Mining Cardinalities from Knowledge Bases

Muñoz, Emir; Nickles, Matthias

doi:10.1007/978-3-319-64468-4_34

Cited by 10 publications

(11 citation statements)

References 27 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 [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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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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“…Muñoz and Nickles [22] considered both minimum and maximum cardinality restrictions. Their approach is based on retrieving the necessary data by posing very complex SPARQL queries.…”

Section: Related Workmentioning

confidence: 99%

“…To the best of our knowledge, this is the first work to consider more elaborate approach for computing cardinalities in cardinality restrictions. In particular, comparing to [22], our approach does not try to avoid or remove outliers, which may be tricky to do in a general case, but rather includes them in estimation and relies on the robustness of the used method instead.…”

Section: Related Workmentioning

confidence: 99%

Mining Cardinality Restrictions in OWL

Potoniec

2020

Foundations of Computing and Decision Sciences

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We present an approach to mine cardinality restriction axioms from an existing knowledge graph, in order to extend an ontology describing the graph. We compare frequency estimation with kernel density estimation as approaches to obtain the cardinalities in restrictions. We also propose numerous strategies for filtering obtained axioms in order to make them more available for the ontology engineer. We report the results of experimental evaluation on DBpedia 2016-10 and show that using kernel density estimation to compute the cardinalities in cardinality restrictions yields more robust results that using frequency estimation. We also show that while filtering is of limited usability for minimum cardinality restrictions, it is much more important for maximum cardinality restrictions. The presented findings can be used to extend existing ontology engineering tools in order to support ontology construction and enable more efficient creation of knowledge-intensive artificial intelligence systems.

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“…For instance, we want to encode prior knowledge in the form of cardinality statements such as "a person should have at most two parents" or "a patient should be taking between 1 and 5 drugs at a time" in neural link prediction models. Such prior knowledge can be provided by domain experts, or automatically extracted from data [11,24]. It is expected that such cardinality constraints will be satisfied by both the facts in the knowledge graph and algorithms analysing the graph, such as link predictors.…”

Section: Triples Probabilitymentioning

confidence: 99%

“…We mine the relation cardinality constraints from the training set of each dataset, following the algorithm proposed by Muñoz and Nickles [24] using the normalisation option but without filtering outliers. Table 4 gives examples of the cardinality constraints mined from each dataset.…”

Section: Datasetsmentioning

confidence: 99%

Embedding cardinality constraints in neural link predictors

Muñoz

Minervini

Nickles

2019

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

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Neural link predictors learn distributed representations of entities and relations in a knowledge graph. They are remarkably powerful in the link prediction and knowledge base completion tasks, mainly due to the learned representations that capture important statistical dependencies in the data. Recent works in the area have focused on either designing new scoring functions or incorporating extra information into the learning process to improve the representations. Yet the representations are mostly learned from the observed links between entities, ignoring commonsense or schema knowledge associated to the relations in the graph. A fundamental aspect of the topology of relational data is the cardinality information, which bounds the number of predictions given for a relation between a minimum and maximum frequency. In this paper, we propose a new regularisation approach to incorporate relation cardinality constraints to any existing neural link predictor without affecting their efficiency or scalability. Our regularisation term aims to impose boundaries on the number of predictions with high probability, thus, structuring the embeddings space to respect commonsense cardinality assumptions resulting in better representations. Experimental results on Freebase, WordNet and YAGO show that, given suitable prior knowledge, the proposed method positively impacts the predictive accuracy of downstream link prediction tasks.

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Mining Cardinalities from Knowledge Bases

Cited by 10 publications

References 27 publications

Dealing with Plethoric Answers of SPARQL Queries

Dealing with Plethoric Answers of SPARQL Queries

Mining Cardinality Restrictions in OWL

Embedding cardinality constraints in neural link predictors

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