Questions to address, now that many OKN initiatives are underway:
+ What's known but missing, what needs more adoption, what's still an open challenge?
+ What norms and practices are needed for different topic areas?
+ When I contribute to an OKN, how can I know how widely it is accessible?
+ How to share namespaces for models, shapes, functions
Outputs: Review+ catalog of current tools; strengthening community across the networks.
<another 2 paras of description here>
<who should come + why>
1 keynote, 2 invited provocations
6 invited papers w/5m talks, 6 invited working demos
A closing workgroup to compile a summary report.
An open call for papers and tools to include in reading materials.
Lara Campbell [NSF], Guha [Google], Mike, SJ, Lydia Pintscher...
From our meta-libraries [Internet Archive] - Bryan Newbold?
From the decentralized web [IPFS] -
From specialist graphs:
Social science + Individuals - Julia Lane's Rich Context grew (Dan Mbanga?)
GIS + location data - ?
Environmental data - ?
Bio data [Broad Institute, Allen, +] - ?
Publication + reference data [SemScholar, Lens, Meta] - Lucy Lu Wang
Knowledge graph + related tech:
Wikidata: Magnus Manske [Wikidata+], Kingsley Idehen [dbpedia]
Extraction: Sarah Chastens
[Truth] evaluation: Duke Lab?
BinderHub/Notebook ecosystem: Yuvi Panda
Live demo stations before lunch: full-track tutoriald and demos of existing work, lined up along workflow lines: from source to annotation to application programming.
Summary working group in the afternoon: compiling a report from the day w/ recommendations, via full-room discussion. (or one breakout group per ~15 people)
+ What are the top five features needed to support OKN verticals?
+ Walk through stages in the knowledge lifecycle: sharing, contextualizing, sifting, mining, querying, disambiguating, aggregating
Adversarial panel: approaches to challenges of adversarial data environments, with disinformation, database and model poisoning, and more.
~ Sharing: Trace a new dataset through genesis, description, compression, publishing, indexing, notification, processing, updating
~ Sifting, coloring: Trace tags over time: identity, authority, review, accuracy, relevance, currency, other qualities
~ Mining: Trace refactoring and reuse over time: first-order extraction, second-order cross-correlation, third-order statistical extraction, updates
~ Finding: Trace a query through networks, time, space: as it is compiled, cached + mirrored, named + referenced, updated + versioned + annotated.
~ Prov: What does the profession of provenance enriching + confirmation look like, now and tomorrow?
~ Trust: What does an optimal high-trust network look like, for a constellation of well aligned participants?
~ Mistrust: What does a no-trust network look like, for a universe of personal knowledge clients protecting what you know or think you know, under adversarial conditions