Showing posts with label semantic networks. Show all posts
Showing posts with label semantic networks. Show all posts

Understanding Knowledge Graphs - What They Are and How to Build Them

Knowledge graphs are a way to represent information and relationships between the real world entities, i.e. objects, events, facts etc., in a structured, interconnected way. They provide a powerful framework for capturing the rich semantics and context of data. Knowledge graphs are also called semantic networks.

At its core, a knowledge graph is a type of data model that structures information as entities (nodes) and their relationships (edges). Entities represent real-world objects like people, places, concepts, or events, while the relationships define how these entities are linked. An example of a small knowledge graph is shown below.

Some key characteristics of knowledge graphs are:

- Entities have properties that describe their attributes (e.g. name, date of birth for a person)

- Relationships have types that define how entities are connected (e.g. bornIn, employedBy)

- They provide context by capturing how pieces of data relate to one another

- Information is stored in a graph structure rather than tables

This graph-based approach provides flexibility in modeling complex, multi-dimensional data in a way that can be easily extended as new information emerges.

Applications of Knowledge Graphs

Knowledge graphs have broad applications across many domains:

- Search engines like Google use knowledge graphs to enhance search results with contextualized information
- Recommendation engines leverage knowledge graphs to provide relevant suggestions based on user preferences and behaviors  
- Biomedical research uses knowledge graphs to represent relationships between diseases, genes, drugs, and more
- Enterprise knowledge graphs help organizations better understand their data assets and how different systems/processes are interconnected

How to Build a Knowledge Graph

There are generally five key steps in creating a knowledge graph:

1. Data Ingestion - Gathering data from various structured and unstructured sources  
2. Entity Extraction - Identifying real-world entities mentioned in the data using techniques like named entity recognition (NER)
3. Relationship Extraction - Determining relationships between entities using methods like pattern matching or machine learning
4. Data Transformation - Converting the extracted triples (entity-relationship-entity) into the proper graph data model format
5. Graph Population - Loading the transformed data into the graph database

There are many different knowledge graph construction toolkits and frameworks available depending on your use case and data needs. Some popular open source options include:

- Stanford's CoreNLP for NLP processing 
- OpenKBC for end-to-end knowledge graph creation
- Python libraries like rdflib and owlready2 for working with semantic web standards

Knowledge graphs unlock new opportunities for data integration, analysis, and discovery. As our ability to capture, process, and connect information continues growing, knowledge graphs will become an increasingly vital tool.