Knowledge Graph Conference 2019, Day 1
On Tuesday, May 7, began the first Knowledge Graph Conference. Organized by François Scharffe and his colleagues at Columbia University, it was located in New York City. The conference goes for two days, and aims at a much more industry-oriented crowd than conferences such as ISWC. And it reflected very prominently in the speaker line-up: especially finance was very well represented (no surprise, with Wall Street being just downtown).
Speakers and participants from Goldman Sachs, Capital One, Wells Fargo, Mastercard, Bank of America, and others were in the room, but also from companies in other industries, such as Astra Zeneca, Amazon, Uber, or AirBnB. The speakers and participants were rather open about their work, often listing numbers of triples and entities (which really is a weird metric to cite, but since it is readily available it is often expected to be stated), and these were usually in the billions. More interesting than the sheer size of their respective KGs were their use cases, and particularly in finance it was often ensuring compliance to insider trading rules and similar regulations.
I presented Wikidata and the idea of an Abstract Wikipedia as going beyond what a Knowledge Graph can easily express. I had the feeling the presentation was well received - it was obvious that many people in the audience were already fully aware of Wikidata and are actively using it or planning to use it. For others, particularly the SPARQL endpoint with its powerful visualization capabilities and the federated queries, and the external identifiers in Wikidata, and the approach to references for the claims in Wikidata were perceived as highlights. The proposal of an Abstract Wikipedia was very warmly received, and it was the first time no one called it out as a crazy idea. I guess the audience was very friendly, despite New York's reputation.
A second set of speakers were offering technologies and services - and I guess I belong to this second set by speaking about Wikidata - and among them were people like Juan Sequeda of Capsenta, who gave an extremely engaging and well-substantiated talk on how to bridge the chasm towards more KG adoption; Pierre Haren of Causality Link, who offered an interesting personal history through KR land from LISP to Causal Graphs; Dieter Fensel of OnLim, who had a a number of really good points on the relation between intelligent assistants and their dialogue systems and KGs; Neo4J, Eccenca, Diffbot.
A highlight for me was the astute and frequent observation by a number of the speakers from the first set that the most challenging problems with Knowledge Graphs were rarely technical. I guess graph serving systems and cloud infrastructure have improved so much that we don't have to worry about these parts anymore unless you are doing crazy big graphs. The most frequently mentioned problems were social and organizational. Since Knowledge Graphs often pulled data sources from many different parts of an organization together, with a common semantics, they trigger feelings of territoriality. Who gets to define the common ontology? What if the data a team provides has problems or is used carelessly, who's at fault? What if others benefit from our data more than we did even though we put all the effort in to clean it up? How do we get recognized for our work? Organizational questions were often about a lack of understanding, especially among engineers, for fundamental Knowledge Graph principles, and a lack of enthusiasm in the management chain - especially when the costs are being estimated and the social problems mentioned before become apparent. One particularly visible moment was when Bethany Sehon from Capital One was asked about the major challenges to standardizing vocabularies - and her first answer was basically "egos".
All speakers talked about the huge benefits they reaped from using Knowledge Graphs (such as detecting likely cliques of potential insider trading that later indeed got convicted) - but then again, this is to be expected since conference participation is self-selecting, and we wouldn't hear of failures in such a setting.
I had a great day at the inaugural Knowledge Graph Conference, and am sad that I have to miss the second day. Thanks to François Scharffe for organizing the conference, and thanks to the sponsors, OntoText, Collibra, and TigerGraph.
For more, see:
Knowledge Graph Technology and Applications 2019