Multilingual qa

ISWC 2017, Vienna, Austria
AMUSE: Multilingual Semantic Parsing for
Question Answering over Linked Data
Sherzod Hakimov, Soufian Jebbara & Philipp Cimiano
Semantic Computing Group
CITEC, Bielefeld University
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ISWC 2017 Vienna, Austria
Virtual Assistants
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Siri
Google Now
Alexa
Cortana

Multilingual -Factoid Question Answering
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Problem Definition -Question Answering
SELECT ?x WHERE {
dbr:Wikipedia dbo:author ?x .
}
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Who created Wikipedia?

SELECT ?x WHERE {
}
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Wer hat Wikipedia gegründet?
¿Quién creó Wikipedia?

SELECT ?x WHERE {
}
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SELECT ?x WHERE {
}
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QALD - 7 Multilingual Question Answering Dataset, ESWC 2017
8 languages
215 Train instances
44 Test instances

Motivation
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Who created Wikipedia? Wer hat Wikipedia gegründet? ¿Quién creó Wikipedia?

Motivation
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Who created Wikipedia? Wer hat Wikipedia gegründet? ¿Quién creó Wikipedia?
Universal Dependencies v2, 50 languages

Knowledge Base -DBpedia
! 2016-04 release
! 125 languages
! 754 classes
! 1,103 object properties
! 1,608 datatype properties
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Preliminaries
Logical Form
Semantic Composition using Dependency Parse Tree
Factor Graphs
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Logical Form -DUDES
• Dependency-based Underspecified Discourse Representation Structures (Cimiano et al [1])
• Formalism for specifying meaning representation
• Flexible semantic composition w.r.t order of application
• Build on semantic dependencies e.g. suitable for working with dependency-based syntactic
analysis
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[1] Cimiano, P., 2009, Flexible semantic composition with DUDES. In Proceedings of the Eighth International Conference on Computational Semantics (pp. 272-276). Association for Computational
Linguistics.

DUDES
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v : is the main variable
vs : is a set of variables (possibly empty), the projection variables
l : is the label of the main DRS
drs : is a DRS (Discourse Representation Structure)
slots : is a set of semantic dependencies (possibly empty)

DUDES
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DUDES
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DUDES
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Semantic Composition with DUDES
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Semantic Composition with DUDES -Bottom Up
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ISWC 2017 Vienna, Austria 23

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Logical Form into SPARQL
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Factor Graphs
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Observed Variables : Nodes, Relations Hidden Variables : KB Ids, Logical Forms, Slots

Approach
- Semantic Parsing using dependency parse tree
- Language independent pipeline
- Model based on factor graphs
- SampleRank to optimise features
- Inference strategy based on MCMC (Markov Chain Monte Carlo)
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Approach
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Approach
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Inference
Semantic Composition
Query Construction

Inference -Markov Chain Monte Carlo
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Stack Decoding

Inference
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Initial State

Inference
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Initial State
m - sampling steps

Inference
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Initial State Sampled State
m - sampling steps

Inference
2 strategies to explore the search space
1) Linking to Knowledge Base (L2KB)
2) Query Construction (QC)
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Inference
2 strategies to explore the search space
1) Linking to Knowledge Base (L2KB)
• objective : compare set of URIs to the expected set of URIs
2) Query Construction (QC)
• objective : compare the constructed query to the expected query
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Linking to Knowledge Base (L2KB)
Explore the edges and assign Knowledge Base IDs based on lemmas of nodes
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Explore the edges and assign Knowledge Base IDs based on lemmas of nodes
Check the triple pattern- ?x dbo:author dbr:Wikipedia : Slot 2, dbr:Wikipedia dbo:author ?x : Slot1
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Query Construction (QC)
Input : sampled state(s) from L2KB
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Query Construction (QC)
Assign DUDES with Return Variable and KB ID to nodes and assign remaining slots
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Features
Edge-based
Lemma, POS, Dependency-Relation, Slot number, KB ID, rdfs:domain/range restrictions
Similarity (Lemma, KB Id)
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Addressing The Lexical Gap
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Who is the writer of The Hunger Games ?
Give me all movies with Tom Cruise. movies — rdf:type dbo:Film
writer — dbo:author

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DBpedia Labels
MATOLL[1]
Word Embeddings[2]
[1] : Walter, S., Unger, C., Cimiano, P.: Dblexipedia: A nucleus for a multilingual lexical semanticweb. In: Proceedings of 3th International Workshop on NLP and DBpedia, co-located withthe ISWC 2015
[2] : Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems.(2013)

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DBpedia Labels
• rdfs:label translated from English to Spanish and German using dict.cc
MATOLL[1]
Word Embeddings[2]

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DBpedia Labels
MATOLL[1]
• English, German and Spanish lexica
Word Embeddings[2]

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DBpedia Labels
MATOLL[1]
• English, German and Spanish lexica
Word Embeddings[2]
• Skip-gram model with 100 dimensions trained on Wikipedia text (for 3
languages)
• Cosine similarity between rdfs:label and mention text

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Word Embeddings
• Skip-gram model with 100 dimensions trained on Wikipedia text (for 3
languages)
• Cosine similarity between rdfs:label and mention text

Evaluation
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• Lexicon
• QA

Evaluation -Lexicon
- Gold standard : manually annotated lexicon (only DBpedia Ontology properties and classes)
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Evaluation -Lexicon
- Gold standard : manually annotated lexicon (only DBpedia Ontology properties and classes)
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English German Spanish

Evaluation -Question Answering
- Model trained and tested on QALD-6
- Evaluated L2KB and QC separately
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55DBlex : MATOLL lexica, Dict: Manually created lexica

System Errors
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• Property (%48)
Who wrote the song Hotel California? - dbo:musicalArtist for song instead of the dbo:writer

• Resource (%30)
When did the Boston Tea Party take place? - The resource wasn’t found

• Query Type (%12)
Where does Piccadilly start? - wrongly infers that this is an ASK-query

• Slot (%10)
How many people live in Poland? - Poland is inferred to ﬁll the 2nd slot instead of the 1st slot of dbo:populationTotal

Conclusion
- Multilingual Semantic Parsing approach based on factor graphs
- Model generalises well even trained with only 161 instances
- Language independent
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Future directions
- Improve results by adding additional inference layers e.g. Query Type Classification
- Apply different ranking functions e.g. Regression, Pair-wise State comparison
- Add more lexical knowledge from other sources
- Paraphrasing the training questions to learn from more data
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Thanks!
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@sherzodhakimov
shakimov AT techfak.uni-bielefeld.de

Approach
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Observed Variables : Dependency parse tree nodes, relations
Hidden Variables : Assigned KB IDs, DUDES, Slots

Factor Graphs
Formal Definition:
! A factor graph G consists of variables V and factors Ψ . Variables can be subdivided into observed variables
xi and hidden variables yi.
! A factor connects subsets of observed and hidden variables and computes a scalar score based on a
feature vector fi(xi,yi) and a set of parameters θi:
● The probability for y for a given input x can be calculated as:
[1] Hakimov, S., ter Horst, H., Jebbara, S., Hartung, M., Cimiano, P.: Combining Textual and Graph-based Features for Named Entity Disambiguation Using Undirected Probabilistic Graphical Models.
[2] Kschischang, F.R., Frey, B.J., Loeliger, H.A.: Factor Graphs and Sum Product Algorithm.
[3] ter Horst, H., Hartung, M. and Cimiano, P. :Joint Entity Recognition and Linking in Technical Domains Using Undirected Probabilistic Graphical Models. LKD 2017
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What is the goal ?
Find y that maximizes the posterior distribution p(y|x; θ)

Multilingual qa

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