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Improving Question Answering Quality Through Language Feature-Based SPARQL Query Candidate Validation

Part of the Lecture Notes in Computer Science book series (LNCS,volume 13261)

Abstract

Question Answering systems are on the rise and on their way to become one of the standard user interfaces. However, in conversational user interfaces, the information quantity needs to be kept low as users expect a limited number of precise answers (often it is 1) – similar to human-human communication. The acceptable number of answers in a result list is a key differentiator from search engines where showing more answers (10–100) to the user is widely accepted. Hence, the quality of Question Answering is crucial for the wide acceptance of such systems. The adaptation of natural-language user interfaces for satisfying the information needs of humans requires high-quality and not-redundant answers. However, providing compact and correct answers to the users’ questions is a challenging task. In this paper, we consider a certain class of Question Answering systems that work over Knowledge Graphs. We developed a system-agnostic approach for optimizing the ranked lists of SPARQL query candidates produced by the Knowledge Graph Question Answering system that are used to retrieve an answer to a given question. We call this a SPARQL query validation process. For the evaluation of our approach, we used two well-known Knowledge Graph Question Answering benchmarks. Our results show a significant improvement in the Question Answering quality. As the approach is system-agnostic, it can be applied to any Knowledge Graph Question Answering system that produces query candidates.

Keywords

  • Question Answering over Knowledge Graphs
  • Query Validation
  • Query Candidate Filtering

A. Gashkov and A. Perevalov—Shared first authorship–these authors contributed equally to this work.

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Notes

  1. 1.

    https://www.w3.org/TR/rdf-sparql-query/.

  2. 2.

    https://doi.org/10.6084/m9.figshare.19434515.

  3. 3.

    Measuring the impact on the verbalization regarding the QV results would be part of additional research.

  4. 4.

    Example: Assuming a user asks for the red or green wire to be cut for defusing a bomb, then a guessed answer by the QA system might have a devastating result in real life.

  5. 5.

    A query candidate is considered as correct if \(F1 \; score(y_{pred}, y_{true}) = 1\), where \(y_{pred}\) – is the set of answers obtained with query candidate and \(y_{true}\) is the “gold standard” answer set.

  6. 6.

    https://www.w3.org/TR/turtle/.

  7. 7.

    BERT was consciously trained to understand relationships between two consecutive sentences if the second sentence follows the first one (e.g., “[CLS] the man went to [MASK] store [SEP] he bought a gallon [MASK] milk [SEP]) because many important downstream tasks such as QA and Natural Language Inference (NLI) are based on understanding the relationship between two sentences” [11].

  8. 8.

    The trained Query Validators are available online;

    LC-QuAD: https://huggingface.co/perevalov/query-validation-lcquad,

    RuBQ: https://huggingface.co/perevalov/query-validation-rubq.

  9. 9.

    In our previous study, we already compared QV ’s quality using different query candidate verbalization methods [19].

  10. 10.

    http://gerbil-qa.aksw.org/gerbil/, version 0.2.3.

  11. 11.

    Our results are available online. LC-QuAD 2.0:

    http://gerbil-qa.aksw.org/gerbil/experiment?id=202112080001 and

    http://gerbil-qa.aksw.org/gerbil/experiment?id=202112080002;

    RuBQ 2.0: http://gerbil-qa.aksw.org/gerbil/experiment?id=202112090005 and

    http://gerbil-qa.aksw.org/gerbil/experiment?id=202112090006.

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Gashkov, A., Perevalov, A., Eltsova, M., Both, A. (2022). Improving Question Answering Quality Through Language Feature-Based SPARQL Query Candidate Validation. In: , et al. The Semantic Web. ESWC 2022. Lecture Notes in Computer Science, vol 13261. Springer, Cham. https://doi.org/10.1007/978-3-031-06981-9_13

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