Hi everyone. This is the presentation for the paper “KTabulator: Interactive Table
Creation Using Knowledge Graphs”, authored by Steven Xia, Nafisa Anzum, Semih Salihoglu,
and Jian Zhao from the University of Waterloo. My name is Steven Xia, and I will be presenting
the paper on behalf of my co-authors. A table is a natural and commonly used data
format to organize and share data that captures facts about sets of entities and their properties.
Consider a student seeking information about public engineering universities across provinces,
or a basketball enthusiast analyzing past draft picks of NBA teams, among many other
examples. The need to find or construct tables arises frequently in everyday life.
However, many of the table people look for are highly varied and ad hoc, which makes
it very challenging for people to find tables on the web that directly satisfy their needs.
When relevant tables are found, they are often incomplete and existing systems do not provide
an easy way to complete the missing information. As a result, preparing the tables can be a
tedious task, requiring manual copy-pasting from browsed information, or searching for
related tables that can be unioned or joined with existing ones using a spreadsheet software.
To address these challenges, in this research, we aim to help users efficiently create ad
hoc tables they need by leveraging a knowledge graph. Knowledge graphs store information
about individual entities and their relationships, such as politicians and celebrities, and geographical
or scientific entities, in some-graph structured data model, making them suitable to be processed
by software systems. In particular, We contribute an interactive
and exploratory data gathering system called KTabulator that uses Wikipedia and its associated
knowledge graph DBpedia as a test-bed. KTabulator puts the power of knowledge graphs directly
into the hands of humans. In a human-in-the-loop manner, users start creating tables from an
entity or a pre-existing table in a Wikipedia article and interactively transform these
tables by: (i) adding sets of related entities as new rows; or (ii) adding new properties
of existing entities in these tables in new columns. Additional entities and properties
can be extracted directly from DBpedia as well as pre-existing tables in the Wikipedia
articles. We now give a quick demo of KTabulator. Suppose
you want to recommend some movies to your friend to watch at home during covid-19, and
you know The Dark Knight is one of the movie that your friend likes, so you want to create
a table using The Dark Knight as a starting point.
We first copy and paste The Dark Knight’s wikipedia page into KTabulator’s landing
page to get started. On the left we have the table panel, which currently has The Dark
Knight as the only entry of the table. On the right a list of properties that describe
The Dark Knight is displayed, including 2000s superhero films. Upon selecting this property,
we can add a list of 2000s superhero movies to the first column of the table. To add attributes
about the movies to the table, we can click on the “edit icon” of an empty column.
Available attributes about the movies can be displayed, followed by a number indicating
what proportion of movies has this attribute in the knowledge graph. By selecting director,
we can add directors of all the movies as a new column to the table. After adding the
“director” column, a list of attribute recommendations is given on the right, including
“distributor”, “producer”, etc. By clicking on the “producer” suggestion,
we can add the producers of the movies to the table as well. KTabulator also supports
adding attributes about other columns. After clicking on the search icon of the director
column, we can now add attributes such as “birthplace” of the directors to the table.
For more details about the implementation, please refer to the accompanying paper.
We conducted a user study to assess the usefulness of KTabulator by investigating how people
use the system to create ad hoc tables that fulfill their goals. We recruited 12 participants
(eight males and four females) at the University of Waterloo and conducted the study via Zoom.
In our study, we asked the users to creates two tables, the first one being a table containing
all the Canadian public universities that have faculty size larger than 100 and are
located in cities with population larger than 50,000, and the second being a table of movie
recommendations in which users can build the table however they like.
Overall, our results suggest that users believe KTabulator is easy to learn and use, and the
features to build tables from both knowledge graph entities and existing Wikipedia tables
are useful. Moreover, we report participants’ feedback to KTabulator in several aspects,
such as extracting data, providing guidance, and browsing wikipedia information. The users
also mentioned several limitations, including better table transformation functions and
more data searching capabilities. For our future work, we plan to extend KTabulator
in two ways. First, DBpedia is a fraction of the publicly available knowledge graphs.
There are many other large publicly available knowledge graphs that cover sever knowledge
domains, such as YAGO and Freebase. We would like to extend KTabulator with features to
extract tables out of multiple knowledge graphs. Second, we want to make use of other publicly
available datasets in the linked open data, which is structured data interlinked with
other data. We believe there is tremendous value in putting humans in loop and facilitating
humans to directly access the entire linked data through interactive systems.
Thanks for attending the presentation. I am happy to take any questions.