Knowledge technologies increase the power, accuracy, and impact of ML and data science, by providing essential machine and human-readable context, enabling greater data flexibility and richness, and lowering complexity and cost.
We are pleased to announce that the Knowledge Graph Conference has returned for the third year in a row! You won't want to miss the exciting talks we have prepared for you all!
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Join us on May 3 - May 6 at KGC 2021 to learn more about knowledge graphs, graph databases, graph AI, and semantic tech!
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Early Bird Tickets On Sale Now!
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Want to win a FREE PASS to KGC 2021?!
Fill out this survey and we just might pick you as a winner! We are partnering w/ Enterprise Knowledge Graph Foundation to conduct a comprehensive global survey to increase awareness about the knowledge graph industry.
This survey takes about 6-8 minutes to complete. Survey results will be released at the 3rd annual Knowledge Graph Conference (May 3-6).
Survey participants receive a copy of the final results and will be automatically entered into a raffle for complimentary passes to KGC 2021.
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Introducing: Speakers of the Week!
We have an amazing lineup prepared for KGC 2021 and will be introducing new speakers every week!
Jay Yu from Intuit
Chris Welty from Google
Maulik R. Kamdar from Elsevier Inc.
Cedric Berger from Novartis International
Veronika Heimsbakk from Capgemini
Andriy Nikolov from AstraZeneca
Paolo Manghi from OpenAIRE, ISTI-CNR
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Jay Yu from Intuit
Elevating Data to Knowledge: Practical Approach to Weave Knowledge Graph Technology in Enterprise Data Architecture
Intuit is embarking on a multi-year journey to transform from a financial product-centric company into an "AI-driven Expert Platform" company. We have aligned our enterprise data architecture and strategy to the company growth strategy, with a clean end-to-end enterprise architecture that embraces data-centric principles with an ambitious goal to elevating data to knowledge. In this talk, I will describe the burning platform of the data challenge, the e2e clean data architecture solution to overcome the challenge, and our incremental approach to apply knowledge graph technology in many aspects of the architecture. I will also share progress made and key lessons learned from the journey.
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Chris Welty from Google
Shopping Sense: Bringing common sense to worldwide shopping knowledge
Knowledge Graphs (KGs) continue to penetrate the industrial world after Google's famous "things not strings" was used to explain their acquisition of FreeBase ten years ago. While many KGs exist, they are by and large little more than "entity catalogs", missing entirely the links between those entities. At Google, we recently launched a new enhancement to search that allows product queries, such as "Milk", "Celery" or "12 string guitar", to return local results - places on maps, nearby, that sell the product. The challenge to making this work is that 70% of stores worldwide - and 40% in the US - do not have a web page, Google's primary data source for search. To overcome this challenge and extend our understanding of small to medium-sized brick&mortar shops, we used a unique combination of Knowledge Graphs, AI and Human Common Sense, that demonstrates both the promises and limitations of AI in solving practical business problems.
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Maulik R. Kamdar from Elsevier
Elsevier’s Healthcare Knowledge Graph: An Actionable Medical Knowledge Platform to Power Diverse Applications
Knowledge Graphs are increasingly being developed and leveraged in academia and industry to tackle complex biomedical challenges, such as drug discovery and safety, medical literature search, clinical decision support, and disease monitoring and management. In this talk, we will present the research and development on Elsevier’s Healthcare Knowledge Graph, a platform built to deliver advanced clinical decision support and enhanced point-of-care content discovery for clinicians and patients. Elsevier’s Healthcare Knowledge Graph uses popular linked data and semantic web technologies to capture knowledge and data from heterogeneous healthcare sources about diseases, drugs, findings, guidelines, cohorts, journals, and books. This talk will provide a perspective on how such knowledge graphs will enable the capture, representation, and provision of complex medical knowledge of high velocity, variety, volume, and veracity, to power, trusted clinical and biomedical research applications.
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Cedric Berger from Novartis International
Data Governance 4.0 applied to a Unified Clinical Data Model
Driven by legacy paper-based approaches, the design, conduction and analysis of clinical studies requires the creation and transformation of many data in many different formats. This hinders the process and necessitates significant resources. Having metadata-driven transformation is not new, however, adoption of standards (.e.g CDISC) has not shown yet its full added value. We propose to extend this metadata-driven approach beyond CDISC standards from the writing of the unstructured clinical study protocol to submission of the clinical study report to health authorities. By sharing and reusing metadata end-to-end of the drug development critical path, we shorten and automate key tasks hence improving the efficiency of the overall process.
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Veronika Heimsbakk from Capgemini
Tales from the road of text to knowledge
When transforming amounts of plain text into semantic knowledge graphs using Resource Description Framework, a service for automatic interpretation became apparent. Manual interpretation of text depends on human domain knowledge and discovery of entities and relationships in the text. This process is a highly time-consuming activity with a risk of misinterpretation. Based on the hypothesis that using Natural Language Processing techniques we can extract information and meaning from text faster than a human being, we successfully implemented a service for automatic metadata and ontology generation for a client in Norwegian public sector. This presentation will walk you through our hypothesis, the steps from text to RDF through Natural Language Processing, and our results.
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Andriy Nikolov from AstraZeneca
Biological Insights Knowledge Graph to help drug development
The use of knowledge graphs as a data source for machine learning methods to solve complex problems in life sciences has rapidly become popular in recent years. Our Biological Insights Knowledge Graph (BIKG) combines relevant data for drug development from more than 50 public as well as internal data sources to provide insights for a range of tasks: from identifying new targets to repurposing existing drugs. In our case study presentation we are going to discuss our solutions to challenges of building an integrated knowledge graph from diverse sources as well as present two illustrative use case studies exploiting graph data to produce recommendations for domain experts: identifying candidate genes responsible for drug resistance in CRISPR screens and finding promising drugs for repurposing.
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Paolo Manghi from OpenAIRE, ISTI-CNR
The OpenAIRE Research Graph: Science as a public good
Conceived as a public and transparent good, populated out of data sources trusted by scientists, the OpenAIRE Research Graph aims at bringing discovery, monitoring, and assessment of science back in the hands of the scientific community. The underlying vision is to provide a trusted view of Open Science that is contextualized, traversable, open, and free-of-charge. The OpenAIRE Research Graph will be a key European Open Science Cloud (EOSC) resource, by providing the EOSC with: (i) a catalog of all scientific products, core in fostering Open Science and establishing its practices in the daily research activities, and (ii) Open Science monitoring tools, to measure trends and impact of Open Science and funding across communities and Nations.
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Introducing: Sponsors of the Week!
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Accenture
Let's give a warm welcome to Katana Graph for sponsoring KGC 2021! Katana Graph is driving graph database technology into an agile, adaptable future by harnessing massive data sets and pushing boundaries in a whole new class of applications and industries!
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Katana Graph
Katana Graph's revolutionary technology helps enterprise clients intelligently and confidently get out ahead of risk and change in their industry. Building from a next-generation data engine, our elite team is creating tools to unlock limitless potential for massive data sets to drive growth and innovation in a whole new set of applications and industries.
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JOIN THE COMMUNITY
Let us know what you're working on in Slack.
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