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WELCOME TO KGC 2021
If you want to learn more about advancements in knowledge graph technology, the Knowledge Graph Conference is the place to be! This year, we will be joined by some of the brightest minds in the knowledge graph industry. We have speakers from major companies such as Amazon, Facebook, Microsoft, Google, Wells Fargo, and many other remarkable companies! We promise you won't want to miss this opportunity! 
Join us on May 3 - May 6 at KGC 2021 to learn more about knowledge graphs, graph databases, graph AI, and semantic tech!
Early Bird Tickets Ends April 12th! 
BUY YOUR TICKET NOW

Introducing: Sponsors of the Week!

Oracle

Oracle is the #1 provider of business software.  Today, 430,000 customers in 175 countries use Oracle technologies to re-imagine their businesses, processes, and experiences, and solve real, tangible challenges.  The innovative technologies of Oracle Cloud and Oracle Autonomous Database are revolutionizing how enterprises manage data.  High performance, standards compliant, feature-rich Property Graph and RDF Graph support in Oracle Database powers enterprise applications in finance, pharma, manufacturing, healthcare, and intelligence.
 
Semantic Web - PoolParty

PoolParty Semantic Suite is the most complete and secure semantic platform on the global market. Semantic Web Company is the vendor of PoolParty and a leading provider of graph-based metadata, search and analysis solutions. High performance, standards compliant, feature-rich Property Graph and RDF Graph support in Oracle Database powers enterprise applications in finance, pharma, manufacturing, healthcare, and intelligence.
 

Introducing: Speakers of the Week! 

We have an amazing lineup prepared for KGC 2021 and will be introducing new speakers every week! 

Amgad Madkour from Microsoft 
Chandrasekhar Iyer from Facebook 
Alex Kalinowski from Wells Fargo 
Luke Feeney from TerminusDB 
Abhishek Mittal 
from Wolters Kluwer
Martynas Jusevicius from AtomGraph
Roi Krakovski from Usearch  
Chaitan Baru
from National Science Foundation 
Zhamak Dehghani from ThoughtWorks 
Laura Ham 
from SeMI Technologies 
Paco Nathan from Derwen, Inc.
Talk Previews
Amgad Madkour from Microsoft

Entity Life Cycle In Search-Centric Knowledge Graphs 

Entity-based results are becoming an integral part of the search experience. Search-centric companies highly rely on knowledge graphs in providing the necessary information for building rich search experiences. An entity can originate from a structured, semi-structured, or unstructured data source. An entity passes through a series of stages to be onboarded into a knowledge graph and then served as part of a search result. This talk presents the life cycle of an entity including extraction, schema mapping, ingestion, entity resolution, data quality, and publishing. It covers the challenges and approaches employed at each stage. It also introduces some of the different experiences that can be generated for a given entity. Finally, it covers the multilingual aspect of knowledge graphs and the challenges of maintaining them at scale.
 
Chandrasekhar Iyer  from Facebook

Multi-Modal retrieval over Knowledge Graphs

Recent Advances in representation learning and application of Deep Neural Nets towards structured data and Knowledge Graphs (KG) is enabling opportunities for multi-modal representation of entities and relations. We can now aspire to build access to data encoded in knowledge Graphs through one of many modalities (image, audio, text or video) and also train joint representations to cross over from one modality to another (e.g. text-to-image, audio-to-text). These kinds of capabilities allow us to build applications that can use entity information in entirely new ways, to exploit the sensors available in modern multi-modal devices like glasses, watches, smart earphones etc... These contextually aware smart devices provide a better model for the users to interact with the world and consequently need a more robust support from a multi-model knowledge Graph to help them contextualize the users environment. In this presentation I will talk about a retrieval architecture to support a multi-modal KG. I will also show some examples of prototypes we have built to multi-modal retrieval.
 
Alex Kalinowski from Wells Fargo


Structured to Unstructured and Back: Integrated Knowledge Graphs and Natural Language Processing Techniques


It is a difficult task for traditional pattern-based matching or machine learning approaches to identify entities and the relationships they share. These techniques rapidly overfit training datasets and struggle to transfer to other contexts or domains. One solution to the lack of transferability includes the utilization of outside knowledge, such as facts contained in a knowledge base or ontology. However, integrating unstructured data such as language models with highly structured data such as knowledge bases is a challenging research problem. Using concepts from distant supervision, word vectors, and knowledge graph embeddings, an elegant unsupervised learning approach will be presented for solving this knowledge integration problem. This talk illustrates the problem from two points-of-view: the natural language processing practitioner unaccustomed to semantics and knowledge bases, and the semantic web developer without a background in deep learning and language models.
 
Luke Feeney from TerminiusDB

Git for Data? Why a Knowledge Graph is Best for Distributed Collaboration


There has been an explosion of tools - especially in the machine learning space - describing themselves as ‘git for data’. This talk will review the main open source players and link the interest to data mesh architectures. Not to jump to outcomes without first conducting the review, but it will conclude that knowledge graphs are the best way to approach distributed collaboration. 
 
Abhishek Mittal from Wolters Kluwer

Re-imagining Regulatory Obligation Management


As part of this presentation, attendees will learn about the following:
  • Content Enrichment: Development and deployment of a 5-stage taxonomy. Applying the taxonomy to tag regulations and classify them for improved discovery & work assignment.
     
  • Smart Authoring: Leveraging advanced NLP and ML techniques to learn from the past content authoring for identification of key requirements and faster & complete authoring.
     
  • Obligation Graph: Finally linking regulatory obligations to products, process, functional area, topics and raw citation text. This enables end-end audit trail and linkage of policies to regulatory requirements enabling financial institutions to meet their regulatory compliance
     
Martynas Jusevicius from AtomGraph

Data-Centric Transformation
 

One of the key pieces of global infrastructure today is the web yet it continues to be developed using legacy technologies dating back to the 1960s. A result of using outdated technology in turn has created several major problems. First, relational data models are a primary contributor to the data silos problems. Second, object-oriented codebases are proliferating complexity, trapping business logic, and stifling code reuse. Third, some experts warn we are heading for a software apocalypse. And finally we’re over paying for software projects by orders of magnitude. Where do we begin to resolve these problems? In this talk, we present a data-centric transformation. We explain how RDF Knowledge Graphs, Data-Driven software, and declarative technologies can be used to create a future-proof architecture with diminishing costs. And we will demonstrate AtomGraph’s Knowledge Graph management system, LinkedDataHub, which implements these principles.
 
Roi Krakovski from Usearch

The Usearch Contextual Graph 


In this talk, we will review how the recent breakthroughs in Neuroscience can be exploited to create Web search engines totally based on AI-generated data - eliminating the need to collect users’ data. In particular, we will focus on:
  • How can we generate search queries that are almost identical to a real user’s ones?
  • How can we exploit the generated queries to predict user intent in a contextual graph?
Chaitan Baru from National Science Foundation

Open Knowledge Network 
 

The concept of an Open Knowledge Network (OKN) is one of the components of the National Science Foundation’s Harnessing the Data Revolution (HDR) Big Idea, with the objective of providing semantic information infrastructure. By encoding information and knowledge about real-world entities and their relationships, the OKN would enable next generation artificial intelligence-based technologies and applications, focusing in particular on science and engineering information. While large-scale knowledge networks have been deployed in services like Google Search, Amazon catalogs, Apple Siri, Microsoft Cortana, and WolframAlpha, an open effort would expand this approach, enabling discovery of non-trivial information from multiple disparate knowledge sources for thousands of new topic areas in scientific and engineering information. With this objective in mind, the NSF Convergence Accelerator announced a “track” on the Open Knowledge Network (Track A) in 2019. Twenty-one projects were funded in Phase I in 2019 and five were funded in Phase II in 2020. Multidisciplinary, multi-sector teams in these projects are addressing a range of issues including, programming environments for knowledge network creation, making hidden/implied geospatial information explicit in knowledge graphs, and encoding information in specific domains, viz. urban flooding, biomedicine, and court records. This talk will provide an overview of the NSF Convergence Accelerator program and the Open Knowledge Network activities.
 
Zhamak Dehgani from ThoughtWorks

Introduction to Data Mesh: A paradigm shift in managing analytical data
 

For over half a century organizations have assumed that data is an asset to collect more of, and data must be centralized to be useful. These assumptions have led to centralized and monolithic architectures such as data warehousing and data lake, and neither of which have been able to enable data-driven innovations at scale. In this talk, Zhamak will unpack the challenges of existing paradigms of big data management. She introduces Data Mesh as an alternative approach to analytical data management. An approach that shifts the data culture, technology and architecture - from collecting data to connecting data - from data as an asset to data as a product - from proprietary big platforms to protocols, and - from top-down manual data governance to a federated computation one. She will explore the relationship between a Data Mesh implementation and emergence of a knowledge graph, and will leave you with a call to action to continue the exploration.
 
Laura Ham from SEMI Technologies

Introduction to Weavite Vector Search Engine 
 

This talk is an introduction to the vector search engine Weaviate. You will learn how storing data using vectors enables semantic search and automatic data classification. Topics like the underlying vector storage mechanism and how the pre-trained language vectorization model enables this are touched. In addition, this presentation consists of live demos to show the power of Weaviate and how you can get started with your own datasets. No prior technical knowledge is required; all concepts are illustrated with real use case examples and live demos.
 
Paco Nathan from Derwen, Inc.

Graph-Based Data Science: 'kglab' open source integration of graph libraries with popular data science tooling
 

Python offers excellent libraries for working with graphs: semantic technologies, graph queries, interactive visualizations, graph algorithms, probabilistic graph inference, as well as embedding and other integrations with deep learning. However, most of these approaches share little common ground, nor do many of them integrate effectively with popular data science tools (pandas, scikit-learn, spaCy, PyTorch), nor efficiently with popular data engineering infrastructure such as Spark, RAPIDS, Ray, Parquet, fsspect, etc. This talk reviews `kglab` https://github.com/DerwenAI/kglab – an open source project that integrates most all of the above, and moreover provides ways to leverage disparate techniques in ways that complement each other, to produce Hybrid AI solutions for industry use cases.Onboarding Cambridge Semantics Speakers
 
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