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== Proceedings ==
 
== Proceedings ==
 +
[12:21] RaviSharma: terry suggested combining option 1 and 3 topics
 +
 +
[12:22] RaviSharma: Terry- graphics more sessions and one on metaphor!
 +
 +
[12:23] Ken Baclawski: Terry suggested having one session of the summit devoted to analogy and metaphor.
 +
 +
[12:24] RaviSharma: Todd- may use it with standards
 +
 +
[12:25] RaviSharma: RDBMS to Graph Databases, distinction is coherence of schema.
 +
 +
[12:26] RaviSharma: Gary - structure in ontologies?
 +
 +
[12:27] RaviSharma: Gary - perhaps Ram would agree
 +
 +
[12:27] DouglasRMiles: wanted to confirm everyone knows about this https://dev.grakn.ai/docs/general/quickstart
 +
 +
[12:28] TerryLongstreth: John Sowa's recent comment in Ontolog: On 7/8/2019 4:17 PM, bruceschuman@cox.net wrote:
 +
 +
"Thinking outside the table" -- on the power of graph databases vs. relational databases
 +
https://www.slideshare.net/ontotext/thinking-outside-the-table
 +
 +
The person who wrote that is intelligent, but hopelessly ignorant
 +
about five things:  (1) logic; (2) databases; (3) data structures;
 +
(4) methods for defining #1, #2, and #3; and methods for implementing
 +
#1, #2, #3, and #4.
 +
 +
From a logical point of view (#1), there is no difference whatsoever
 +
between a graph and a collection of tables.  From the point of view
 +
of the other four, it all depends on the application.
 +
 +
John
 +
 +
[12:28] DouglasRMiles: Specifically they have 20+ users active developers at any one day
 +
 +
[12:30] ToddSchneider: Common definition of 'Knowledge': facts, information, and skills acquired by a person through experience or education; the theoretical or practical understanding of a subject.
 +
 +
[12:30] TerryLongstreth: So, Todd, Knowledge graphs are ?
 +
 +
[12:31] Gary: Some standard topics on KGs:                                                                                                                                           
 +
 +
* Construction and Maintenance of Knowledge Graphs
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** Handling noisy and incomplete data
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** Entity linking and resolution
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** Consistency checking when adding new knowledge
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** Collaborative maintenance of knowledge graphs
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** Provenance solutions for Knowledge Graphs
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** Handling uncertain content in Knowledge Graphs
 +
 +
* Operations over Knowledge Graphs
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** Innovative methods for querying and interacting with knowledge graphs
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** Searching over knowledge graphs
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** Reasoning over knowledge graphs
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** Explaining knowledge graph contents
 +
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* Mining Knowledge Graphs
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** Machine Learning and deep learning techniques for knowledge graph mining
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** Heterogeneous graph mining
 +
 +
* Storage mechanisms for Knowledge Graphs
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** Graph databases, triple stores
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** New storage and indexing schemes for property graphs
 +
 +
* Knowledge Graphs for NLP and IR
 +
** Web search, conversational agents, recommender systems, information access systems, and summary generation
 +
 +
* Knowledge Graphs in the industry
 +
** manufacturing, aviation, power, oil and gas, healthcare, banking, finance, and IoT
 +
** Industry use cases and best practices
 +
 +
[12:32] RaviSharma: measures of semantic distances among graphs - Todd
 +
 +
[12:34] RaviSharma: Ravi- how do you measure semantic distance. In Statistics, Mahalanobis distance is known, also in multivariate space but what is semantic distance?
 +
 +
[12:36] RaviSharma: similarities will imply shorter distance for example - Gary and Todd
 +
 +
[12:39] Ken Baclawski: Douglas can you unmute yourself?
 +
 +
[12:39] DouglasRMiles: I switched headsets with my daughter .. evidently the mic doesn't work. its good though
 +
 +
[12:40] RaviSharma: Todd and Gary - representation of knowledge, specific answers to Qs like in Google Graphs, relationship to ontologies,
 +
 +
[12:41] DouglasRMiles: I going to see if I can find it in her room.. but basically my question was answered.
 +
 +
[12:42] RaviSharma: The Knowledge Graph is a knowledge base used by Google and its services to enhance its search engine's results with information gathered from a variety of ...
 +
Knowledge base  Knowledge engine  Ontology  Freebase
 +
 +
[12:46] TerryLongstreth: Got to leave for my 1 o'clock
 +
 +
[12:48] Gary: Regarding quality of a KB  -whichever approach is taken for constructing a knowledge graph, the result will never be perfect [10].
 +
As a model of the real world or a part thereof, formalized knowledge cannot reasonably reach full coverage,
 +
i.e., contain information about each and every entity in
 +
the universe. Furthermore, it is unlikely, in particular when heuristic methods are applied, that the knowledge
 +
graph is fully correct  there is usually a trade-off between coverage and correctness....
 +
 +
[12:49] RaviSharma: What is the disconnect between KG and Ontology? -Todd
 +
 +
[12:50] RaviSharma: Can a subtopic be visual representation?
 +
 +
[12:50] Gary: Ref for KB building and refining : Antoine Bordes and Evgeniy Gabrilovich. Constructing and Mining Web-scale Knowledge Graphs. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 19671967, New York, 2014. ACM. http://dx.doi.org/10.1145/ 2623330.2630803.
 +
 +
[12:52] RaviSharma: Most people did not agree with visual representation
 +
 +
[12:53] RaviSharma: Janet- KG as engineering artifacts?
 +
 +
[12:53] RaviSharma: ontologies and the KG?
 +
 +
[12:54] ToddSchneider: Knowledge graphs engineered via ontologies
 +
 +
[12:54] Gary: Leveraging Ontologies for Knowledge Graph Schemas
 +
 +
Daniela Oliveira, Ratnesh Sahay, Mathieu d'Aquin
 +
 +
06 Mar 2019 (modified: 15 Apr 2019)ESWC 2019 Workshop KGB
 +
 +
Keywords: Knowledge Graphs, Semantic Web, Ontologies
 +
 +
In this paper, we propose a method to build a schema for Knowledge Graphs that leverages ontologies by integrating them in a single unified graph, enriched by an ontology matching step.
 +
 +
Abstract: Knowledge Graphs have emerged as a core technology to enhance the search experience of users in research and industry alike. Applications are being developed to build and exploit graph structures in data repositories, to improve search and integration of knowledge. However, data is available on the Web in diverse formats and integrating data from different sources is still an open research area. In this paper, we propose a method to build a schema for Knowledge Graphs that leverages ontologies by integrating them in a single unified graph, enriched by an ontology matching step.  We evaluate this method by comparing the different stages of graph structures and measuring structural properties of the resulting graphs.
 +
We also propose an approach that groups ontology classes by conceptual type, showcasing how an underlying ontology-based schema can enhance a Knowledge Graph. The results demonstrate the viability of this approach, highlighting how the chosen techniques based on edge addition successfully make our ontology-based knowledge graph schema more complete and tightly connected.
 +
 +
[12:55] DouglasRMiles: For sure the authors of Grakn would love to do a talk on Graql
 +
 +
[12:55] RaviSharma: Ravi- could a subtopic be  relationship between FOL and KG?
 +
 +
[12:56] DouglasRMiles: Sure! that wont preclude any other useful things
 +
 +
[12:57] RaviSharma: Ravi - another subtopic be KG, ontology and standards relating to them?
 +
 +
[12:58] RaviSharma: semantic graphs - Todd
 +
 +
[12:58] Mark Underwood: Here's the paper I mentioned https://queue.acm.org/detail.cfm?id=3332266
 +
 +
[12:59] RaviSharma: Semantic Web, -Gary
 +
 +
[12:59] RaviSharma: Janet- Barry Smith and standards bodies.
 +
 +
[13:03] Gary: Other types of graphs - conceptual graphs as a type of semantic network.
 +
 +
[13:04] Mark Underwood: @Ken - That's cool RE Natasha
 +
 +
[13:05] Mark Underwood: Columbia hosted a Knowledge Graphs conference earlier this yr https://sps.columbia.edu/academics/seminars-executive-education/programs-individuals/knowledge-graph-conference
 +
 +
[13:05] RaviSharma: Ravi sent a PDF presentation on standards: [http://bit.ly/2XHyglr Presentation on Ontology Standards by Ravi Sharma]
 +
 +
[13:05] Mark Underwood: I think I mentioned this last week
 +
 +
[13:06] Mark Underwood: Pasting the topics list from that conf below:
 +
 +
* Storing and Querying Knowledge Graphs
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* Formats and Languages
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* Metadata, Schemas, Ontologies, and Taxonomies
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* Data Governance
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* Data Quality
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* Linked-data
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* Master Data Management
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* Knowledge Graphs for AI
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* Natural Language Processing
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* Understanding Knowledge Graph Embeddings
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* Visualization
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* Search and Answer Engine Optimization
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* Applications in Healthcare, Finance, Media, and Open Data
 +
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[13:07] Mark Underwood: @Ravi - I notice they included visualization
 +
 +
[13:07] RaviSharma: thanks Mark
  
 
== Resources ==
 
== Resources ==

Revision as of 10:47, 17 July 2019

[ ]
    (1)
Session Topic Discussion
Duration 1 hour60 minute
3,600 second
0.0417 day
Date/Time July 10 2019 16:00 GMT
9:00am PDT/12:00pm EDT
5:00pm BST/6:00pm CEST
Convener Ken Baclawski

Contents

Agenda     (2A)

The following agenda items may require multiple meetings.     (2A1)

  1. Discussion of the three topic areas     (2A3)
    1. Ontologies and standards Mike Bennett     (2A3B)
    2. Analogy and Metaphor David Whitten (to be confirmed)     (2A3C)
  2. Selection of the topic area     (2A4)
  3. Choose tracks and track champions     (2A5)
  4. Draft summit mission statement     (2A6)
  5. Choose tentative summit title     (2A7)

Ontology Summit Topic Suggestions     (2B)

  • Knowledge graphs and ontologies     (2B1)
  • Ontologies and standards (revisiting Ontology Summit 2009)     (2B2)
  • Analogy and Metaphor     (2B3)
    • "Metaphor is for most people device of the poetic imagination and the rhetorical flourish--a matter of extraordinary rather than ordinary language. Moreover, metaphor is typically viewed as characteristic of language alone, a matter of words rather than thought or action. For this reason, most people think they can get along perfectly well without metaphor. We have found,on the contrary, that metaphor is pervasive in everyday life, not just in language but in thought and action. Our ordinary conceptual system, in terms of which we both think and act, is fundamentally metaphorical in nature." from "Metaphors We Live By" by George Lakoff and Mark Johnson 1980     (2B3A)
    • "One of the main contentions of this book is that the cognitive mechanisms involved in metaphor may prove to be a better paradigm for structuring knowledge that that of first-order logic." from the Preface to "Knowledge Representation and Metaphor" by Eileen Cornell Way 1991     (2B3B)
    • "Analogy, metaphor, representation and user interface have much in common: each involves signs, meaning, one or more people, and some context ..." "An introduction to algebraic semiotics, with application to user interface design" by Joseph Goguen 1999     (2B3C)
    • Champion (during planning sessions): David Whitten     (2B3D)

Conference Call Information     (2C)

Participants     (2D)

Proceedings     (2E)

[12:21] RaviSharma: terry suggested combining option 1 and 3 topics     (2E1)

[12:22] RaviSharma: Terry- graphics more sessions and one on metaphor!     (2E2)

[12:23] Ken Baclawski: Terry suggested having one session of the summit devoted to analogy and metaphor.     (2E3)

[12:24] RaviSharma: Todd- may use it with standards     (2E4)

[12:25] RaviSharma: RDBMS to Graph Databases, distinction is coherence of schema.     (2E5)

[12:26] RaviSharma: Gary - structure in ontologies?     (2E6)

[12:27] RaviSharma: Gary - perhaps Ram would agree     (2E7)

[12:27] DouglasRMiles: wanted to confirm everyone knows about this https://dev.grakn.ai/docs/general/quickstart     (2E8)

[12:28] TerryLongstreth: John Sowa's recent comment in Ontolog: On 7/8/2019 4:17 PM, bruceschuman@cox.net wrote:     (2E9)

"Thinking outside the table" -- on the power of graph databases vs. relational databases https://www.slideshare.net/ontotext/thinking-outside-the-table     (2E10)

The person who wrote that is intelligent, but hopelessly ignorant about five things: (1) logic; (2) databases; (3) data structures; (4) methods for defining #1, #2, and #3; and methods for implementing     (2E11)

  1. 1, #2, #3, and #4.     (2E12)

From a logical point of view (#1), there is no difference whatsoever between a graph and a collection of tables. From the point of view of the other four, it all depends on the application.     (2E13)

[12:28] DouglasRMiles: Specifically they have 20+ users active developers at any one day     (2E15)

[12:30] ToddSchneider: Common definition of 'Knowledge': facts, information, and skills acquired by a person through experience or education; the theoretical or practical understanding of a subject.     (2E16)

[12:30] TerryLongstreth: So, Todd, Knowledge graphs are ?     (2E17)

[12:31] Gary: Some standard topics on KGs:     (2E18)

  • Knowledge Graphs for NLP and IR     (2E23)
    • Web search, conversational agents, recommender systems, information access systems, and summary generation     (2E23A)

[12:32] RaviSharma: measures of semantic distances among graphs - Todd     (2E25)

[12:34] RaviSharma: Ravi- how do you measure semantic distance. In Statistics, Mahalanobis distance is known, also in multivariate space but what is semantic distance?     (2E26)

[12:36] RaviSharma: similarities will imply shorter distance for example - Gary and Todd     (2E27)

[12:39] Ken Baclawski: Douglas can you unmute yourself?     (2E28)

[12:39] DouglasRMiles: I switched headsets with my daughter .. evidently the mic doesn't work. its good though     (2E29)

[12:40] RaviSharma: Todd and Gary - representation of knowledge, specific answers to Qs like in Google Graphs, relationship to ontologies,     (2E30)

[12:41] DouglasRMiles: I going to see if I can find it in her room.. but basically my question was answered.     (2E31)

[12:42] RaviSharma: The Knowledge Graph is a knowledge base used by Google and its services to enhance its search engine's results with information gathered from a variety of ... Knowledge base Knowledge engine Ontology Freebase     (2E32)

[12:46] TerryLongstreth: Got to leave for my 1 o'clock     (2E33)

[12:48] Gary: Regarding quality of a KB -whichever approach is taken for constructing a knowledge graph, the result will never be perfect [10]. As a model of the real world or a part thereof, formalized knowledge cannot reasonably reach full coverage, i.e., contain information about each and every entity in the universe. Furthermore, it is unlikely, in particular when heuristic methods are applied, that the knowledge graph is fully correct there is usually a trade-off between coverage and correctness....     (2E34)

[12:49] RaviSharma: What is the disconnect between KG and Ontology? -Todd     (2E35)

[12:50] RaviSharma: Can a subtopic be visual representation?     (2E36)

[12:50] Gary: Ref for KB building and refining : Antoine Bordes and Evgeniy Gabrilovich. Constructing and Mining Web-scale Knowledge Graphs. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 19671967, New York, 2014. ACM. http://dx.doi.org/10.1145/ 2623330.2630803.     (2E37)

[12:52] RaviSharma: Most people did not agree with visual representation     (2E38)

[12:53] RaviSharma: Janet- KG as engineering artifacts?     (2E39)

[12:53] RaviSharma: ontologies and the KG?     (2E40)

[12:54] ToddSchneider: Knowledge graphs engineered via ontologies     (2E41)

[12:54] Gary: Leveraging Ontologies for Knowledge Graph Schemas     (2E42)

Daniela Oliveira, Ratnesh Sahay, Mathieu d'Aquin     (2E43)

06 Mar 2019 (modified: 15 Apr 2019)ESWC 2019 Workshop KGB     (2E44)

Keywords: Knowledge Graphs, Semantic Web, Ontologies     (2E45)

In this paper, we propose a method to build a schema for Knowledge Graphs that leverages ontologies by integrating them in a single unified graph, enriched by an ontology matching step.     (2E46)

Abstract: Knowledge Graphs have emerged as a core technology to enhance the search experience of users in research and industry alike. Applications are being developed to build and exploit graph structures in data repositories, to improve search and integration of knowledge. However, data is available on the Web in diverse formats and integrating data from different sources is still an open research area. In this paper, we propose a method to build a schema for Knowledge Graphs that leverages ontologies by integrating them in a single unified graph, enriched by an ontology matching step. We evaluate this method by comparing the different stages of graph structures and measuring structural properties of the resulting graphs. We also propose an approach that groups ontology classes by conceptual type, showcasing how an underlying ontology-based schema can enhance a Knowledge Graph. The results demonstrate the viability of this approach, highlighting how the chosen techniques based on edge addition successfully make our ontology-based knowledge graph schema more complete and tightly connected.     (2E47)

[12:55] DouglasRMiles: For sure the authors of Grakn would love to do a talk on Graql     (2E48)

[12:55] RaviSharma: Ravi- could a subtopic be relationship between FOL and KG?     (2E49)

[12:56] DouglasRMiles: Sure! that wont preclude any other useful things     (2E50)

[12:57] RaviSharma: Ravi - another subtopic be KG, ontology and standards relating to them?     (2E51)

[12:58] RaviSharma: semantic graphs - Todd     (2E52)

[12:58] Mark Underwood: Here's the paper I mentioned https://queue.acm.org/detail.cfm?id=3332266     (2E53)

[12:59] RaviSharma: Semantic Web, -Gary     (2E54)

[12:59] RaviSharma: Janet- Barry Smith and standards bodies.     (2E55)

[13:03] Gary: Other types of graphs - conceptual graphs as a type of semantic network.     (2E56)

[13:04] Mark Underwood: @Ken - That's cool RE Natasha     (2E57)

[13:05] Mark Underwood: Columbia hosted a Knowledge Graphs conference earlier this yr https://sps.columbia.edu/academics/seminars-executive-education/programs-individuals/knowledge-graph-conference     (2E58)

[13:05] RaviSharma: Ravi sent a PDF presentation on standards: Presentation on Ontology Standards by Ravi Sharma     (2E59)

[13:05] Mark Underwood: I think I mentioned this last week     (2E60)

[13:06] Mark Underwood: Pasting the topics list from that conf below:     (2E61)

[13:07] Mark Underwood: @Ravi - I notice they included visualization     (2E75)

[13:07] RaviSharma: thanks Mark     (2E76)

Resources     (2F)

Previous Meetings     (2G)


Next Meetings     (2H)