Broadening the base ideas

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The document discusses ways to broaden the use of ontologies and semantic technologies. Some of the key points discussed include: 1. The need for robust and applicable methods from domain experts to help with tasks like ontology acquisition and development to avoid common mistakes. 2. The importance of identifying and documenting best practices and tools available for different phases of ontology engineering. 3. The potential benefit of developing a front-end visualization application for ontology and RDF data to help lower barriers and improve understanding. 4. Addressing issues like unclear semantics around rdfs:domain and rdfs:range that can hinder understanding and lead to over-constraining ontologies.

Technology

Questions:
1.How can we lower the entrance hurdle?
2.Can we improve tool support; which tools are missing?
3.What are the lessons learned in designing our current technology
stack that we can apply in the future?
4.How do we improve support for scope (time, space,...) and
probability/uncertainty?
5.When does reasoning actually matter?
Breakout I: Broadening the base
Raw notes:
https://docs.google.com/document/d/1SHZMpiDsrtBpEaXQOQrAuV11VgTbMK96w0Q1Wh28oqg/edit#

Ideas (and notes) Breakout I: Broadening the base
Notes: David Booth
https://docs.google.com/document/d/1SHZMpiDsrtBpEaXQOQrAuV11VgTbMK96w0Q1Wh28oqg/edit#
1. Need robust,applicable methods from domain experts to ontologies
(acquisition to product building - common mistakes..misuse of domain & range)
● what tools are good for each phase/task of ont engineering?
2. Best practices: what tools are available? Need a BP summary...
3. Front-end visualization App needed for RDF/ontologized data
● “Semantic” Zoom-in/out like google maps ...need low hanging fruit?
4. The Open WA can be a major barrier to understanding
– rdfs:domain and rdfs:range are not constraints!
– But users expect them to be...and we can also over constrain...
5. RDF/Turtle with JSON-LD Need long range perspective –
Reusable modeling versus (JSON) encoding..maybe JSON APIs?
6. Mimic how SQL was adopted (training, books 4 developers..)
1. Cookbooks/reuse maybe ODPs will also building blocks.

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Broadening the base ideas

1. Questions: 1.How can we lower the entrance hurdle? 2.Can we improve tool support; which tools are missing? 3.What are the lessons learned in designing our current technology stack that we can apply in the future? 4.How do we improve support for scope (time, space,...) and probability/uncertainty? 5.When does reasoning actually matter? Breakout I: Broadening the base Raw notes: https://docs.google.com/document/d/1SHZMpiDsrtBpEaXQOQrAuV11VgTbMK96w0Q1Wh28oqg/edit#

2. Ideas (and notes) Breakout I: Broadening the base Notes: David Booth https://docs.google.com/document/d/1SHZMpiDsrtBpEaXQOQrAuV11VgTbMK96w0Q1Wh28oqg/edit# 1. Need robust,applicable methods from domain experts to ontologies (acquisition to product building - common mistakes..misuse of domain & range) ● what tools are good for each phase/task of ont engineering? 2. Best practices: what tools are available? Need a BP summary... 3. Front-end visualization App needed for RDF/ontologized data ● “Semantic” Zoom-in/out like google maps ...need low hanging fruit? 4. The Open WA can be a major barrier to understanding – rdfs:domain and rdfs:range are not constraints! – But users expect them to be...and we can also over constrain... 5. RDF/Turtle with JSON-LD Need long range perspective – Reusable modeling versus (JSON) encoding..maybe JSON APIs? 6. Mimic how SQL was adopted (training, books 4 developers..) 1. Cookbooks/reuse maybe ODPs will also building blocks.

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