Knowledge graphs handle unstructured data by converting it into a structured format that can be easily processed and analyzed. Unstructured data, such as text documents, social media posts, or images, does not fit neatly into traditional data tables. To utilize this data in a knowledge graph, techniques like natural language processing (NLP) are employed to extract relevant entities, relationships, and attributes. For instance, an NLP algorithm can identify people, organizations, and locations mentioned in a news article, transforming these components into nodes and edges in a graph.
Once the relevant information is extracted, it is mapped to a predefined schema or ontology. A schema provides a common framework that defines how different entities relate to one another. An example would be a travel knowledge graph that includes entities like "Hotels," "Cities," and "Attractions," where relationships denote, for example, "located in" or "offers." By using a schema, knowledge graphs enable consistent representation and querying of unstructured data, which makes it easier for applications to retrieve and process this information.
After structuring the data, developers can perform richer queries and analyses on the knowledge graph. For instance, they could ask questions like, "What are the top-rated hotels in a specific city that are near certain attractions?" The structured nature of the graph allows complex queries to be executed efficiently, enabling applications such as personalized recommendations or trend analysis. As unstructured data continues to grow, knowledge graphs provide a powerful way to convert this data into actionable insights.