Knowledge Graphs have become increasingly crucial for enterprises' knowledge representation and data consolidation. They provide the basis to solve complex data problems, provide insights into data, and thereby support intelligent decision-making in the digital workplace.
In this talk, we present an approach to combining Semantic Web technologies and Neo4j to build a knowledge-based recommender powered by a semantic knowledge graph and processing the recommendations using a property graph representation.
We present the use case of analyzing ESG-related documents and providing intelligent insights into ESG standards and documents to support the writing of ESG reports.
Guest: Astrid Krickl
Data & Knowledge Engineer, Semantic Web Company
Astrid is working at Semantic Web Company since very recently, November 2022 as a Data and Knowledge Engineer. She studied business informatics at UAS Technikum Vienna and worked already as software developer and tester, and later as system engineer at WU Vienna. She also has research experience as a research and teaching associate at WU Vienna. There, she investigated tools and techniques (mainly NLP and machine learning) that can help with the problem of misinformation and fake news.
PoolParty: https://www.poolparty.biz/
neosemantics: https://bit.ly/3XLJ0Cc
Going Meta: https://development.neo4j.dev/video/going-meta-a-series-on-graphs-semantics-and-knowledge/
NODES 2023 - Data Population, Analysis, and Visualization Over the SustainGraph: https://youtu.be/BUSTa6hclY4
#neo4j #graphdatabase #recommendations #esg #semantic #knowledgegraph #esg