Lab: Semantic Lifting - CSV

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):

• chapter 3 on RDF (from section 3.1 on Distributing Data across the Web)
• Information about the dataset

Pandas:

• Article about working with pandas.DataFrames and CSV
• User Guide for Pandas
• class: DataFrame (methods: read_csv, set_index, apply, iterrows, astype)

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:

• Ontotext Refine Product page
• Ontotext Refine Documentation

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!