ConnectionLens is a tool for finding connections between user-specified search terms across heterogeneous data sources. ConnectionLens treats a set of heterogeneous, independently authored data sources as a single virtual graph, whereas nodes represent fine-granularity data items (relational tuples, attributes, key-value pairs, RDF, JSON or XML nodes…) and edges correspond either to structural connections (e.g., a tuple is in a database, an attribute is in a tuple, a JSON node has a parent…) or to similarity (sameAs) links. To further enrich the content journalists work with, we also apply entity extraction which enables us to detect the people, organizations, etc. mentioned in the text, whether full-text or text snippets found e.g. in RDF or XML.
ConnectionLens is a tool for finding connections between user-specified search terms across heterogeneous data sources. ConnectionLens treats a set of heterogeneous, independently authored data sources as a single virtual graph, whereas nodes represent fine-granularity data items (relational tuples, attributes, key-value pairs, RDF, JSON or XML nodes…) and edges correspond either to structural connections (e.g., a tuple is in a database, an attribute is in a tuple, a JSON node has a parent…) or to similarity (sameAs) links. To further enrich the content journalists work with, we also apply entity extraction which enables us to detect the people, organizations, etc. mentioned in the text, whether full-text or text snippets found e.g. in RDF or XML.
