What is a knowledge graph?

[fs-toc-omit]A bio-inspired neural graph is a data structure together with associated algorithms designed to mimic the efficiency and interconnectedness of the human brain, using associative connections and neurons to manage and interpret data. This approach optimizes performance and enables efficient handling of complex datasets by embedding algorithms within the graph structure and utilizing weighted and specialized associations to represent relationships.

How does a knowledge graph work?

The process of building a knowledge graph begins with collecting data from various sources: databases, documents, web pages, or sensors. This data is then integrated to form a unified representation. Entities like people, places, organizations, and events are identified and then extracted from the raw data directly or using natural language processing (NLP) techniques, machine learning models, and manual tagging. Relationships between entities are identified and represented as edges in the graph, connecting the relevant nodes (entities).

The constructed knowledge graph is stored in a graph database, which is designed to handle graph structures efficiently and ensure scalability and performance.

What challenges do companies face using knowledge graphs in data analytics?  

While knowledge graphs are highly useful, they come with several challenges. The main problem is designing the structure of the knowledge graph, naming objects and relationships, and assigning them semantics.

Knowledge graphs can become massive, making it challenging to manage, store, and query extensive datasets efficiently. The absence of standardized practices and tools can result in interoperability issues between different knowledge graph implementations.

Knowledge graphs primarily serve as a knowledge base with better query capabilities than traditional relational databases. Performing analytics on data and the graph structure requires the use of other tools.

Bio-inspired neural graphs: more powerful graph analytics

To address these challenges, the GiQ team has developed a more powerful solution: bio-inspired neural graphs. This innovative approach leverages principles from biological systems to create more dynamic, adaptive, and interconnected data structures, providing several advantages over traditional knowledge graphs.

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Dynamic and adaptive datastructures

Traditional knowledge graphs require significant effort to adjust datasets to models, often involving extensive preprocessing and schema adjustments. In contrast, bio-inspired neural graphs reduce the effort needed to adjust datasets, as the algorithms run in place without additional effort. They dynamically adapt to changes in data, making them more flexible and robust.

Easy exploration of complex relationships

Conventional knowledge graphs may not effectively reveal non-obvious relationships and insights within the data. In contrast, bio-inspired neural graphs utilize their associative nature and powerful algorithms for frequent pattern and association rules mining, ranking lists, entity mapping, grouping, and clustering. This allows for the exploration of hidden relationships and insights within the data.

Enhanced computing efficiency

The old-way knowledge graphs often struggle with memory usage and processing speed when handling large datasets. Conversely, bio-inspired neural graphs manage data more effectively, maintaining a logarithmic memory consumption curve as the graph grows denser. This algorithm-as-structure approach enhances efficiency, even with extensive datasets, minimizing the cost of basic operations.