Extracts relation triplets contained in sentences of a document. While the StanfordNLP Relation Extractor
node extracts pre-defined types of relations between two named-entities, this node extracts entailed clauses
which then are reduced to their main statement and split into subject, predicate and object.
The node can be used in two different ways by either checking the Apply preprocessing option or not. If the option is selected, the node takes care of part-of-speech (POS) and named-entity (NE) tagging as well as lemmatizing. Stanford CoreNLP standard settings are used in this case. However, tags are not applied to the documents, since the preprocessing is only applied internally. If the option is unchecked, it is necessary to provide a column with (at least POS) tagged documents as well as a column containing lemmatized documents. Lemmatized documents consist of terms that were converted to their canonical, dictionary or citation form.
Note: Creating the same pipeline by using KNIME's Stanford nodes with default settings will not necessarily lead to the same results as using the Apply preprocessing option, since KNIME is using the Penn-Treebank (PTB) tag set. This tag set uses the
SYM tag for
any kind of punctuation and quotation marks. However, Stanford CoreNLP uses a modified version of the
PTB tag set to distinguish these symbols, since they are important for dependency parsing and
natural logic annotation.
The node creates four new columns: a subject column, an object column, a predicate column and a column containing the confidence for the relation between the subject and the object. It is possbile that the node cannot extract a relation from a document, because it only extracts positive sequences. For example, for the sentence "No house cats have rabies. no relation is extracted. A detailed explanation of how clauses are extracted and shortened and how the confidence is calculated can be found in this paper from the StanfordNLP group.
Note: Open Information Extraction is a computationally expensive operation. For the usage of this node it is recommended to run KNIME with at least 4GB of heap space. To increase the heap space, change the -Xmx setting in the knime.ini file.
This node is based on Stanford CoreNLP 3.9.1.
For more information about StanfordNLP and Open Information Extraction, click here.
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To use this node in KNIME, install the extension KNIME Textprocessing from the below update site following our NodePit Product and Node Installation Guide:
A zipped version of the software site can be downloaded here.
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