Connecting Meaning: The promise and challenges of Knowledge Graphs as providers of large-scale data semantics

Ever since Google announced that “their knowledge graph allowed searching for things, not strings”, the term “knowledge graph” has been widely adopted, to denote any graph-like network of interrelated typed entities and concepts that can be used to integrate, share and exploit data and knowledge.

This idea of interconnected data under common semantics is actually much older and the term is a rebranding of several other concepts and research areas (semantic networks, knowledge bases, ontologies, semantic web, linked data etc). Google popularized this idea and made it more visible to the public and the industry, the result being several prominent companies, developing and using their own knowledge graphs for data integration, data analytics, semantic search, question answering and other cognitive applications.

As the use of knowledge graphs continues to expand across various domains, the need for ensuring the accuracy, reliability, and consensus of semantic information becomes paramount. The intricacies involved in constructing and utilizing knowledge graphs present a spectrum of challenges, from data quality assurance to ensuring scalability and adaptability to evolving contexts.

In this talk, we will delve deeper into the significance of knowledge graphs as facilitators of large-scale data semantics. The discussion will encompass the core concepts, challenges, and strategic considerations that architects and decision-makers encounter while initiating and implementing knowledge graph projects.

The session will cover:

  • Understanding Knowledge Graphs: Exploring the fundamental concepts and significance of knowledge graphs in integrating, organizing, and harnessing data across diverse domains
  • Challenges in Building Knowledge Graphs: Identifying and dissecting primary hurdles such as data quality assurance, schema alignment, scalability, and ongoing maintenance
  • Strategic Dilemmas: Examining critical decision points and dilemmas faced by architects and executives when designing and executing knowledge graph initiatives
  • Crafting an Effective Strategy: Outlining guidelines to formulate a robust knowledge graph strategy tailored to specific organizational goals, considering scalability, interoperability, and domain relevance.