Lab: Semantic Lifting - CSV: Difference between revisions

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* Reading non-semantic data tables into semantic knowledge graphs
* Reading non-semantic data tables into semantic knowledge graphs
* Specifically, reading data in CSV format via Pandas dataframes into RDF graphs
* Specifically, reading data in CSV format via Pandas dataframes into RDF graphs
* ''If you have more time'', reading data in CSV format via Ontotext Refine into RDF graphs


== Useful materials ==
== Useful materials ==
The textbook (Allemang, Hendler & Gandon):  
The textbook (Allemang, Hendler & Gandon):  
* chapter on RDF (section on ''Distributing Data across the Web'')
* chapter 3 on RDF (from section 3.1 on ''Distributing Data across the Web'')
* [https://github.com/fivethirtyeight/data/tree/master/russia-investigation Information about the dataset]


Pandas:
Pandas:
* class: DataFrame (methods: read_csv, apply, iterrows, astype)
* [https://towardsdatascience.com/pandas-dataframe-playing-with-csv-files-944225d19ff Article about working with pandas.DataFrames and CSV]
* [https://pandas.pydata.org/docs/user_guide/io.html#parsing-options User Guide for Pandas]
* class: DataFrame (methods: read_csv, set_index, apply, iterrows, astype)


rdflib:
rdflib:
* classes/interfaces from earlier (such as Graph, Namespace, URIRef, Literal, perhaps BNode)
* classes/interfaces from earlier (such as Graph, Namespace, URIRef, Literal, perhaps BNode)
* also vocabulary classes like RDF and XSD (for datatypes)
* also vocabulary classes like RDF (e.g., type), RDFS (e.g., label) and XSD (for various datatypes)
 
''If you have more time'', Ontotext Refine:
* [https://www.ontotext.com/products/ontotext-refine/ Ontotext Refine Product page]
* [https://platform.ontotext.com/ontorefine/ Ontotext Refine Documentation]


== Tasks ==
== Tasks ==
Line 26: Line 34:


'''Task:'''
'''Task:'''
Inspect the Pandas dataframe. If you have called your dataframe ''df'', you can start with the expressions below. Use the documentation to understand what each of them does.
''(Pandas basics)'' Inspect the Pandas dataframe. If you have called your dataframe ''df'', you can check out the expressions below. Use the [https://pandas.pydata.org/docs/user_guide/io.html#parsing-options documentation] to understand what each of them does.
  df.shape
  df.shape
  df.index
  df.index # ...and list(df.index)
  df.columns
  df.columns
df['name']
  df.name
  df.name
  df['name']
  df.loc[3]
 
df.loc[3]['president']
(Pandas offers many ways of picking out rows, columns, and values. These are just examples to get started.)


** You can use the data in the Turtle file [[File:russia_investigation_kg.txt]]. Make sure you save it with the correct extension, as ''russia_investigation_kg.ttl'' (not ''.txt'').
'''Task:'''
''(Pandas basics)'' Pandas' apply method offers a compact way to process all the rows in a dataframe. This line lists all the rows in your dataframe as a Python dict():
df.apply(lambda row: print(dict(row)), axis=1)
What happens if you drop the ''axis'' argument, or set ''axis=0''?


'''Task:'''
'''Task:'''
Using the data in ''russia_investigation_kg.ttl'', write the following SPARQL SELECT queries.
Instead of the ''lambda'' function, you can use a named function. Write a function that prints out only the ''name'' and ''indictment-days'' in a row, and use it to print out the ''name'' and ''indictment-days'' for all rows in the dataframe.
([[Russian investigation KG | This page explains]] the Russian investigation KG a bit more.)


It contains the following columns:
''Alternative to df.apply():''
* investigation
Pandas offers several ways to iterate through data. You can also use the ''itertuples'' methods in a simple ''for''-loop to iterate through rows.
* investigation-start
* investigation-end
* investigation-days
* name
* indictment-days
* type
* cp-date
* cp-days
* overturned
* pardoned
* american
* president


More information about the columns and the dataset here: https://github.com/fivethirtyeight/data/tree/master/russia-investigation
'''Task:'''
Modify your function so it adds ''name'' and ''indictment-days'' triples to a global rdflib ''Graph'' for each row in the dataframe. The subject in each triple could be the numeric index of the row.


Our goal is to convert this non-semantic dataset into a semantic one. To do this we will go row-by-row through the dataset and extract the content of each column.
You can use standard terms from RDF, RDFS, XSD, and other vocabularies when you see fit. Otherwise, just use an example-prefix.
An investigation may have multiple rows in the dataset if it investigates multiple people, you can choose to represent these as one or multiple entities in the graph. Each investigation may also have a sub-event representing the result of the investigation, this could for instance be indictment or guilty-plea.


For a row we will start by creating a resource representing the investigation. In this example we handle all investigations with the same name as the samme entity, and will therefore use the name of the investigation ("investigation"-column) to create the URI:
Things may be easier if you copy ''df.index'' into an ordinary column of the dataframe:
df['id'] = df.index
You can use this index, along with a prefix, as the subject in your triples.


=== Semantic Vocabularies ===
'''Task:'''
You do not have to use the same ones, but these should be well suited.
Continue to extend your function to convert the non-semantic CSV dataset into a semantic RDF one. Here is an example of how the data for one investigation could look like in the end:
* RDF: type
muellerkg:investigation_0 a muellerkg:Indictment ;
* RDFS: label
    muellerkg:american true ;
* Simple Event Ontology (sem): Event, eventType, Actor, hasActor, hasActorType, hasBeginTimeStamp, EndTimeStamp, hasTime, hasSubEvent
    muellerkg:cp_date "1973-01-30"^^xsd:date ;
* TimeLine Ontology (tl): durationInt
    muellerkg:cp_days -109 ;
* An example-namespace to represent terms not found elsewhere (ex): IndictmentDays, Overturned, Pardoned
    muellerkg:indictment_days -246 ;
* DBpedia
    muellerkg:investigation muellerkg:watergate ;
    muellerkg:investigation_days 1492 ;
    muellerkg:investigation_end "1977-06-19"^^xsd:date ;
    muellerkg:investigation_start "1973-05-19"^^xsd:date ;
    muellerkg:name muellerkg:James_W._McCord ;
    muellerkg:outcome muellerkg:conviction ;
    muellerkg:overturned false ;
    muellerkg:pardoned false ;
    muellerkg:president muellerkg:Richard_Nixon .


== If you have more time ==


'''Task:'''
If you have not done so already, you should include checks to ensure that you do not add empty columns to your graph.


<syntaxhighlight>
'''Task:'''
name = row["investigation"]
In the SPARQL exercise and earlier in this lab, you used data downloaded as a Turtle file ([[File:russia_investigation_kg.txt]], which you renamed to ''.ttl''). This time you will [https://github.com/fivethirtyeight/data/tree/master/russia-investigation download the data as a CSV file from GitHub].


investigation = URIRef(ex + name)
'''Task:'''
g.add((investigation, RDF.type, sem.Event))
Install Ontotext Refine on your computer, and create a repository in GraphDB named ''refine''.
</syntaxhighlight>
 
Further we will create a relation between the investigation and all its associated columns. For when the investigation started we'll use the "investigation-start"-column and we can use the property sem:hasBeginTimeStamp:
 
<syntaxhighlight>
investigation_start = row["investigation-start"]
 
g.add((investigation, sem.hasBeginTimeStamp, Literal(investigation_start, datatype=XSD.date)))
</syntaxhighlight>
 
To represent the result of the investigation, if it has one, We can create another entity and connect it to the investigation using the sem:hasSubEvent. If so the following columns can be attributed to the sub-event:
* type
* indictment-days
* overturned
* pardon
* cp_date
* cp_days
* name (the name of the investigatee, not the name of the investigation)
 
=== Code to get you started ===
<syntaxhighlight>


import pandas as pd
* Windows
import rdflib
** Download the Refine ''.msi'' installer file.
** Double-click the application file and follow the on-screen installer prompts.
** You will be asked for an installation location.
** Locate the application in the Windows Start menu or on the desktop and start it.
** The Refine application opens at http://localhost:7333/.


from rdflib import Graph, Namespace, URIRef, Literal, BNode
* MacOS
from rdflib.namespace import RDF, RDFS, XSD
** Download the Refine ''.dmg'' file.
** Double-click it and get a virtual disk on your desktop.
** Copy the program from the virtual disk to your hard disk Applications folder.
** Locate the application on the desktop and start it.
** The Refine application opens at http://localhost:7333/.


ex = Namespace("http://example.org/")
* Linux
dbr = Namespace("http://dbpedia.org/resource/")
** Download the Refine ''.deb'' or ''.rpm'' file.
sem = Namespace("http://semanticweb.cs.vu.nl/2009/11/sem/")
** Install the package:
tl = Namespace("http://purl.org/NET/c4dm/timeline.owl#")
** Debian and derivatives: sudo dpkg -i <package-name>.deb
** Redhat and derivatives: sudo rpm -i  <package-name>.rpm
** CentOS and derivatives: sudo yum install <package-name>
** Alternatively, double-click the package.
** Locate the application on the desktop and start it.
** The Refine application opens at http://localhost:7333/.


g = Graph()
Note: Alternatively you can change the ''Repository ID'' in Ontotext Refine under the Setup tab to either a preexisting repository's name, or to one that you would prefer. Ontotext Refine will not make changes to this reposity.
g.bind("ex", ex)
g.bind("dbr", dbr)
g.bind("sem", sem)
g.bind("tl", tl)


df = pd.read_csv("data/investigations.csv")
'''Task:''' ''Data loading''
# We need to correct the type of the columns in the DataFrame, as Pandas assigns an incorrect type when it reads the file (for me at least). We use .astype("str") to convert the content of the columns to a string.
df["name"] = df["name"].astype("str")
df["type"] = df["type"].astype("str")


# iterrows creates an iterable object (list of rows)
Load the CSV-file into Ontotext Refine, and convert the columns' types into the appropriate ones. (Dates to dates, ints to ints.)
for index, row in df.iterrows():
# Do something here to add the content of the row to the graph
pass


g.serialize("output.ttl", format="ttl")
'''Task:''' ''Setting up RDF mappings'' 
</syntaxhighlight>


== If you have more time ==
Use the Visual RDF Mapper tool to map columns to triples. Try to recreate the structure we had in the KG up until this point. Make sure to set types for literals!
If you have not already you should include some checks to assure that you don't add any empty columns to your graph.


If you have more time you can implement DBpedia Spotlight to link the people mentioned in the dataset to DBpedia resources.
'''Task:''' ''Setting up RDF mappings'' 
You can use the same code example as in the last lab, but you will need some error-handling for when DBpedia is unable to find a match. For instance:


<syntaxhighlight>
In addition to the previous structure, we would also like to store the names of presidents as properties.
# Parameter given to spotlight to filter out results with confidence lower than this value
Modify your mapping so each president has an foaf:surname, and a foaf:lastname property, with the appropriate values.
CONFIDENCE = 0.5


def annotate_entity(entity, filters={"types":"DBpedia:Person"}):
'''Task:''' ''Testing your KG''
annotations = []
try:
annotations = spotlight.annotate(SERVER, entity, confidence=CONFIDENCE, filters=filters)
    # This catches errors thrown from Spotlight, including when no resource is found in DBpedia
except SpotlightException as e:
print(e)
# Implement some error handling here
return annotations
</syntaxhighlight>


Here we use the types-filter with DBpedia:Person, as we only want it to match with people. You can choose to only implement the URIs in the response, or the types as well. An issue here is that
Load your freshly created triples into GraphDB, and try to run the queries you have created during the previous labs.
 
If they do not work as they did before, try and update your mappings so they do!
== Useful readings ==
* [https://github.com/fivethirtyeight/data/tree/master/russia-investigation Information about the dataset]
* [https://towardsdatascience.com/pandas-dataframe-playing-with-csv-files-944225d19ff Article about working with pandas.DataFrames and CSV]
* [https://pandas.pydata.org/pandas-docs/stable/reference/frame.html Pandas DataFrame documentation]
* [https://semanticweb.cs.vu.nl/2009/11/sem/#sem:eventType Simple Event Ontology Descripiton]
* [http://motools.sourceforge.net/timeline/timeline.html The TimeLine Ontology Description]
* [https://www.dbpedia-spotlight.org/api Spotlight Documentation]

Latest revision as of 18:56, 3 March 2024

Topic

  • Reading non-semantic data tables into semantic knowledge graphs
  • Specifically, reading data in CSV format via Pandas dataframes into RDF graphs
  • If you have more time, reading data in CSV format via Ontotext Refine into RDF graphs

Useful materials

The textbook (Allemang, Hendler & Gandon):

Pandas:

rdflib:

  • classes/interfaces from earlier (such as Graph, Namespace, URIRef, Literal, perhaps BNode)
  • also vocabulary classes like RDF (e.g., type), RDFS (e.g., label) and XSD (for various datatypes)

If you have more time, Ontotext Refine:

Tasks

We will be working with the same dataset as in the SPARQL exercise: FiveThirtyEight's Russia Investigation. It contains data about special investigations conducted by the United States from the Watergate-investigation until May 2017. This page explains the Russia Investigation dataset a bit more.

Task: In the SPARQL exercise, you downloaded the data as a Turtle file (File:Russia investigation kg.txt, which you renamed to .ttl). This time you will download the data as a CSV file from GitHub.

Task: Install Pandas in your virtual environment, for example 

pip install pandas

Write a Python program that imports the pandas API and uses Pandas' read_csv function to load the russia-investigation.csv dataset into a Pandas dataframe.

Task: (Pandas basics) Inspect the Pandas dataframe. If you have called your dataframe df, you can check out the expressions below. Use the documentation to understand what each of them does. 

df.shape
df.index  # ...and list(df.index)
df.columns
df['name']
df.name
df.loc[3]
df.loc[3]['president']

(Pandas offers many ways of picking out rows, columns, and values. These are just examples to get started.)

Task: (Pandas basics) Pandas' apply method offers a compact way to process all the rows in a dataframe. This line lists all the rows in your dataframe as a Python dict(): 

df.apply(lambda row: print(dict(row)), axis=1)

What happens if you drop the axis argument, or set axis=0?

Task: Instead of the lambda function, you can use a named function. Write a function that prints out only the name and indictment-days in a row, and use it to print out the name and indictment-days for all rows in the dataframe.

Alternative to df.apply(): Pandas offers several ways to iterate through data. You can also use the itertuples methods in a simple for-loop to iterate through rows.

Task: Modify your function so it adds name and indictment-days triples to a global rdflib Graph for each row in the dataframe. The subject in each triple could be the numeric index of the row.

You can use standard terms from RDF, RDFS, XSD, and other vocabularies when you see fit. Otherwise, just use an example-prefix.

Things may be easier if you copy df.index into an ordinary column of the dataframe: 

df['id'] = df.index

You can use this index, along with a prefix, as the subject in your triples.

Task: Continue to extend your function to convert the non-semantic CSV dataset into a semantic RDF one. Here is an example of how the data for one investigation could look like in the end: 

muellerkg:investigation_0 a muellerkg:Indictment ;
    muellerkg:american true ;
    muellerkg:cp_date "1973-01-30"^^xsd:date ;
    muellerkg:cp_days -109 ;
    muellerkg:indictment_days -246 ;
    muellerkg:investigation muellerkg:watergate ;
    muellerkg:investigation_days 1492 ;
    muellerkg:investigation_end "1977-06-19"^^xsd:date ;
    muellerkg:investigation_start "1973-05-19"^^xsd:date ;
    muellerkg:name muellerkg:James_W._McCord ;
    muellerkg:outcome muellerkg:conviction ;
    muellerkg:overturned false ;
    muellerkg:pardoned false ;
    muellerkg:president muellerkg:Richard_Nixon .

If you have more time

Task: If you have not done so already, you should include checks to ensure that you do not add empty columns to your graph.

Task: In the SPARQL exercise and earlier in this lab, you used data downloaded as a Turtle file (File:Russia investigation kg.txt, which you renamed to .ttl). This time you will download the data as a CSV file from GitHub.

Task: Install Ontotext Refine on your computer, and create a repository in GraphDB named refine.

  • Windows
    • Download the Refine .msi installer file.
    • Double-click the application file and follow the on-screen installer prompts.
    • You will be asked for an installation location.
    • Locate the application in the Windows Start menu or on the desktop and start it.
    • The Refine application opens at http://localhost:7333/.
  • MacOS
    • Download the Refine .dmg file.
    • Double-click it and get a virtual disk on your desktop.
    • Copy the program from the virtual disk to your hard disk Applications folder.
    • Locate the application on the desktop and start it.
    • The Refine application opens at http://localhost:7333/.
  • Linux
    • Download the Refine .deb or .rpm file.
    • Install the package:
    • Debian and derivatives: sudo dpkg -i <package-name>.deb
    • Redhat and derivatives: sudo rpm -i <package-name>.rpm
    • CentOS and derivatives: sudo yum install <package-name>
    • Alternatively, double-click the package.
    • Locate the application on the desktop and start it.
    • The Refine application opens at http://localhost:7333/.

Note: Alternatively you can change the Repository ID in Ontotext Refine under the Setup tab to either a preexisting repository's name, or to one that you would prefer. Ontotext Refine will not make changes to this reposity.

Task: Data loading

Load the CSV-file into Ontotext Refine, and convert the columns' types into the appropriate ones. (Dates to dates, ints to ints.)

Task: Setting up RDF mappings

Use the Visual RDF Mapper tool to map columns to triples. Try to recreate the structure we had in the KG up until this point. Make sure to set types for literals!

Task: Setting up RDF mappings

In addition to the previous structure, we would also like to store the names of presidents as properties. Modify your mapping so each president has an foaf:surname, and a foaf:lastname property, with the appropriate values.

Task: Testing your KG

Load your freshly created triples into GraphDB, and try to run the queries you have created during the previous labs. If they do not work as they did before, try and update your mappings so they do!

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