to be instrumented. This kind of instrumentation gives deep insight into how users actually use their browsers and security settings.

Another example of deep observational data, was the use of mouse tracking on search result pages to detect how people search under anxiety conditions:

In the paper by Wei Sui and co-authors on Computational Creative Advertisements presented at the HumL workshop – they use in-home facial and video tracking to measure emotional response to ads by volunteers.

The final example was the use of FMRI scans to track brain activity of participants during web search tasks. All these examples provide amazing insights into how people use these technologies but as these sorts of methods are more broadly adopted, we need to make sure to adopt the kinds of safe-guards adopted by these researchers – e.g. consent, IRBs, anonymization.

Scientific Content as a Driver

It’s probably our bias but we saw a lot of work tackling scientific content. Probably because it’s both interesting and provides a number of challenges. For example, the best paper of the conference (HighLife) was about extracting n-ary relations for knowledge graph construction motivated by the need for such types of relations in creating biomedical knowledge graphs. The aforementioned work on tabular data often is motivated by the needs of research. Obviously SAVE-SD covered this in detail:

In the demo track, the etymo.io search engine was presented to summarize and visualization of scientific papers. Kuansan Wang at the BigNet workshop talked about Microsoft Academic Search and the difficulties and opportunities in processing so much scientific data.

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Paul gave a keynote at the same workshop also using science as the motivation for new methods for building out knowledge graphs. Slides below:

In the panel, Structured Data on the Web 7.0, Google’s Evgeniy Gabrilovich – creator of the Knowledge Vote – noted the challenges of getting highly correct data for Google’s Medical Knowledge graph and that doing this automatically is still difficult.

Finally, using DOIs for studying persistent identifier use over time on the Web.

Wrap-up

Overall, we had a fantastic web conference. Good research, good conversations and good food:

Random Thoughts

 

Last week, I had the pleasure to be able to attend a bilateral meeting between the Royal Society and the KNAW. The aim was to strengthen the relation between the UK and Dutch scientific communities. The meeting focused on three scientific areas: quantum physics & technology; nanochemistry; and responsible data science. I was there for the latter. The event was held at Chicheley Hall which is a classic baroque English country house (think Pride & Prejudice). It’s a marvelous venue – very much similar in concept to Dagstuhl (but with an English vibe) where you are really wholly immersed in academic conversation.

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One of the fun things about the event was getting a glimpse of what other colleagues from other technical disciplines are doing. It was cool to see Prof. Bert Weckhuysen enthusiasm for using imaging technologies to understand catalysts at the nanoscale. Likewise, seeing both the progress and the investment (!) in quantum computing from Prof. Ian Walmsley was informative. I also got an insider intro to the challenges of engineering a quantum computer from Dr. Ruth Oulton.

The responsible data science track had ~15 people. What I liked was that the organizers not only included computer scientists but also legal scholars, politicians, social scientists, philosophers and policy makers. The session consisted primarily of talks but luckily everyone was open to discussion throughout. Broadly, responsible data science covers the ethics of the practice and implications of data science or put another way:

For more context, I suggest starting with two sources: 1) The Dutch consortium on responsible data science 2) the paper 10 Simple Rules for Responsible Big Data Research. I took away two themes both from the track as well as my various chats with people during coffee breaks, dinner and the bar.

1) The computer science community is engaging

It was apparent through out the meeting that the computer science community is confronting the challenges head on. A compelling example was the talk by Dr. Alastair Beresford from Cambridge about Device Analyzer a system that captures the activity of user’s mobile phones in order to provide data to improve device security, which it has:

He talked compellingly about the trade-offs between consent and privacy and how the project tries to manage these issues. In particular, I thought how they handle data sharing with other researchers was interesting. It reminded me very much of how the Dutch Central Bureau of Statistics manages microdata on populations.

Another example was the discussion by Prof. Maarten De Rijke on the work going on with diversity for recommender and search systems. He called out the Conference on Fairness, Accountability, and Transparency (FAT*) that was happening just after this meeting, where the data science community is engaging on these issues. Indeed, one of my colleagues was tweeting from that meeting:

Julian Huppert, former MP, discussed the independent review board setup up by DeepMind Health to enable transparency about their practices. He is part of that board.  Interestingly, Richard Horton, Editor of the Lancet is also part of that board Furthermore, Prof. Bart Jacobs discussed the polymorphic encryption based privacy system he’s developing for a collaboration between Google’s Verily and Radboud University around Parkinson’s disease. This is an example that  even the majors are engaged around these notions of responsibility. To emphasize this engagement notion even more, during the meeting a new report on the Malicious Uses of AI came out from a number or well-known organizations.

One thing that I kept thinking is that we need more assets or concrete artifacts that data scientists can apply in practice.

For example, I like the direction outlined in this article from Dr. Virginia Dignum about defining concrete principles using a design for values based approach. See TU Delft’s Design for Values Institute for more on this kind of approach.

2) Other methods needed

As data scientists, we tend to want to use an experimental / data driven approach even to these notions surrounding responsibility.

Even though I think there’s absolutely a role here for a data driven approach, it’s worth looking at other kinds of more qualitative methods, for example, by using survey instruments or an ethnographic approach or even studying the textual representation of the regulatory apparatus.  For instance, reflecting on the notion of Thick Data is compelling for data science practice. This was brought home by Dr. Ian Brown in his talk on data science and regulation which combined both an economic and survey view:

Personally, I tried to bring some social science literature to bear when discussing the need for transparency in how we source our data. I also argued for the idea that adopting a responsible approach is also actually good for the operational side of data science practice:

While I think it’s important for computer scientists to look at different methods, it’s also important for other disciplines to gain insight into the actual process of data science itself as Dr. Linnet Taylor grappled within in her talk about observing a data governance project.

Overall, I enjoyed both the setting and the content of the meeting. If we can continue to have these sorts of conversations, I think the data science field will be much better placed to deal with the ethical and other implications of our technology.

Random Thoughts

  • Peacocks!
  • Regulating Code – something for the reading list
  • Somebody remind me to bring a jacket next time I go to an English Country house!
  • I always love it when egg codes get brought up when talking about provenance.
  • I was told that I had a “Californian conceptualization” of things – I don’t think it was meant as a complement – but I’ll take it as such 🙂
  • Interesting pointer to work by Seda Gurses about in privacy and software engineering from @1Br0wn
  • Lots of discussion of large internet majors and monopolies. There’s lots of academic work on this but I really like Ben Thompson’s notion of aggregator’s as the way to think about them.
  • Merkle trees are great – but blockchain is a nicer name 😉

 

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.

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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:

A month ago, I had the opportunity to attend the Dagstuhl Seminar  Citizen Science: Design and Engagement. Dagstuhl is really a wonderful place. This was my fifth time there. You can get an impression of the atmosphere from the report I wrote about my first trip there. I have primarily been to Dagstuhl for technical topics in the area of data provenance and semantic data management as well as for conversations about open science/research communication.

This seminar was a great chance for me to learn more about citizen science and discuss its intersection with the practice of open science. There was a great group of people there covering the gamut from creators of citizen science platforms to crowd-sourcing researchers. 17272.01.l

As usual with Dagstuhl seminars, it’s less about presentations and more about the conversations. There will be a report documenting the outcome and hopefully a paper describing the common thoughts of the participants. Neal Reeves took vast amounts of notes so I’m sure that this will be a good report :-). Here’s a whiteboard we had full of input:

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Thus, instead of trying to relay what we came up with (you’ll have to wait for the report), I’ll just pull out some of my own brief highlights.

Background on Citizen Science

There were a lot of good pointers on where to start understand current thinking around citizen science. First, two tutorials from the seminar:

What do citizen science projects look like:

Example projects:

How should citizen science be pursued:

And a Book:

Open Science & Citizen Science

Claudia Göbel gave an excellent talk about the overlap of citizen science and open science. First, she gave an important reminder that science in particular in the 1700s was done as public demonstrations walking us through the example painting below. 2017-07-04 11.23.02

She then looked at the overlap between citizen science and open science. Summarized below:

citizenopenscience.png

A follow-on discussion at the with some of the seminar participants led to input for a whitepaper that is being developed through the ECSA on Citizen & Open Science for Europe. Check out the preliminary draft. I look forward to seeing the outcome.

Questioning Assumptions

One thing that I left the seminar thinking about was was the need to question my own (and my field’s) assumptions. This was really inspired by talking to Chris Welty and reflecting on his work with Lora Aroyo on the issues in human annotation and the construction of gold sets.  Some assumptions to question:

  • What qualifications you need to have to be considered a scientist.
  • Interoperability is a good thing to pursue.
  • Openness is a worthy pursuit.
  • We can safely assume a lack of dynamics in computational systems.
  • That human performance is good performance.

Indeed, in Marissa Ponti she pointed to the example below and highlighted some of the potential ramifications of what each of these (what at first blush are positive) citizen science projects could lead to. 2017-07-03 10.06.36

That being said, the ability to rapidly engage more people in the science system seems to be a good thing indeed. An an assumption I’m happy to hold.

Random

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