Mining Significant Maximum Cardinalities in Knowledge Bases

Giacometti, Arnaud; Markhoff, Béatrice; Soulet, Arnaud

doi:10.1007/978-3-030-30793-6_11

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Upon further investigation, many predicates that should in theory have maximum cardinality 1, such as BirthDate, in fact have maximum cardinality 2. This can be due to errors in the data or uncertain information (Giacometti et al, 2019;Muñoz and Nickles, 2017). Using a curated dataset would likely improve the benefit of the cardinality-based pruning.…”

Section: Algorithm Comparisonmentioning

confidence: 99%

A cooperative treatment of the plethoric answers problem in RDF

et al. 2022

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When querying Knowledge Bases (KBs), users are faced with large sets of data, often without knowing their underlying structures. It follows that users may make mistakes when formulating their queries, therefore receiving an unhelpful response. In this paper, we address the plethoric answers problem, the situation where the user query produces significantly more results than the user was expecting. The common approach to solving this problem, i.e. the top-K approach, reduces the query's result size by applying various criteria to select only some answers. This selection is performed without considering the causes producing plethoric answers, and can therefore miss an underlying issue within the query.We deal with this problem by proposing an approach that identifies the parts of the failing query, called Minimal Failure Inducing Subqueries (MFIS), that cause plethoric answers. As long as the query contains an MFIS, it will fail to reach a sufficiently low amount of answers. Thus, thanks to these MFIS, interactive and automatic approaches can be set up to help the user in reformulating their query. The dual notion of MFIS, called Maximal Succeeding Subqueries (XSS), is also useful. They provide queries with a maximal number of parts of the original query that return non plethoric answers. Our goal is to compute MFIS and XSS efficiently, so that they may be used to solve the plethoric answers problem. We

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Section: Algorithm Comparisonmentioning

confidence: 99%

A cooperative treatment of the plethoric answers problem in RDF

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Upon further investigation, many predicates that should in theory have maximum cardinality 1, such as BirthDate, in fact have maximum cardinality 2. This can be due to errors in the data or uncertain information [24,25]. Using a curated dataset would likely improve the benefit of the cardinality-based pruning.…”

Section: Real Dataset and Queriesmentioning

confidence: 99%

Dealing with Plethoric Answers of SPARQL Queries

Parkin

Chardin

Jean

et al. 2021

Lecture Notes in Computer Science

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When querying Knowledge Bases (KBs), users are faced with large sets of data, often without knowing their underlying structures. It follows that users may make mistakes when formulating their queries, therefore receiving an unhelpful response. In this paper, we address the plethoric answers problem, the situation where a query produces significantly more results than the user was expecting. We deal with this problem by identifying the parts of the failing query, called Minimal Failure Inducing Subqueries (MFIS), that cause plethoric answers. As long as the query contains an MFIS, it will fail to reach a sufficiently low amount of answers. Thanks to these MFIS, interactive and automatic approaches can be set up to help the user reformulate their query. The dual notion of MFIS, maXimal Succeeding Subqueries (XSS), is also useful. They are queries with the most parts of the original query that return non plethoric answers. Our goal is to compute MFIS and XSS efficiently, so that they may be used to solve the plethoric answers problem. We propose two algorithms that leverage query and data properties to compute MFIS and XSS. We show experimentally that our algorithms clearly outperform a baseline method on generated queries as well as real user-submitted queries.

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Completeness, Recall, and Negation in Open-world Knowledge Bases: A Survey

Razniewski,

Arnaout,

Ghosh

et al. 2024

ACM Comput. Surv.

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General-purpose knowledge bases (KBs) are a cornerstone of knowledge-centric AI. Many of them are constructed pragmatically from web sources, and are thus far from complete. This poses challenges for the consumption as well as the curation of their content. While several surveys target the problem of completing incomplete KBs, the first problem is arguably to know whether and where the KB is incomplete in the first place, and to which degree. In this survey, we discuss how knowledge about completeness, recall, and negation in KBs can be expressed, extracted, and inferred. We cover (i) the logical foundations of knowledge representation and querying under partial closed-world semantics; (ii) the estimation of this information via statistical patterns; (iii) the extraction of information about recall from KBs and text; (iv) the identification of interesting negative statements; and (v) relaxed notions of relative recall. This survey is targeted at two types of audiences: (1) practitioners who are interested in tracking KB quality, focusing extraction efforts, and building quality-aware downstream applications; and (2) data management, knowledge base and semantic web researchers who wish to understand the state of the art of knowledge bases beyond the open-world assumption. Consequently, our survey presents both fundamental methodologies and their working, and gives practice-oriented recommendations on how to choose between different approaches for a problem at hand.

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Mining Significant Maximum Cardinalities in Knowledge Bases

Cited by 3 publications

References 21 publications

A cooperative treatment of the plethoric answers problem in RDF

A cooperative treatment of the plethoric answers problem in RDF

Dealing with Plethoric Answers of SPARQL Queries

Completeness, Recall, and Negation in Open-world Knowledge Bases: A Survey

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