Contemplating upgrading your product portfolio with a knowledge graph? Read on to hear how Stardog customers have delivered delightful user experiences and new features to their customer bases, thanks to Stardog.
In 2016, Springer Nature, one of the world’s largest academic publishers, diversified their product offerings to include subscription based datasets and databases. Their Database and Solutions offering provides consistent, complete, and on-demand data from a combination of diverse data sources centered around a particular topic.
The Database Group manages the tools and infrastructure that serve as the backbone to the platform. To bring together the variety of discrete data sources that are incorporated into these subscription based datasets, the DG team originally relied on robust XML document store and keyword/element builds. However, the legacy infrastructure required time-intensive updates and was outgrown by the demands of users and internal needs. As the team brought on more data sources to integrate into the platform, developed novel features for search and interaction, and updated existing data with new research, the connections within the infrastructure strained under the pressure of the ever-evolving data slowing down their release cadence.
The Stardog difference: Stardog’s semantic graph model is inherently more flexible than relational, XML, or even property graph models. Learn more about the benefits of the semantic graph model and how they support data reusability, dramatically reducing the time needed to execute changes or iterate on existing models.
To meet end-user and internal requirements, the Database Group needed a flexible and dynamic data infrastructure that allowed easy iterations and updates within existing data while also providing context embedded within search results to end-users. By unifying their data in Stardog, the Database Group created a single declarative model to bring all the connections between the content into a unified view.
“Deploying on Stardog fosters the idea of reusability among the mapping and vocabularies within the database products. Stardog makes large-scale graph data management possible for Springer Nature — and propels our data-modeling and data-integration efforts to a completely new level.”
- Marcel Karnstedt-Hulpus, Director Data & Knowledge Technologies DRG
Products including SpringerMaterials, Springer Nature Experiments, and Nano now all run on Stardog. The native flexibility of a knowledge graph allows Springer Nature to make continuous additions and adjustments to the data as more discoveries are made, their proprietary datasets grow, and new data sources emerge.
Dow Jones was sitting on a trove of proprietary data, stored in varying structures. Between their flagship publications (The Wall Street Journal), news archive aggregators (Factiva) and specialized news datasets (DNA), Dow Jones has access to millions of facts derived from fifty years of digitized news media. Factiva alone offers content from over 33,000 global news and information sources from over 200 countries and in 28 languages.
Dow Jones realized they could better serve their customers by helping them understand what actions to take based on news events. For example:
- An energy company wants to know what regulations might affect their customers so that they may help provide solutions.
- An advertising sales team wants to know whether any of their customers are planning product launches that might necessitate a media buy.
- Bankruptcy professional wants to know when a company is in distress and who the relevant parties may be to offer services.
In order to automate reading the news to answer these questions, Dow Jones had to address some challenges, including:
- The knowledge within Down Jones was dispersed among multiple products and data sets. (Both structured and unstructured data.)
- Customers increasingly expect personalized experiences and context-relevant news and data.
- There was a growing need to make sure that both news and data could be consumed by humans as well as machines.
Further, they needed a solution that was scalable, updates quickly, and operates well at high volumes — after all, the news changes minute to minute. Dow Jones ultimately turned to Stardog, allowing them to create a complete view of the news, comprehensible by humans and readable by machines.
Using Natural Language Processing (NLP), Dow Jones’ knowledge graph extracts entities (people, companies, events, dates) and their relationships (employed by, invested in, associated with) from the unstructured news articles. Stardog links these entities to the related data in the knowledge graph, placing these entities in the context of other news stories and historic relationships. The graph shows all the relationships to any given entity and also uncovers indirect links between entities. This allows Dow Jones’ customers to see exactly what news events impact their interests, even if an investment isn’t explicitly named within the article.
Image via Dow Jones presentation at Connected Data London 2019
The knowledge graph also allows a level of personalization unrivaled by other news aggregators. Dow Jones accomplishes this by linking their customer’s CRM (Customer Relationship Management system) and extracts and matches customer records to the entities in the knowledge graph. With a watchlist in place, as news events occur the relevant facts are delivered to the customer directly to their data warehouse via an API. Further, these signals can be fed into automated systems, such as sales lead scoring, to make their customers’ processes more intelligent.
The Stardog difference: Stardog offers many Connectors to popular enterprise data sources, including CRMs, to make it easy to collaborate with third parties. This saves valuable engineering time, while increasing capacity for personalized experiences. Learn more about how Stardog supports interoperability across your varied data sources here.
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