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The theme of the Summit is to examine KGs from a number of points of view ranging from low-level representation and storage techniques to high-level semantics, and from the vendors to the end users.
 
The theme of the Summit is to examine KGs from a number of points of view ranging from low-level representation and storage techniques to high-level semantics, and from the vendors to the end users.
 
Rather being split into tracks, the Summit will cover a number of relevant areas with a mix of individual speaker sessions and panel discussion sessions. The following are the relevant areas:  
 
Rather being split into tracks, the Summit will cover a number of relevant areas with a mix of individual speaker sessions and panel discussion sessions. The following are the relevant areas:  
 
Knowledge graphs, closely related to ontologies and semantic networks, have emerged in the last few years to be an important semantic
 
technology and research area.  Some of the communities where KGs are relevant are Ontologies, Big Data, Linked Data, Open Knowledge Network, Artificial Intelligence, Deep Learning, and many others.  While related to other semantic
 
technologies, KGs and related notations are widely used for some emerging applications.  The theme of
 
the summit is to examine KGs from a number of points of view ranging from low
 
level representation and storage techniques to high level semantics, and from
 
the vendors to the end users.
 
 
Knowledge graphs are structured representations of semantic knowledge that are stored in a graph.
 
KGs are lightweight versions of semantic networks that scale to massive datasets such as the entire World Wide Web.
 
Industry has devoted a great deal of effort to the development of knowledge graphs,
 
and they are now critical to the functions of intelligent virtual assistants such as Siri and Alexa.
 
 
Rather than divide the Summit into tracks, the Summit will cover a number of
 
relevant areas with a mix of individual speaker sessions and panel discussion
 
sessions.  The following are the relevant areas:
 
 
 
* Background
 
* Background
 
** Definitions
 
** Definitions

Revision as of 13:32, 14 August 2019

[ ]
    (1)
Session Topic Discussion
Duration 1 hour60 minute
3,600 second
0.0417 day
Date/Time August 14 2019 16:00 GMT
9:00am PDT/12:00pm EDT
5:00pm BST/6:00pm CEST
Convener Ken Baclawski

Contents

Agenda     (2A)

Discussion of the Summit Theme. The following is the first draft of the Summit Theme and Description:     (2A1)

Ontology Summit 2020: Knowledge Graphs     (2A2)

Knowledge graphs, closely related to ontologies and semantic networks, have emerged in the last few years to be an important semantic technology and research area. As structured representations of semantic knowledge that are stored in a graph, KGs are lightweight versions of semantic networks that scale to massive datasets such as the entire World Wide Web. Industry has devoted a great deal of effort to the development of knowledge graphs, and they are now critical to the functions of intelligent virtual assistants such as Siri and Alexa. Some of the research communities where KGs are relevant are Ontologies, Big Data, Linked Data, Open Knowledge Network, Artificial Intelligence, Deep Learning, and many others.     (2A3)

The theme of the Summit is to examine KGs from a number of points of view ranging from low-level representation and storage techniques to high-level semantics, and from the vendors to the end users. Rather being split into tracks, the Summit will cover a number of relevant areas with a mix of individual speaker sessions and panel discussion sessions. The following are the relevant areas:     (2A4)

Conference Call Information     (2B)

Participants     (2C)

Proceedings     (2D)

[12:09] Ken Baclawski: Ontology Summit 2020: Knowledge Graphs     (2D2)

Knowledge graphs have emerged in the last few years to be an important semantic technology and research area. While closely related to other semantic technologies, KGs have some advantages for emerging applications. The theme of the summit is to examine KGs from a number of points of view ranging from low level representation and storage techniques to high level semantics, and from the vendors to the end users.     (2D3)

Rather than divide the Summit into tracks, the Summit will cover a number of relevant areas with a mix of individual speaker sessions and panel discussion sessions. The following are the relevant areas:     (2D4)

[12:10] ToddSchneider: Engineering/Manufacturing     (2D14)

[12:14] Mark Underwood @knowlengr: The topics for Columbia's KG conference were:     (2D15)

[12:14] Mark Underwood @knowlengr:     (2D16)

[12:17] Gary: We can add as part of the attraction -an interest in graph-based representation that are accessible to more developers provided a happy medium for scalability, performance, and maintainability, especially for the applications involving big data.     (2D30)

[12:22] Gary: From the NSF Convergence Accelerator (NSF 19-050) site: What is an Open Knowledge Network (OKN)? Knowledge networks are structured representations of semantic knowledge that are stored in a graph. Semantic knowledge graph development has largely been done in industry and is critical to the functions of intelligent virtual assistants such as Siri and Alexa. An Open Knowledge Network will similarly allow stored data to be located and its attributes and relationship to other data and to real-world objects and concepts to be understood at a semantic level; however, it will be an open, shared, public infrastructure that can drive innovation similar to the effects that development of the internet has had.     (2D31)

[12:26] Gary: Sample NSF award for KG work that makes an argument for them: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1528175&ActiveAwards=true&ExpiredAwards=true "...A real sea change in information search is coming! A broad range of new applications are emerging in intelligent policing, personal assistance, individualized healthcare, legal services, scientific literature search, and recently robotics. This project will serve these applications and make fundamental advances in querying heterogeneous knowledge graphs, which are ubiquitous. It is going to significantly ease query formulation and improve search quality/speed in these applications.     (2D32)

Given the high data heterogeneity in knowledge graphs, writing structured queries that fully comply with data specification is extremely hard for ordinary users, while keyword queries can be too ambiguous to reflect user search intent. The situation becomes even worse when there are various representations for the same entity or relation. It is expected that a sophisticated query system shall be able to support different concept representations without forcing users to use very controlled vocabulary. It shall provide simple mechanisms to users so that they can quickly come up with a right query either explicitly or implicitly (e.g., via relevance feedback). This proposal is going to develop such system, make it user-friendly and scalable. The proposed research includes a plan to build a flexible query benchmark that is able to cope with heterogeneous, large-scale knowledge graphs, as well as user specified configurations and performance metrics. Benchmarks are indispensable for rapid development of database research. There were many successful examples of how robust and meaningful benchmarks can greatly expedite the development of a research area. The query benchmark proposed in this project is very needed. It is going to (1) provide a standardized way to fairly and comprehensively evaluate different knowledge graph query algorithms, (2) improve the understanding of the existing query engines, and (3) advance the area by getting researchers involved in the same play ground for building better, faster, and more intelligent methods.     (2D33)

[12:28] Mark Underwood @knowlengr: This paper refers to deriving an ontology *from* a knowledge graph / https://www.osti.gov/servlets/purl/1424501     (2D34)

[12:30] Mark Underwood @knowlengr: Maybe "Some believe that ..."     (2D36)

[12:32] RaviSharma: Gary's posting needs attention     (2D37)

[12:32] RaviSharma: How well do we know what the knowledge graphs are?     (2D38)

[12:33] RaviSharma: John Sowa says KGs are subset of FOL.     (2D39)

[12:34] RaviSharma: John said half century ago semantic networks, 1960's     (2D40)

[12:35] RaviSharma: dependency graphs in linguistics 1920's     (2D41)

[12:35] RaviSharma: Databases 1970's codasyl db used KG     (2D42)

[12:36] RaviSharma: John continues 1st cent AD is where it all started?     (2D43)

[12:36] RaviSharma: 1960's and 70's     (2D44)

[12:37] RaviSharma: network graph and IMS network graph     (2D45)

[12:37] RaviSharma: All above from John path based queries     (2D46)

[12:40] RaviSharma: janet - asked and John said new thing DAML Berners Lee and Jim Handler - 5 yrs for DoD wanted more powerful - Pat Hayes, Sowa, Cyc Werty others worked beyond DAML.     (2D47)

[12:41] RaviSharma: John basically these are close like is RDF     (2D48)

[12:43] RaviSharma: John said Ontology 3rd AD Aristotle Hierarchy and today ontology ?     (2D49)

[12:44] RaviSharma: Janet -made comments on history vs new interest - incorporated in meeting page     (2D50)

[12:45] John Sowa: Knowledge graphs are version of semantic networks used to represent and reason about big data     (2D51)

[12:46] RaviSharma: closely related to RDF and OODB says John     (2D52)

[12:47] RaviSharma: Gary - a lightweight version     (2D53)

[12:48] RaviSharma: John - reasoning method, other technology areas,     (2D54)

[12:48] RaviSharma: John and Ram - they are designed to scale to WWWeb     (2D55)

[12:49] RaviSharma: old versions do not scale     (2D56)

[12:49] RaviSharma: then you can apply these to things like Watson or Jeopardy.     (2D57)

[12:50] Mark Underwood @knowlengr: Suggested: Can ontologies be derived from mature uses of graphs e.g., decision trees, cyberdefense playbooks, flowcharts, network topologies, fault trees, dependency trees     (2D58)

[12:50] RaviSharma: Gary - Big Data     (2D59)

[12:52] Mark Underwood @knowlengr: + execution trees     (2D60)

[12:53] RaviSharma: John - ref Stonebraker - all are algorithms whether to trees and tables.     (2D61)

[12:55] RaviSharma: John and Gary - may be he can be a speaker.     (2D62)

[12:56] RaviSharma: John - ontology vs schema is same     (2D63)

[12:57] Mark Underwood @knowlengr: for marketing, these other types of trees will be of interest to other specialists     (2D64)

[12:58] Gary: I was briefly hired by Stonebraker when he was CTO on Informix to by on his technical staff...before he was pushed out by IBM that bought Informix around 2000.     (2D65)

[12:59] RaviSharma: John Sowa - KR Book is out of Print but print on demand.     (2D66)

[13:03] RaviSharma: John - KGs are raw materials, Jeopardy Watson, for Qs bring detailed data for reasoning for relevant subset.     (2D67)

[13:07] Gary: I like using the broader term reasoning of which inference is one type.     (2D68)

[13:09] RaviSharma: Jans on 4th, John on Sept 11 and others later.     (2D70)

[13:11] RaviSharma: Janet - communities that do focused research and industries that use them range from abstract to industry use. Ken and Todd said balance is needed.     (2D71)

[13:12] RaviSharma: I feel some discussion is needed on the list on today's meeting page even though no tracks are planned, then it is more difficult to balance these areas.     (2D72)

Resources     (2E)

Previous Meetings     (2F)


Next Meetings     (2G)