Slides for the Commonsense Knowledge Graph, from 2020-07-07

1. Information Sciences Institute
THE COMMONSENSE KNOWLEDGE GRAPH
Filip Ilievski, Pedro Szekely, Jingwei Cheng,
Fu Zhang, Ehsan Qasemi

2. Information Sciences Institute
Component Knowledge Sources
• ConceptNet
– Information about everyday objects, actions, states and relationships among them, extensive links to WordNet
– Incomplete coverage, “related-to” accounts for 75% of statements
• ATOMIC
– Pre- and post-states for events and their participants, physical and mental aspects covered
– Only 25% of nodes have links to ConceptNet, difficult to combine with other resources
• WordNet
– Meanings of words & relationships to other words, high coverage, many resources have links to WordNet, example sentences
– No description of the properties of objects or roles in verbs, only is-a and part-of relations
• VerbNet, FrameNet
– Defines participants/roles for a large number of situations/frames, links to verbs, syntactic forms and example sentences
– No semantic typing of roles, many roles are very abstract (e.g., Agent), lacks info about state changes, or pre-post conditions
• Visual Genome
– “Visual” commonsense, many possible attributes, relationships/actions among objects, linked to WordNet, many edges for a KG
– No abstraction mechanism to understand prevalence of relations
• Wikidata
– Comprehensive descriptions of objects, both specific (named entities) and generic (nouns)
– Sparse information about events and states, much knowledge is on instance-level and abstraction is non-trivial

3. Information Sciences Institute
CSKG Hypothesis
Integrating multiple knowledge sources in
CSKG is beneficial for answering
commonsense questions.

4. Information Sciences Institute
On stage, a woman takes a seat at the piano. She
1. sits on a bench as her sister plays with the doll.
2. smiles with someone as the music plays.
3. is in the crowd, watching the dancers.
4. nervously sets her fingers on the keys.
ConceptNet: pianos have keys, are used to perform music

5. Information Sciences Institute
On stage, a woman takes a seat at the piano. She
1. sits on a bench as her sister plays with the doll.
2. smiles with someone as the music plays.
3. is in the crowd, watching the dancers.
4. nervously sets her fingers on the keys.
ConceptNet: pianos have keys, are used to perform music
WordNet: pianos are played by pressing keys

6. Information Sciences Institute
On stage, a woman takes a seat at the piano. She
1. sits on a bench as her sister plays with the doll.
2. smiles with someone as the music plays.
3. is in the crowd, watching the dancers.
4. nervously sets her fingers on the keys.
ConceptNet: pianos have keys, are used to perform music
WordNet: pianos are played by pressing keys
Visual Genome: person can play a piano while
sitting, his hands are on the keyboard

7. Information Sciences Institute
On stage, a woman takes a seat at the piano. She
1. sits on a bench as her sister plays with the doll.
2. smiles with someone as the music plays.
3. is in the crowd, watching the dancers.
4. nervously sets her fingers on the keys.
ConceptNet: pianos have keys, are used to perform music
WordNet: pianos are played by pressing keys
ATOMIC: to play piano, a person needs to sit at it, on stage and reach for the keys; feelings
Visual Genome: person can play a piano while
sitting, his hands are on the keyboard

8. Information Sciences Institute
On stage, a woman takes a seat at the piano. She
1. sits on a bench as her sister plays with the doll.
2. smiles with someone as the music plays.
3. is in the crowd, watching the dancers.
4. nervously sets her fingers on the keys.
ConceptNet: pianos have keys, are used to perform music
WordNet: pianos are played by pressing keys
Visual Genome: person can play a piano while
sitting, his hands are on the keyboard
ATOMIC: to play piano, a person needs to sit at it, on stage and reach for the keys; feelings
FrameNet:
performer entertains audience

9. Information Sciences Institute
Integration process
• define a simple, modular format
– KGTK format (TSV)
– a single edge file
– currently, 7 sources are represented in CSKG
• extract and create mappings
– as many as possible
– 6 mappings at the moment, mostly identity relations
• integrate and refine
– deduplicate nodes and edges
– merge identical nodes

10. Information Sciences Institute
CSKG stats 4.7M nodes
17.2M edges
284k identity links

11. Information Sciences Institute
CSKG snippet

12. Information Sciences Institute
Answering questions with CSKG (1/2)
Does CSKG improve common sense QA?
Approach:
1. take existing systems that already use ConceptNet (e.g., HyKAS)
2. adapt them to use CSKG
3. apply them to commonsense datasets
Initial insights:

13. Information Sciences Institute
Answering questions with CSKG (2/2)
Q: Bob the lizard lives in a warm place with lots of
water. Where does he probably live?
A: tropical rainforest
Evidence in CSKG:
lizard – AtLocation - tropical rainforest [ConceptNet]
place – HasInstance – tropical [FrameNet-ConceptNet]
water – RelatedTo – tropical [Visual Genome]

14. Information Sciences Institute
CSKG Tutorial @ ISWC 2020
November 5-6, 2020