What tools are you using for knowledge graph building? - UCAN2
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iamtherhino
We see Neo4j get used relatively frequently as the aggregate view of data
pipelines, e.g., Roam Analytics uses Neo4j to spit out tables/views across
many different data sources to perform ML enrichment on that they then pipe
back into the graph to feed their app.

eBay (ShopBot) is a Neo4j powered ML chatbot. AirBnb also builds knowledge
graphs with Neo4j.

Video from GraphConnect today talking about knowledge graphs:
[https://youtu.be/dqrlotzdUlo?t=3175](https://youtu.be/dqrlotzdUlo?t=3175)

Transparency: I'm an employee at Neo4j.

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graqlbot
GRAKN.AI ([https://grakn.ai/](https://grakn.ai/)) is a powerful, open-source
knowledge graph platform. Highly recommended!

\- Disclosure: I work at Grakn Labs. That being said, I am convinced that it
is one of the most innovative solutions out there, and we have a great
community working on really neat projects.

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lcall
[http://www.onemodel.org](http://www.onemodel.org)

It makes an internal knowledge graph as one uses the product (stored in
postgres, runs fast). It builds an object model on the fly as a side-effect of
using the product, using relationships, numbers, etc as knowledge at an atomic
level where words are secondary. The best info organizer (for my style at
least) that I know of, though (so far) less feature-rich than many products. I
hope the About page at that link explains the present and future well.

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meej
for modeling:

VoCol [https://github.com/vocol/vocol](https://github.com/vocol/vocol)

ROBOT [https://github.com/ontodev/robot](https://github.com/ontodev/robot)

Karma [http://usc-isi-i2.github.io/karma/](http://usc-isi-i2.github.io/karma/)

for data:

Blazegraph [https://www.blazegraph.com/](https://www.blazegraph.com/)

for querying:

SPARQL kernel for Jupyter [https://github.com/paulovn/sparql-
kernel](https://github.com/paulovn/sparql-kernel)

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wk0
I've used Apache Jena (Java) for a research project with DBpedia. I know there
are some other options that are a bit quicker for processing RDFs, but I think
most are proprietary.

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ssijak
Hmm, very interesting software proposed here that I did not know of (tried
neo4j). Can you guys tell in a few sentences what differentiate your products?
For example, GRAKN.AI is marketing as best for AI purposes but could not
figure why it was exactly better than other graph DBs. DGraph says it is fast,
is that only differentiator? Etc..

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haikalpribadi
Hi, sorry we didn't manage to clearly capture this question on our site. Grakn
is not "just a graph database". Here are 4 key points on how Grakn is
different from other databases (especially neo4j):

1\. Operates as a database for both OLTP (traditional query transactions) and
OLAP (distributed graph analytics as a language)

2\. Has an ontology as a flexible object model (i.e. schema) with types,
subtypes, rules and instances

3\. Guarantees logical integrity of data with regards to the ontology (i.e.
schema constraint, but on a much more expressive data model)

4\. Reasoning query language, to retrieve explicitly stored data and
implicitly derived information (i.e. infers types, relations, context, and
hierarchies of rules, in real time OLTP).

GRAKN.AI has the logical integrity of SQL, which NoSQL and Graph databases
lack. It has the data relationships like Graph databases, which SQL and NoSQL
do not have. And it scales horizontally like NoSQL, which SQL and Neo4j could
not do.

And here's a more detailed differentiator table with granular points:
[http://links.grakn.ai/362529/10476081](http://links.grakn.ai/362529/10476081)

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ssijak
Is it free and will it always be free? I am asking because you are a
registered company and need to make money somehow (support or?). Would not
commit to something that will ask a lot of money after 2 years. Looks
promising, good luck :)

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haikalpribadi
The technology has an opensource version and enterprise version. Let me
explain..

GRAKN.AI is made of 2 core components:

1\. Grakn: the storage (i.e. knowledge base) where you store data

2\. Graql: the language to retrieve the data Both Grakn and Graql is
opensource and will always be opensource, forever. Just like MySQL, Hadoop,
Spark, etc.

GRAKN.AI Enterprise is a commercial distribution (which will be released in 3
months), which comes with:

1\. Cluster manageent: monitoring and provisioning

2\. Security: authenticaion and custom user access right (granular separation
of access for users based on different portions of the data model)

3\. Natural Language search: both fuzzy string matching and NLP search

4\. Knowledge IDE: and IDE for UI-driven knowledge modeling, and IDE to
develop the model, and all kinds of modeling and analysis tool to help you
manage your knowledge base.

All 4 features above are not available in the opensource distribution. To get
them, you need to purchase GRAKN.AI Enterprise. The user can decide to
purchase them when they need them.

I hope that helps? Let me know if that makes sense. :)

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crypto5
How would you compare your product with
[https://www.blazegraph.com/](https://www.blazegraph.com/), which has similar
feature set, but is much longer on market, and has wider adaptation(e.g.
powers wikidata)?

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fppt
Blazegraph at the core, is a property graph which persists into an RDF format.
A property graph is a simple graph structure made up of vertices and edges. It
does not inherently encapsulate any domain or knowledge.

Grakn sits a layer above this in that is a knowledge graph. You can think of a
knowledge graph as a property graph consolidated by an ontology or schema
which enables it to encapsulate domain specific information in a structured
manner. This in turn enables more advance features such as the automatic
resolution of data based on pre defined rules.

You could indeed build a knowledge graph using Blazegraph (or any other
property graph) but you would have to go through all the pains of coming up
with an integrated and flexible schema as well as a resolution mechanism.
Grakn comes with these things out of the box.

So Grakn is not competing with Blazegraph but rather builds on the core
principals used by Blazegraph, TitanDB, JanusGraph, and other property graph
systems. It builds on it to provide a structured yet flexible graph as well as
a built in resolution system.

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jhoechtl
DGraph [http://dgraph.io/](http://dgraph.io/)

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fnazeeri
At my company we built this (open source) tool for authoring knowledge graphs
[https://extensionengine.com/ee-labs/](https://extensionengine.com/ee-labs/)

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mindcrime
Apache Jena, along with Apache Stanbol.

Shameless plug: we are incorporating both into products and will be offering
support/services around both. If anybody is looking for help with this stuff,
give us a shout.

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kajecounterhack
Badwolf [https://github.com/google/badwolf](https://github.com/google/badwolf)
(RDF-based query language)

