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KGEcom 2020 : Knowledge Graphs and E-commerce workshop, Held in conjunction with KDD 2020

San Diego, CA
Event Date: Aug 24, 2020 - Aug 24, 2020
Submission Deadline: May 20, 2020
Notification of Acceptance: Jun 15, 2020

About

Knowledge Graphs (KGs) have become immensely popular in recent years, both in industry and academia. Although graphs have been ubiquitous in AI and knowledge discovery since the earliest days, the Google Knowledge Graph led to the realization that representing ‘knowledge’ by way of sets of triples, often automatically extracted from raw data, could be used to power rich applications like knowledge panels and semantic search. Academic and industrial interest in the subject flourished soon after, with research published across topics as diverse as Entity Resolution and knowledge graph embeddings (primarily in venues like NIPS, AAAI and KDD), to representation and reasoning over knowledge graphs (primarily in venues like VLDB and ISWC). More recently, with the rapid rise and ongoing growth of e-commerce, there has been growing interest from major retailers and e-commerce players alike, including Walmart, Amazon and Home Depot, to adopt knowledge graphs (or an analogous graph-based technology like Amazon’s Product Graph) for facilitating rich machine learning and e-commerce applications. This workshop will cover knowledge discovery and knowledge graphs, including construction, application and embeddings, primarily for e-commerce and enterprise applications.

Call For Paper

Short and long papers are solicited for the following set of non-exhaustive topics:

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).

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