In this paper we introduce the notion of Demand-Weighted Completeness, allowing estimation of the completeness of a knowledge base with respect to how it is used. Defining an entity by its classes, we employ usage data to predict the distribution over relations for that entity. For example, instances of person in a knowledge base may require a birth date, name and nationality to be considered complete. These predicted relation distributions enable detection of important gaps in the knowledge base, and define the required facts for unseen entities. Such characterisation of the knowledge base can also quantify how usage and completeness change over time. We demonstrate a method to measure Demand-Weighted Completeness, and show that a simple neural network model performs well at this prediction task.
Previous work has studied the completeness of the individual properties or database tables over which queries are executed @cite_14 @cite_11 . This approach is suitable for KBs or use cases where individual tables, and individual rows in those tables, are all of equal importance to the KB, or are queried separately.
@cite_14 @cite_11
2189547572 2018592576
Data completeness is an important aspect of data quality as in many scenarios it is crucial to guarantee completeness of query answers. We develop techniques to conclude the completeness of query answers from information about the completeness of parts of a generally incomplete database. In our framework, completeness of a database can be described in two ways: by table completeness (TC) statements, which say that certain parts of a relation are complete, and by query completeness (QC) statements, which say that the set of answers of a query is complete. We identify as core problem to decide whether table completeness entails query completeness (TC-QC). We develop decision procedures and assess the complexity of TC-QC inferences depending on the languages of the TC and QC statements. We show that in important cases weakest preconditions for query completeness can be expressed in terms of table completeness statements, which means that these statements identify precisely the parts of a database that are critical for the completeness of a query. For the related problem of QC-QC entailment, we discuss its connection to query determinacy. Moreover, we show how to use the concrete state of a database to enable further completeness inferences. In many applications including loosely coupled cloud databases, collaborative editing and network monitoring, data from multiple sources is regularly used for query answering. For reasons such as system failures, insufficient author knowledge or network issues, data may be temporarily unavailable or generally nonexistent. Hence, not all data needed for query answering may be available. In this paper, we propose a natural class of completeness patterns, expressed by selections on database tables, to specify complete parts of database tables. We then show how to adapt the operators of relational algebra so that they manipulate these completeness patterns to compute completeness patterns pertaining to query answers. Our proposed algebra is computationally sound and complete with respect to the information that the patterns provide. We show that stronger completeness patterns can be obtained by considering not only the schema but also the database instance and we extend the algebra to take into account this additional information. We develop novel techniques to efficiently implement the computation of completeness patterns on query answers and demonstrate their scalability on real data.