Ambiguity, language discrepancies, and lack of background information are just a few challenges that organizations face on a daily basis when trying to analyze their content and data. When an organization produces data that is hard to manage, what methodologies can be used to turn unstructured (i...

When knowledge graphs in your company get larger and larger, a scalable graph search is in high demand. In the current graph search solutions, scalability is still a big issue. Furthermore, with the fast development of deep learning on graphs, many companies rely on deep learning methods to mine ...

Semantic systems provide tremendous opportunities to interoperate our data, facilitate shared vocabularies, and power enterprise knowledge graphs, but these increasingly distributed data ecosystems also introduce new instabilities and concerns. What happens when data we rely on is changing in une...

Despite the estimated 70 zettabytes of data produced worldwide, the production and use of data is unequal and its conversion to knowledge uneven across communities and sectors. To address this data & knowledge inequality, both the public and philanthropic sectors are increasing investments in dat...

In this panel discussion leaders from Raytheon, Bosch, Merck KGaA, and Deloitte will share their vision for knowledge graph technology and describe how they are translating that vision into reality within their own Digital Twin, Regulatory Data Fabric, and Operational Resilience initiatives. Wit...

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...

We’ll explore how unstructured and structured data can come together to tell a complete story through the lens of a pharma clinical trial and subsequent events in the field. We’ll weave through the patient history and other documents using an ontology and several NLP tools, then use natural langu...

In recently naming graph technology one of their top 10 trends data and analytic trends for 2021, the Gartner Group highlighted an emerging pattern: today an ever increasing number of F1000 organizations are implementing graph technology not to address point graph use cases, but rather data integ...

Ontotext teams up with portfolio partners to offer a complete platform with best-of-bread tools for: schema and taxonomy editing; data transformation and linking; access control and federation; data updates and validation; search and visualization. The ecosystem also includes global and regional ...

Description: The switch to decentralization in data management is finally happening as organizations struggle with a disconnect between data teams and domain knowledge. Once a new organizational structure around data domains is in place, the need for platforms for managing data products appears. ...

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,...

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...