Category: NLP

Creating Knowledge Graphs automatically for GraphRAG: Part 1: with NLP

(next post Part 2: with LLM)

I first investigated how NLP could be used for both entity recognition and relation extraction for creating a knowledge graphs of content. Tomaz Bratanic’s Neo4j blog article  used Relik for NLP along with LlamaIndex for creating a graph in Neo4j, and setting up an embedding model for use with LLM queries.

In my llama_relik github project, I used the  notebook from the blog article and changed it to use fastcoref instead of coreferee. Fastcoref was mentioned in the medium article version of the Neo4j blog article in the comments. It’s supposed to work better. There is also a python file in this project than can be used instead of the notebook.

I submitted some fixes to Relik on Windows, but it performs best on Linux in general and was more able to use the GPU “cuda” mode instead of “cpu”.

Similar work has been done using Rebel for NLP by Neo4j / Tomaz Bratanic, Saurav Joshi, and Qrious Kamal

Note that Relik has closed information extraction (CIE) models that do both entity linking (EL) and relation extraction (RE) . It also has models focused on either EL or RE.

Below is a screenshot from Neo4j with a knowledge graph created with the python file from the llama_relik project using the “relik-cie-small” model with the spacy space station sample text (ignore chunk node and it’s mentions relations). Notice how it has separate entities for “ISS” and “International Space Station” .

The “relik-cie-large” model finds more relations in screenshot below. It also has separate entities for “ISS” and “International Space Station” (and throws in second “International Space Station”).

Alfresco GenAI Semantic project updated: now adds regular Alfresco tags, uses local Wikidata and DBpedia entity recognizers

The Alfresco GenAI Semantic  github project  now adds regular Alfresco tags when performing auto tagging when enhancing with links to Wikidata and DBpedia. Semantic entity linking info is kept in 3 parallel multi-value properties (labels, links, super type lists) in the WikiData and DBpedia custom aspects. The labels values are used for the tag labels.

I switched to a local, private Wikidata recognizer.  The spaCy-entity-linker python library is used for getting Wikidata entity links without having to call a public serivce api. It was created before spaCy had its own entity linking system. It still has the advantage of not needing to do training. Had previously used the  spaCyOpenTapioca library, which calls an OpenTapioca public web service api URL. Note the URLs in the links properties do go to the public website wikidata.org if used in your application.

I also switched to a local, private DBpedia Spotlight entity recognizer in a docker composed in. The local URL to this docker is given the to the spacy DBpedia Spotlight for SpaCy library. This library was using a public Spotlight web service api URL by default previously. Note the URLs in the links properties do go to to the public website dbpeda.org if used in your application.

For documents with the Wikidata or DBpedia aspects added to them, tags will show up in the Alfresco clients (ACA, ADW, Share) after PDF rendition creation and alfresco-genai-semantic AI Listener gets responses from REST apis in the genai-stack. Shown below are tags in the ACA community content app:

Multi-value Wikidata aspect properties of a document in the ACA client are shown below in the view details expanded out. The labels property repeats what the labels of the tags have. The links properties have URLs to wikidata.org. The super types properties have the zero “” or one or multiple comma separated super types in wikidata for each entity. These supertypes are wikidata ids (are links once you add “http://www.wikidata.org/wiki/” in front of the ids).

The same style DBpedia aspect multivalue properties are shown below in the ACA client. Note that the super types can be from Wikidata, DBpedia, Schema (schema.org), foaf, or DUL (ontologydesignpatterns.org DUL.owl), etc.