Resources - videos

Videos, Slides, Case Studies and other GraphAware related resources

NODES2022 - Temporal Graph Analysis

25 Nov 2022 videos KG ML

Fabio Montagna is Lead Machine Learning Engineer at GraphAware and presented Temporal Graph Analysis at NODES2022. In this session, we’ll share our experience with horizon scanning over a graph of medical research papers. By leveraging the author keywords from scientific publications, it’s possible to build a cooccurrence graph with a temporal component provided by the paper publication date. We’ll show how we can analyze trends and evolution patterns using an unsupervised algorithm that assigns roles to author keyword.

NODES2022 - Neo4j With Docker and Docker Compose Deep Dive

25 Nov 2022 videos Neo4j

Christophe Willemsen, CTO at GraphAware, spoke on NODES2022 about using Neo4j with Docker and Docker Compose, presented tips and tricks on basic usage, gave an explanation of the Docker image itself, backups and restore and building custom images extending the official Neo4j image.

NODES2022 - Keyword Disambiguation Using Transformers and Clustering to Build Cleaner Knowledge

25 Nov 2022 videos KG NLP

Federica Ventruto and Alessia Melania Lonoce are Junior Data Scientists at GraphAware who spoke at NODES2022. Natural language processing is an indispensable toolkit to build knowledge graphs from unstructured data. However, it comes with a price. Keywords and entities in unstructured texts are ambiguous - the same concept can be expressed by many different linguistic variations. The resulting knowledge graph would thus be polluted with many nodes representing the same entity without any order. In this session, we show how the semantic similarity based on transformer embeddings and agglomerative clustering can help in the domain of academic disciplines and research fields and how Neo4j improves the browsing experience of this knowledge graph.

NODES2022 - Data Management with Knowledge Graphs Bringing Archives to Life

25 Nov 2022 videos KG NLP

Vlasta Kůs is Lead Data Scientist at GraphAware and presented at NODES2022. Public archives contain incredible amount of knowledge. In this session, we’ll cover a real use case of building a knowledge graph for the archive of a major foundation to help empower researchers (or business analysts) to access previously unavailable levels of insights. This archive, going up to a century back, contains detailed information about funded projects and conversations preceding them, budgets, research endeavors, and outcomes, as well as priceless knowledge about influence networks of foundation representatives, researchers, and students. A particular challenge was that the same events were described in multiple sources. The only way to leverage all of this knowledge was through the use of advanced analytics and machine learning. We will explore the technologies (including OCR, NLP, and graph data science) and complex pipelines employed to create this major knowledge graph.

NODES2022 - Building a Neo4j/Python OGM

25 Nov 2022 videos CYPHER

Estelle Scifo is a Machine Learning Engineer at GraphAware and presented at NODES2022. Leverage Cypher map projections and Python dynamic typing to build an Object Graph Mapper for Neo4j. In this step-by-step session, you’ll learn how to get started on such a project, from defining the framework API to automatically building Cypher queries.

Tracking Data Sources of Fused Entities in Law Enforcement Graphs

18 Jun 2022 videos Hume law enforcement

Graphs are commonplace in investigative, intelligence, and law enforcement work. One of the primary advantages of a graph is to connect data from various data sources, digital and human, and maximize insights across deep and complex networks of connections, bringing them together in fusion centers for a centralized view of suspicious activities. For analysts, data quality and trust is key. The reliability, validity, and general consistency of data sources that contribute to forming real world fused entities is a factor that influences the analysts’ interpretation of events. This session talks about the challenges related to surfacing these aspects of data provenance and various approaches that can be employed to address them using Neo4j. We will touch on graph modeling, implications for data security, and how sources and information ratings can be effectively shared with analysts who need access to them.