{"id":7888,"date":"2025-08-12T13:23:36","date_gmt":"2025-08-12T13:23:36","guid":{"rendered":"https:\/\/stg.innov8-studios.com\/Semafora-2\/?p=7888"},"modified":"2025-08-12T13:47:56","modified_gmt":"2025-08-12T13:47:56","slug":"new-built-ins-available-for-ontobroker","status":"publish","type":"post","link":"https:\/\/stg.innov8-studios.com\/semafora\/new-built-ins-available-for-ontobroker\/","title":{"rendered":"New Built-ins available for OntoBroker"},"content":{"rendered":"\n

There are built-ins with interface functionality (connectors to external data sources and processing units) and those with pure data processing functionality. Furthermore, there are built-ins that access external processes for data processing, e.g. the Python engine, which can be used for preprocessing by means of machine learning.<\/p>\n\n\n\n

A fourth category are so-called utility built-ins, which e.g. make such data structure contexts explicitly accessible, where a syntactic (in this case symbolic) realization seems too fiddly for the application. Such a case we have with the access to predicate functions. Here, one can now use the _predToMap function to obtain all components of a predicate of first or higher order. For example:<\/p>\n\n\n\n

?x = P(a,b,c)(e,f).<\/p>\n\n\n\n

?- _predToMap(?x,?y).<\/p>\n\n\n\n

Result:<\/p>\n\n\n\n

[0->P,1->[0->a,1->b,2->c],2->[0->d,1->e]]<\/p>\n\n\n\n

Another built-in for data processing is _wordDist, which gives the normalized Levenshtein distance<\/u><\/a> of two strings. The function is useful to find searched words e.g. despite typos or plural\/singular form. For example:<\/p>\n\n\n\n

_wordDist(“material”, “materials”,?x) for ?x gives a value of 0.118<\/p>\n\n\n\n

_wordDist(“material”, “machine”,?x) for ?x gives a value of 0.625<\/p>\n\n\n\n

If it does not give any proximity, the value is 1 and 0 for a complete match.<\/p>\n\n\n\n

The built-ins are available from OntoBroker V 6.3.<\/p>\n<\/body><\/html>\n","protected":false},"excerpt":{"rendered":"