Session	Contexts in the Open Knowledge Network
Duration	2 hours
Date/Time	Feb 14 2018 17:00 GMT
	9:00am PST/12:00pm EST
	5:00pm GMT/6:00pm CET
Convener	RamSriram and GaryBergCross

Ontology Summit 2018 Contexts in the Open Knowledge Network Session 1

Agenda

Introduction to the Open Knowledge Network (OKN) Note: Some overview material on OKN was provided by Gary Berg-Cross as part of the Summit Launch session in January and not be repeated here. For people who missed this session which featured introductions to the summit tracks by the track co-champions, with John Sowa as the session convener see that site which include Intro slides to OKN.

Motivation for a Knowledge Network

Natural interfaces to large knowledge structures have the potential to impact science, education and business to an extent comparable to the WWW. We have already seen a 1st wave of this in consumer services such as Siri, Cortana and Alexa. But these services are largely private, limited in their scope of knowledge, not open to direct access or contributors beyond their corporate firewalls, and can only answer relatively limited questions in particular business areas. Can we establish a larger & open knowledge network to enable another generation of applications? Among the questions to consider is how to leverage & evolve existing efforts such as Schema.org, the role of knowledge graphs and how to enrich them to support a wider, open vision. Three distinguished researchers have been invited to present their ideas on these topics and discuss context as an ingredient of this vision.

Speakers:

Vicki Tardif Holland (Google) Schema.org and OKN Slides in PDF format
- Abstract: Schema.org provides a rich vocabulary for people, places, events, as well as providing a formal extension mechanism for adding complexity as needed. Schema.org is already used across the web but we can ask, "Can something like OKN use schema.org as a base to build off of?" "If not as it currently exist, what is missing?"
- Vicki Tardif Holland is an active contributor to schema.org and has been a member of the steering group since 2014. She also works on Google's Knowledge Graph team, helping to extend the underlying ontology. Vicki has an MLIS from Simmons College and a bachelor's degree in Computer Science from the Massachusetts Institute of Technology.
Ramanathan Guha: OKN Overview (website CV ) Slides in PDF format
Mayank Kejriwal (ISI) Context-rich Social Uses of Knowledge Graphs ( website CV ) Slides in PDF format
- Abstract: Knowledge graphs (KGs) have emerged as an excellent foundation for capturing both background and domain-specific knowledge in structured, machine-readable formats. Thus far, the ability of KGs to capture large quantities of background information has been extensively covered in the literature, with discussions on domain-specific KGs limited mostly to the domain sciences. In this talk, I will show that domain-specific KGs can also be used for building advanced decision support systems in sensitive investigative domains like human trafficking and securities fraud, and that such KGs prove to be potent AI for Social Good frameworks. In order to construct and use such KGs effectively, one must take the tri-partite context of the user, the domain and the raw data quality (both in terms of ‘coverage’ as well as ‘automation difficulty’) into account. I will also briefly cover an emergent architectural framework, called the Domain-specific Insight Graphs (DIG), that manages to take such contexts into account at critical junctures. Finally, I will highlight opportunities for robustly incorporating such a framework into the OKN ecosystem.

Video Recording of the Session

Conference Call Information

Date: Wednesday, 14-February-2018
Start Time: 9:00am PST / 12:00pm EST / 6:00pm CET / 5:00pm GMT / 1700 UTC
- World Clock
Expected Call Duration: ~2 hours

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Attendees

Proceedings

Sample Issues suggested to our speakers included:

What knowledge do we need to support this OKN vision?
What would be the nature of these KBs and their Knowledge representation?
What is the nature of the k-graph & how axiom-rich (formal) would these KBs be?
Can we/should we build on Schema.org or the like?
What quality ontologies are available as part of this effort?
What supporting technologies do we need, including use of rapidly advancing Machine learning (ML) technology which may help in extracting, and developing public knowledge bases from a variety of forms.
How do manage and maintain KBs of open knowledge as we leverage new input and related sources of informal information such as metadata annotation?
How do we handle big, noisy data for portions of reality described in many contexts?
How do we handle fitness for many uses in many different contexts?......

The OKN white paper (OKN: Open Knowledge Network: Creating the Semantic Information Infrastructure for the Future by RV Guha, Schema.org; Andrew Moore, Carnegie Mellon University) identifies some "Roles for the Research, Commercial, and Government Sectors":

For example, Researchers could contribute by:

Collecting and incorporating facts/assertions from new sources.
Developing new big data technologies for aggregating, disambiguating, and resolving references and maintaining provenance (the history of where a concept or relationship came from).
Providing support for cross-domain inferences.
Addressing, in an open academic forum, the design decisions around privacy and societal expectations regarding storage and dissemination of knowledge. For example, there would be well-justified and grave public concern if a politically charged historical account was to be included as a fact rather than a reported assertion. This is a topic for linguists, digital humanities experts and ethicists to work on in collaboration with computer scientists and statisticians.
Studying scenarios for supporting multiple schemas created with the same data.
Studying how to support free text assertions (schemas), and how they can be treated as evidence of knowledge.
How to transcend from “narrow AI” to “broad AI”? How to efficiently learn and transfer structure, knowledge, and experience from one application domain to another? A system like IBM Watson, for example, is really a family of siloed knowledge bases. Can OKN lead to insights on how a common knowledge infrastructure could be created to make this knowledge transfer across domains much more easy and efficient?
A central design around dynamic growth of knowledge: ** how to verify and modify existing assertions, when new assertions come in.
Though OKN is an open system, it may include links to proprietary knowledge bases.

How does one address security, access control, knowledge representation, and inference in such an environment?

How does one combine proprietary facts with “open facts” in an open architecture?
Implementing compliance with legal/contractual constraints, incorporating the issues of who owns the data (therefore, who might own the derived information); who has the right to use the data and its derivations; and for what purpose, etc.?
Defining “Grand Challenges” related to populating and use of the knowledge network.
Multilingual knowledge. We have the opportunity to engineer the system to provide knowledge that can be used to support dialogues in any world language.

Resources

[[1]] Link to Knoblock Graphic on Building a Knowledge Graph

The following references were provided at the Launch Session: OKN White paper: http://ichs.ucsf.edu/wp-content/uploads/2017/08/OKN-White-Paper..docx] Open Knowledge Network, A.W. Moore & R.V.Guha [https://www.nitrd.gov/nitrdgroups/images/9/96/OKN_Moore_Guha.pdf Andrew Moore on "TOKeN: The Open Knowledge Network" [2] Schema.org: Evolution of Structured Data on the Web, [3] Baru, Chaitan. "Harnessing the Data Revolution." (2017). [4]

"Knowledge graphs for social good: An entity-centric search engine for the human trafficking domain." IKejriwal, Mayank, and Pedro Szekely. EEE Transactions on Big Data (2017). [5]

NITRD Big Data Interagency Working Group: 3rd Workshop on an Open Knowledge Network (2017). [6]

The (slightly edited) chat transcript is available at ConferenceCall_2018_02_14/Chat

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