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...

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...

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 ...

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...

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 ...

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 ...

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...

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...

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...

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

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...

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...

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...

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...

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...

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...

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, ...

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,...

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...

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 ...

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...

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...

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...

Can one build a knowledge graph (KG) for all products in the world? Knowledge graphs have firmly established themselves as valuable sources of information for search and question answering, and it is natural to wonder if a KG can contain information about products
offered at online retail sites....