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Making smarter connections with knowledge graphs

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Graph database technology is among the fastest-growing sectors in the IT industry, with an expected increase of more than 20% per year over the next 5 years, according to MarketsAndMarkets. Just under $2 billion in 2021, this market is expected to exceed $5 billion by 2026. The primary factor that will drive this growth is the ability of graph databases to reveal relationships, and their ability to perform near-real-time queries that leverage these relationships. They also can ingest many data types from multiple repositories and use existing taxonomies and ontologies. These capabilities allow them to use existing resources rather than requiring replacement of legacy systems.

Early developers of graph databases included tech giants such as Googe, Meta (formerly Facebook), and LinkedIn, which used them for detecting relationships among their users; Amazon and Netflix also used them to power their recommendation engines. Now, graph database technology has become accessible to companies of all sizes for these applications and many others. In addition, significant strides have been made in simplifying the development and use of graph databases.

Graph-powered insights

The mission of the European Space Agency (ESA) is to explore the use of space for peaceful purposes throughout the world. Its areas of research include Earth observation, telecommunications, and satellite navigation, among many others. In order to monitor and analyze the ever-increasing volume of information related to space exploration, ESA wanted to develop a knowledge graph based on a large collection of both structured and unstructured data. However, without an automated method of ingesting and mapping data into the knowledge graph, this effort was not feasible.

ESA contacted GraphAware to investigate the potential of Hume, a graph-powered insights engine built on Neo4j that uses natural language processing in its analyses. ESA wanted to devise a proof of concept to determine whether Hume could effectively support its mission. Hume was configured to automate ingestion of unstructured data from documents and webpages, and Hume Orchestra was used to manage the workflow.

To process the content, Hume was configured for entity recognition, entity relationship extraction, and post-processing. The system then used Hume’s Labs, a supervised annotation system, to train machine learning algorithms based on the expertise of domain experts. ESA found that the Hume knowledge graph visualization could reveal custom insights through Hume Actions. Now ESA plans to build on its initial demonstration and bring the system to more users throughout the organization.

“Graph technology is an enabler, and knowledge graphs are a semantic representation for your data,” said Alessandro Negro, chief scientist at GraphAware. “In a knowledge graph you can get to the data, whether structured or unstructured, from many different access points.” He noted that taxonomies and ontologies are essential for effective development and use of knowledge graphs. “The semantics of taxonomies and ontologies allows order to be created out of huge amounts of data.”

The latest release of GraphAware Hume includes alerts that enable users to monitor their graphs and receive notifications of significant changes for both nodes and relationships. Hume Orchestra provides the workflow engine to move and convert data in the graph model and the subsequent analysis. Hume Actions provides improved exploration of data by allowing data from one or more Neo4j resources to be visualized effectively even by non-experts.

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