Knowledge Graph Technology Showcase Honest Review: Ontotext GraphDB (Winter UPDATE 2023 E7)

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Not Sponsored Ontotext GraphDB has recently rolled out an update so you can talk to your graph database and pose complex questions via LLM to your graph. This helps limit the blockers of accessing information from your graph. Come check out the new feature and the demo you can try for yourself below.
Note: all opinions are my own as a data scientist and researcher in the field and are not representative of the tool/company being reviewed, nor of any other company.
Stay in touch:
LinkedIn: / ashleighnfaith
Direct Message: isadatathing-at-gmail.com
Resources:
Talk to Your Graph: graphdb.ontote...
ChatGPT Retrieval Connector: graphdb.ontote...
Product link too (for downloading and trying things out): www.ontotext.c...

Опубликовано:

17 сен 2024

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Комментарии : 6

@mikecanavan6184 8 месяцев назад

Hi Ashleigh, I'm the VP of Sales for an emerging Knowledge Graph company, Kobai.. Appreciate your channel.

@alioungorDIOUF-f4e Месяц назад

HI Ashleigh, as you did with your configuration because I had a problem with the configuration.

@DavidMedinets 2 месяца назад

Thanks for producing this video. I did not realize that GraphDB was so advanced. This feature is very relevant since I have a process to turn all of my data into RDF/TTL. I now wonder how GraphDB compares to Neo4j and AWS Neptune. I have a dataset that updates monthly that is roughly 700MB in Neo4j. It would be interesting to compare technologies.

@AshleighFaith 2 месяца назад

I know Neo just came out with a csv to graph library and they also have neosemantics that converts to RDF. Neptune doesn’t have a native data science studio but they obviously have sagemaker and all the other ML tools that you can use in their suite. And yah, I have wanted to do a side by side with the same dataset and queries over all the graph databases I have reviewed but I just haven’t found the time yet!

@DavidMedinets 2 месяца назад

@@AshleighFaith Hi. In my situation, the source files are compressed S3 objects. Our infrastruture policy did not allow creating a large temporary EBS volume to hold a copy of the data. Therefore, I wrote a python script to read the files in S3, then pump the data into Neo4j. This approach traded local volume size for import speed. This comment is apropos of nothing. I just wanted to vent. :)

@AshleighFaith 2 месяца назад

@DavidMedinets we all need to do that sometimes! But I’m glad you shared your work-around because that’s the real value of sharing knowledge.