- From: Aidan Hogan <aidhog@gmail.com>
- Date: Tue, 10 Mar 2020 03:02:38 -0300
- To: semantic-web <semantic-web@w3.org>
- Cc: "kg-tutorial@googlegroups.com" <kg-tutorial@googlegroups.com>, Claudio Gutierrez <cgutierr@dcc.uchile.cl>
Hi all, The following paper is a comprehensive introduction to Knowledge Graphs, aimed at newcomers and non-newcomers alike: https://arxiv.org/abs/2003.02320 The goal of the paper it to try to gather together a variety of perspectives on the issue of Knowledge Graphs. We hope you (or perhaps your students) find it interesting/useful! It is one of the results of a Dagstuhl seminar [1], which was discussed a couple of months ago on the list. The paper was a collaboration effort spanning around a year, with 18 authors involved, including myself, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, José Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, Antoine Zimmermann (in CC). The paper has also benefited from discussion with many other people, particularly some of the other attendees of the seminar. Many thanks to all those who contributed either directly or indirectly! ---------------------------------------------------------------------- <my-2-pesos> Maybe just to end with a personal "take-away" message for me from the seminar and this work ... I think a key aspect of Knowledge Graphs is that they are being explored in parallel by different communities that have not had as much cross-over in the past as they perhaps should have. In particular, Knowledge Graphs could become a confluence of all sorts of techniques, blending queries, ontologies, information extraction, graph algorithms, machine learning, etc., all working within a common framework of applying a graph abstraction to data. (I think other attendees of the seminar came away with the same message [1].) Academically speaking, I think Knowledge Graphs are interesting in the sense that they can become a "confluence" of these areas, raising interesting questions about how to combine ontologies and machine learning, or how to combine queries with graph algorithms, etc. Practically speaking, combining these sorts of techniques is becoming more and more necessary (maybe even "natural") for solving a wide variety of practical problems. To give a small sense of that, I think the video "Knowledge Graphs & Deep Learning at YouTube" [2] is a useful anecdote, talking about using collaborative filtering, deep learning, multimedia, Wikipedia, Wikidata, RDF, SPARQL, entity linking, etc., all towards recommending users videos that they might like to see. (One thing that's leaves the anecdote incomplete is the omission of ontologies, but it's not difficult to see where they might fit in.) </my-2-pesos> Best, Aidan [1] https://www.dagstuhl.de/en/program/calendar/semhp/?semnr=18371 - Report by Eva Blomqvist: http://blog.liu.se/semanticweb/2018/09/15/dagstuhl-seminar-on-knowledge-graphs/ - Report by Paul Groth: https://thinklinks.wordpress.com/2018/09/18/trip-report-dagstuhl-seminar-on-knowledge-graphs/ - Report by Juan Sequeda: http://www.juansequeda.com/blog/2018/09/18/trip-report-on-knowledge-graph-dagstuhl-seminar/ - Seminar report: https://drops.dagstuhl.de/opus/volltexte/2019/10328/ [2] https://www.youtube.com/watch?v=D-bTGefJj0A
Received on Tuesday, 10 March 2020 06:02:53 UTC