KGC | All Access Subscription

KGC | All Access Subscription

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KGC | All Access Subscription
  • Mike Tung | Automated Knowledge Graphs For Market Intelligence

    Nearly every business is constantly trying to identify, analyze, and grow their market. Yet, traditional processes for data management inevitably lead to a database that contains missing, outdated, invalid, or inconsistent data. Automated knowledge graph construction techniques are a scalable w...

  • Laura Ham | Introduction To Weaviate 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 tou...

  • Stefan Plantikow | The Upcoming GQL Standard

    Following the GQL Manifesto, the ISO working group that develops the SQL standard voted to initiate a project for a new database language: GQL (Graph Query Language). This talk presents an overview of the goals of GQL and the progress so far, key aspects of the language design such as the basic d...

  • Luke Feeney | 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 ...

  • Michael Cafarella | Infrastructure For Knowledge Graph Application Programming

    Social Knowledge Graphs such as Wikidata have become massive successes, obtaining a level of coverage and quality that would be the envy of many traditional relational database engineering projects. And yet the downstream use scenarios for such datasets remain sharply limited compared to the vast...

  • Roi Krakovski | The Usearch Contextual Graph

    We exploit the recent breakthroughs in Neuroscience to build web search engines based entirely on AI-generated data, thus eliminating the need to collect users’ data. We show how to generate search queries that are almost identical to real users’ queries. We use the generated queries to build a ...

  • Krzysztof Janowicz | Know, Know Where, KnowWhereGraph

    The KnowWhereGraph project aims at providing a densely interlinked knowledge graph for environmental intelligence applications and situational awareness services (area briefings) that enrich the data of decision-makers and data scientists with pre-integrated data custom-tailored to their spatial ...

  • Neda Abolhassani & Teresa Tung | Accelerating Industry Data Integration

    A data supply chain is industry-specific, but many data prep tools are industry agnostic. As part doing this work, data engineers and domain experts apply their deep knowledge of how to transform raw data to a form that can address specific problems. In this way, the data supply chain is a doma...

  • Julian Grummer | What Can We Learn From Knowledge Graphs: A Wirecard Perspective

    The Wirecard scandal was one of the most shocking economic events in Germany in 2020. The former DAX30 company collapsed on June 25, owing creditors more than €3.5 billion (almost $4 billion) after disclosing a gaping hole in its books that its auditor EY said was the result of a sophisticated gl...

  • Mohammed Aaser | Future Of Enterprise Data Management

    Many organizations have initiated data and analytics transformations with some success, however are beginning to face challenges in scaling efforts beyond a handful of applications/use cases. One of the major barriers remains around data management, including challenges with data transparency, i...

  • Peter Hicks | Visualizing Data Lineage

    Extracting metadata from data pipelines and building useful graphs for real production environments.

  • Zhamak Dehghani | Introduction To Data Mesh

    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...

  • Mark Grover | From Discovering To Trusting Data

    Over a third of analyst time is spent in understanding what data exists, can it be trusted and how to use it. Countless Data Engineering time is spent in answering the same questions about data - what does that column mean, how does it get populated, how often does it update and if there’s any in...

  • Steven Gustafson | Connecting The Dots With Knowledge & Data

    Knowledge graphs provide a way for us to capture and relate information into a representation that can mimic expert knowledge. One type of expert knowledge that has proven to be particularly useful in industry is reasoning by analogy, or using a replacement problem and solution pair to think abo...

  • Martynas Jusevicius | 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 dat...

  • Alex Kalinowski | Structured To Unstructured & Back: Integrated KG and NLP

    Identification of entities and the relations between them is a difficult task for traditional pattern-based matching or machine learning approaches; these techniques rapidly overfit training datasets and struggle to transfer to other contexts or domains. Utilizing outside knowledge, such as facts...

  • Cedric Berger | 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, howe...

  • Chen Zhang & Dmytro Dolgopolov | Entity Disambiguation With Knowledge Graph

    During the presentation, we will share our experience in building a knowledge graph leveraging Spark, NLP, and Machine Learning. We will start with explaining the business problems and challenges. Then walk through our data pipeline, including text analytics processes, name similarity solutions, ...

  • Keshav Pingali | High Performance Knowledge Graph Computing On Katana Graph

    Knowledge Graphs now power many applications across diverse industries such as FinTech, Pharma and Manufacturing. Data volumes are growing at a staggering rate, and graphs with hundreds of billions edges are not uncommon. Computations on such data sets include querying, analytics, pattern mining,...

  • Melliyal Annamalai | Developing Enterprise Applications With Oracle Graph

    Application developers often need to work with a variety of data types, data models, and workloads within an application. Oracle Database is a multi-model, multi-workload data platform with model-specific tools and technologies, enabling developers to build integrated applications while taking a...

  • Ridho Reinanda | Financial Knowledge Graph At Bloomberg

    The Bloomberg Knowledge Graph is a graph-centric representation of entities and relationships in the financial world which connects cross-domain data from various sources within Bloomberg. Recent developments in machine learning, knowledge graphs, and language technology have enabled intelligent ...

  • Jay Yu | Weave Knowledge Graph Tech 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 embrac...

  • Shekhar Iyer | 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...

  • Peter Rose | Integrating Heterogeneous Data Sources Into A COVID-19 Graph

    The COVID-19 pandemic has mobilized researchers worldwide to investigate many aspects of the outbreak, ranging from case statistics, patient demographics, transportation modeling, epidemiological studies, to viral genome sequencing. Relevant data are produced and publically shared at an unprecede...