Question Formulation and Question Answering for Knowledge Graph Completion by Maria Khvalchik, Christian Blaschke, and Artem Revenko presented at MLKG DEXA, August 27 2019, Linz

1. Question Formulation and
Question Answering for
Knowledge Graph Completion
by Maria Khvalchik, Christian Blaschke, and Artem Revenko

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Building Legal Knowledge Graph for
Multilingual Compliance Services

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Problem Statement
▸ Need of Knowledge Graph population
▸ Need of up-to-date information in KG
▸ QA systems outperforming humans
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Approach
1. Question formulation
2. Retrieving documents potentially containing answers from the
corpus
3. Employing QA over documents to get candidate answers and
their scores
4. Choosing correct answers
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Question Formulation
Question Patterns:
▸ learned from SQuAD and DBpedia
▸ generalized ways to construct a question:
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Question Formulation
Sentence Patterns:
▸ the input is a set of pairs
▹ where t is a text from a corpus C, sgt
is a subgraph of DBpedia aligned to t
▸ used to generate the Lex and Wh-word functions
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Question Answering
Input: a natural language question and a passage
Output: a set of pairs (answer, confidence score)
SQuAD task:
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Pre-training BERT
Pre-training data: Wikipedia (2.5B words) + BookCorpus (800M words)
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Verification and Integration
1. Match answers produced by the QA system to KG instances
a. No match -> discard
2. For undiscarded answers
a. Check if the class of the matched instances complies with the range
restrictions of the predicate definition in the schema
b. If satisfied -> add the triple (subject, predicate, matched instances) to the
KG
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Test cases:
Two sets of PubMed abstracts
of 3300 and 250 documents.
Experiment
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Conclusion
We considered an important and practically relevant task of link
prediction in KGs.
▸ Combining link prediction techniques with QA system to extract
concealed knowledge from a text corpus and to formulate new
triples.
▸ First experiments show promising results for domain-specific
datasets.
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