Unsupervised Relation Mapping: Going from Text to Schema

The schema of a database models the knowledge content of the database. However, database users often have natural language text documents, e.g., relatively unstructured data, with information related to the database. Understanding the semantics of the text documents entails the identification of entities in the document and the relations (as specified in the schema) that connect the entities. This disclosure describes techniques to find the correct relationship in the schema for a given input pair of entities. Per the techniques, two inputs are extracted from the documents - the pairs (knowledge graph entity, input string) and a set of target attributes, e.g., binary relations between entities and other entities or values that capture particular domain semantics. A list of attributes is returned, ranked by the likelihood that the attributes capture the semantics of the input string regarded as an attribute of the input knowledge graph entity

The Ontop framework for ontology based data access

Ontology Based Data Access Ontology Based Data Access (OBDA) Formally, an OBDA system is a triple O = T , S, M , where: -T is the intensional level of an ontology. We consider ontologies formalized in description logics (DLs), hence T is a DL TBox. -S is a relational database representing the sources. -M is a set of mapping assertions, each one of the form • Φ(x) is a query over S, returning tuples of values for x • Ψ (x) is a query over T whose free variables are from x. The main functionality of OBDA systems is query answering. A schematic description of the query transformation process (usually SPARQL to SQL) performed by a typical OBDA system is provided i