Russell Jurney

Added Stanford tokenizer, sentence splitter, and part of speech tagging to varaha.text

4

register ../../lib/stanford-postagger-withModel.jar register ../../target/varaha-1.0-SNAPSHOT.jar reviews = LOAD 'data/ten.avro' USING AvroStorage; foo = FOREACH reviews GENERATE business_id, varaha.text.StanfordTokenize(text) AS tagged; DUMP foo (41J1FgfIsmsLRCZ3QILG6w,{(truly),(impressive),(facility),(came),(for),(two),(books),(not),(knowing),(this),(location),(-LRB-),(normally),(Appaloosa),(-RRB-),(The),(staff),(was),(very),(helpful),(and),(found),(what),(wanted),(very),(quickly),(was),(there),(minutes),(tops),(would),(highly),(recommend),(this),(Library),(anyone),(interested),('ll),(coming),(back),(very),(soon),(for),(next),(batch)}) (4YX4ZtUqs6xtcc4AdjbpeQ,{(Other),(circle),(are),(much),(cleaner),(than),(this),(one),(The),(best),(thing),(about),(this),(store),(the),(Employees),(are),(friendly),(and),(nice),('ve),(been),(this),(location),(the),(morning),(and),(the),(evening),(and),(there),(must),(point),(where),(the),(shift),(changes),(and),(they),(stop),(cleaning),(the),(bathrooms),(and),(emptying),(the),(trash),(the),(morning),(everything),(clean),(the),(time),(evening),(rolls),(around),(there),(are),(odd),(smells),(all),(over),(the),(store),(shame),(since),(larger),(newer),(looking),(store),(that),(n't),(cleaner),('ll),(back),(hopes),(they),(clean),(little),(more)}) (5kRug3bEienrpovtPRVVwg,{(Went),(with),(husband),(Richardson),(Rokerij),(for),(the),(first),(time),(raved),(about),(this),(place),(went),(Wednesday),(night),(with),(reservation),(The),(wait),(was),(about),(hour),(Luckily),(there),(were),(bar),(seats),(that),(became),(available),(took),(them),(ordered),(the),(cheese),(flatbread),(appetizer),(and),(was),(delicious),(had),(large),(salad),(for),(dinner),(which),(was),(perfect),(was),(not),(very),(hungry),(husband),(had),(the),(chicken),(enchiladas),(that),(tasted),(and),(were),(very),(good),(The),(food),(cooked),(order),(did),(take),(while),(get),(our),(meal),(but),(was),(worth),(the),(wait),(and),(service),(was),(excellent),(While),(waiting),(chatted),(with),(several),(people),(the),(bar),(and),(one),(couple),(offered),(taste),(their),(appetizer),(returned),(the),(favor),(when),(flatbread),(came),(One),(more),(thing),(not),(leave),(without),(getting),(the),(decadent),(truffle),(dessert),(Heavenly),(but),(not),(over),(done),(any),(way),(All),(all),(great),(experience),(recommend),(reservations)}) reviews = LOAD 'data/ten.avro' USING AvroStorage();...

See https://issues.apache.org/jira/browse/PIG-3190 Need your permission.

# Use of `graphlet.nlp.ie` The entities and their relations that form the input to this module will be extracted using `graphlet.nlp.ie` - see #11 and #1. # Integration with BLINK...

# Use of `graphlet.etl` Schema Models We can use `graphlet.etl`'s [Pandera Schema Models](https://pandera.readthedocs.io/en/latest/schema_models.html) schema models to define the entities and relations we are extracting. ## About `graphlet.etl` The module `graphlet.etl`...

# Create a generic, configurable system for entity resolution of heterogeneous networks using pre-trained LMs and GATs Graph Neural Networks (GNNs) accept arbitrary features as input... making an embedding produced...

Incorporate spark-rapids into architecture

1

[spark-rapids](https://nvidia.github.io/spark-rapids/) incorporates [RAPIDS](https://rapids.ai/) into [Apache Spark](https://spark.apache.org/) and could accelerate our architecture significantly. * [Getting Started](https://nvidia.github.io/spark-rapids/Getting-Started/) makes this sound easy. * [spark-rapids on Databricks](https://nvidia.github.io/spark-rapids/docs/get-started/getting-started-databricks.html)

# Summary This ticket is to create utilities - including node and edge base classes - that provide an object-oriented interface with runtime validation for defining data types in a...

Implement efficient random motif searching via neural subgraph matching

3

Motif search for heterogeneous networks - especially temporal heterogeneous networks - has fundamental scalability challenges. [Neural Subgraph Matching](https://arxiv.org/abs/2007.03092) proposes a technique using graph representation learning and vector search called _NeuroMatch_....

In order to determine whether a graphlet is a network motif, we need to compare its frequency versus a null model to determine if it is statistically significant. This means...

# PySpark / GraphFrames  # Property Graph Minors