Readtograph

Readtograph is a term used to describe the process or set of tools that converts natural language text into graph-structured data. In practice, readtograph pipelines aim to transform unstructured information into nodes and edges that form a knowledge graph or property graph. The goal is to enable structured querying, reasoning, and integration of information from text sources.

A typical readtograph pipeline includes named entity recognition to identify people, places, and concepts; coreference resolution to link mentions that refer to the same entity; and relation extraction to identify how entities are connected. In some approaches, open information extraction is used to generate general subject–predicate–object triples without a fixed schema. The extracted elements are then aligned to an ontology or schema and loaded into a graph store.

Graphs can be stored as RDF graphs or property graphs, and may be serialized in formats such as RDF, JSON-LD, or graph query languages like SPARQL or Cypher. Many implementations use graph databases such as Neo4j, JanusGraph, or cloud-native graph services to support querying and reasoning.

Readtograph supports semantic search, question answering, knowledge integration across data silos, and recommendation systems. It also enables provenance tracking and graph-based reasoning over textual content.

Challenges include language ambiguity, coreference across documents, noisy text, multilingual data, and maintaining up-to-date graphs as new information arrives. Evaluation is difficult because truth depends on context and the chosen granularity of the graph.