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PKG 2021 : Workshop on Personal Knowledge Graphs


When Oct 7, 2021 - Oct 7, 2021
Where Virtual
Submission Deadline Sep 6, 2021
Notification Due Sep 20, 2021
Final Version Due Sep 30, 2021
Categories    knowledge graph   knowledge management   knowledge representation   natural language processing

Call For Papers

PKG: Workshop on Personal Knowledge Graphs,
Co-located with AKBC 2021, Virtual Event, October 7, 2021
For queries:
Submissions due: Sep 6, 2021


The concept of personal knowledge graphs (PKGs) has been around for a while, in recognition of the need to represent structured information about entities that are personally related to a user. However, several open questions remain:

* Definition: The notion of a personal knowledge graph has been established loosely, as a resource of structured information about entities personally related to its user. This definition needs crystallization: What is personal knowledge and how is it represented in a PKG? What differentiates a PKG from general KGs, how are they related? How can PKGs benefit from information stored in general KGs and how is the benefit realised? How is work on PKG related to work in areas such as commonsense KGs and entity/event-centric understanding?

* PKG construction/population: What are the potential data sources (textual, visual, geolocation, etc.)? How to mitigate the 'dual use' of automated technology for PKG construction/population, since it can be used to extract and exploit personal knowledge about others?

* PKG utilities: What novel application scenarios would PKGs enable and what role does/can novel techniques such as semantic technologies and knowledge modeling play in this respect? How do PKG compare to existing solutions to these applications?

* Practical realization: Where would PKGs be stored and how would these interact with a range of external services, while considering access control as well as privacy concerns of users?

The goal of the PKG workshop is to create a forum for researchers and practitioners from diverse areas to present and discuss methods, tools, techniques, and experiences related to the construction and use of personal knowledge graphs, identify open questions, and create a shared research agenda.

We invite submissions of regular papers, position papers, demonstrators as well as encore talks (featuring already published work).

Topics of interest include, but are not limited to:

* Modeling personal knowledge (ontologies and knowledge representation)
* Populating and maintaining personal knowledge graphs (natural language processing, entity and relation extraction, information integration)
* Applications of personal knowledge graphs (including but not limited to intelligent personal assistants, personal information management systems, health information systems, recommender systems)
* Evaluation (evaluation measures and methodology, benchmark construction)
* Systems and toolkits

Important dates (Submission deadlines are 23:59, anywhere on Earth)
Sep 6, 2021: Submissions due
Sep 20, 2021: Paper notifications
Sep 30, 2021: Camera ready deadline
Oct 7, 2021: Workshop day

Submission Guidelines:

We welcome submissions of regular papers (4-6 pages) that present original technical, theoretical, or experimental contributions; position papers (2-4 pages) that explore controversial, risk-taking or nascent ideas that have the potential to spark debate and discussion at the workshop; demonstrator papers (max 4 pages) that present first-hand experience with research prototypes or operational systems. Submissions are single-blind and should use a double-column CEUR-WS proceedings format.

In addition to the paper contributions, we also invite submissions of encore talks, to present work that has already been published in a leading conference or journal, but is relevant to the topics of this workshop. We require a copy of the paper along with a brief (max 200 words) statement of relevance.

Submission site:

Organizing committee:
Krisztian Balog (University of Stavanger, Norway)
Paramita Mirza (Max Planck Institute for Informatics, Germany)
Martin G. Skjæveland (University of Oslo, Norway)
Zhilin Wang (University of Washington, USA)


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