Knowledge Representation

Abstract

To improve the automated processability of web sites, formal knowledge representation standards are required that can be used not only to annotate markup elements for simple machine-readable data but also to express complex statements and relationships in a machine-processable manner. After understanding the structure of these statements and their serialization in the Resource Description Framework (RDF), the structured data can be efficiently modeled as well as annotated in the markup, or written in separate, machine-readable metadata files. The formal definitions used for modeling and representing data make efficient data analysis and reuse possible. The three most common machine-readable annotations that are recognized and processed by search engines are RDFa (RDF in attributes), HTML5 Microdata, and JSON-LD, of which HTML5 Microdata is the recommended format. The machine-readable annotations extend the core (X)HTML markup with additional elements and attributes through external vocabularies that contain the terminology and properties of a knowledge representation domain, as well as the relationship between the properties in a machine-readable form. Ontologies can be used for searching, querying, indexing, and managing agent or service metadata and improving application and database interoperability. Ontologies are especially useful for knowledge-intensive applications, in which text extraction, decision support, or resource planning are common tasks, as well as in knowledge repositories used for knowledge acquisition. The schemas defining the most common concepts of a field of interest, the relationships between them, and related individuals are collected by semantic knowledge bases. These schemas are the de facto standards used by machine-readable annotations serialized in RDFa, HTML5 Microdata, or JSON-LD, as well as in RDF files of Linked Open Data datasets.