How do knowledge graphs handle unstructured data?

Knowledge graphs handle unstructured data by converting it into structured, interconnected information through a combination of extraction, normalization, and linking processes. Unstructured data, such as text documents, images, or videos, lacks predefined formatting, making it challenging to integrate directly into a graph. To address this, knowledge graphs rely on techniques like natural language processing (NLP) and computer vision to identify entities, relationships, and attributes within the data. For example, an NLP pipeline might parse a news article to extract mentions of people, organizations, and locations, then infer connections like “employed_by” or “located_in.” These extracted elements are then mapped to nodes and edges in the graph, often using standardized identifiers or schema.org types to ensure consistency.

A key step in this process is entity resolution, which ensures that extracted entities correspond to existing nodes in the graph or create new ones when necessary. For instance, if a document mentions “Apple” in the context of a tech company, the system might link it to a pre-existing node for “Apple Inc.” rather than creating a duplicate. Tools like spaCy or Stanford NER are often used for entity recognition, while frameworks like Apache OpenNLP help parse relationships. Additionally, unstructured data like images might be processed using object detection models (e.g., YOLO or ResNet) to identify visual entities (e.g., “car,” “tree”) and their spatial relationships, which are then added to the graph as metadata or linked nodes.

Challenges arise in handling ambiguity and context. For example, the word “Java” could refer to a programming language, an island, or coffee. Knowledge graphs mitigate this by leveraging contextual clues from surrounding text or metadata. They may also use external knowledge bases (e.g., Wikidata) to validate and disambiguate entities. Scalability is another concern, as processing large volumes of unstructured data requires efficient pipelines. Developers often address this by using distributed systems like Apache Spark or pre-trained transformer models (e.g., BERT) for faster analysis. By systematically transforming unstructured data into structured triples (subject-predicate-object), knowledge graphs enable querying, inference, and integration with other datasets, turning raw information into actionable insights.