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Final Call for Contributions: Knowledge Graphs and E-Commerce Workshop at KDD 2020

From: Mayank Kejriwal <mayankkejriwal@utexas.edu>
Date: Sat, 6 Jun 2020 19:48:16 -0700
Message-ID: <CAKHm4iYXAmh=Kfco55BeOWEiWbW0ODwnXOgFdPMZ6Ba4h3yKzg@mail.gmail.com>
To: Mayank Kejriwal <kejriwal@isi.edu>
Dear Colleague,

Apologies for cross-posting. We have extended the deadline for the workshop
on Knowledge Graphs and E-Commerce
<http://usc-isi-i2.github.io/KDD2020workshop/> at KDD 2020 to *June 15*. In
addition to the full call for contributions (see below), we are also
soliciting *1-2 page extended abstracts* for oral presentation of
significant preliminary results and/or topical areas of discussion (such as
implementations, lessons learned etc.) falling under knowledge graphs and
e-commerce. Please consider submitting if you have such results that you
would like to share or discuss in an oral presentation.



We hope that you will consider submitting your research and sharing it with
colleagues, students, and friends who would be interested. This year, we
already have an exciting lineup of keynote speakers and panelists, and the
workshop’s organizers span both academia and industry (USC, Amazon, Home
Depot and LinkedIn).



In light of the COVID-19 crisis, please stay safe and do contact us at
kejriwal@isi.edu <Kejriwal@isi.edu> should any questions or concerns arise.



Thank You,

Mayank Kejriwal

Research Assistant Professor/Research Lead

University of Southern California

Viterbi School of Engineering

*--------------------------------*

*Call for Contributions:*  KEGC 2020 - International Workshop on Knowledge
Graphs and E-Commerce at the 26th ACM KDD 2020, San Diego, California, USA

*Deadline:* June 15, 2020

*Website*: http://usc-isi-i2.github.io/KDD2020workshop/



This workshop welcomes submissions from both researchers and industry
practitioners in knowledge graphs (KG) and e-commerce, and KG applications,
with a particular emphasis on real-world deployment and pipelines that are
relevant to industrial settings. We solicit research that is broadly
related to e-commerce KG research and sub-fields, including data cleaning
(e.g., Entity Resolution), representation learning and embeddings, natural
language processing (e.g., information extraction) and information
retrieval (e.g., semantic search). *Full paper submissions* (maximum 8
pages) are solicited in the form of research papers which propose new
techniques and advances with industrial potential using data mining
techniques for KGs, as well as papers from industry that describe practical
applications and system innovations in e-commerce application areas. *Short
papers* (maximum 4 pages) describing case studies or work-in-progress are
also solicited. Exceptionally well-argued position papers are also welcome.



Topics motivating research and discussion in dealing with real-world
challenges in e-commerce and product data, include* (but not limited to):*

*Theory, Algorithms and Methods*


•Knowledge graph construction e.g., constructing KGs from structured,
semi-structured and natural language data
• Novel definitions and theories regarding KGs , especially taking into
account attributes and features commonly found in enterprise settings,
including customers, products and spatiotemporal dependencies in KGs.
•Querying and infrastructure of KG-centric architectures and applications
•Effective use of public KGs
•Foundational proposals for content models that combine statistical and
symbolic representations
•Novel embedding algorithms , especially for large-scale KGs
•Statistical learning methods and algorithms for working with noisy KGs
•Data quality assessment for large-scale enterprise KGs

*Applications*


• Web search
• Question answering
• Personalization
• Data Mining
• User interfaces and visualization
•Semantic recommendations
•E-commerce
•Link prediction
•Node classification
•Instance matching/Entity resolution
•Knowledge graph embeddings
• Knowledge graph completion

*Experiments, Systems and Data*


•Novel datasets , especially datasets acquired through, or useful for
evaluating, hybrid KG construction approaches utilizing a combination of
structured, semi-structured and natural language data
•Novel methodologies , concerning both evaluations and data
curation/collection
•Experimental results using existing methods, including negative results of
interest
•Systems issues in KG-centric systems , including best practices, case
studies, lessons learned, and feature descriptions

*Vision, Opinion and Position Papers*

We will also accept a small number of *vision, opinion and position
papers* that
provide discussions on challenges and roadmaps (for KG-centric systems,
applications and emerging models for e-commerce and product data).



For any questions, please contact kejriwal@isi.edu <Kejriwal@isi.edu>.



*Co-Chairs*

Qi He, LinkedIn

Faizan Javed, Home Depot

Andrey Kan, Amazon

Mayank Kejriwal, University of Southern California

Anoop Kumar, Amazon

*--------------------------------*
Received on Sunday, 7 June 2020 02:48:41 UTC

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