Generating Rules to Filter Candidate Triples for their Correctness Checking by Knowledge Graph Completion Techniques

Abstract

Knowledge Graphs (KGs) contain large amounts of structured information. Due to their inherent incompleteness, a process known as KG completion is often carried out to find the missing triples in a KG, usually by training a fact checking model that is able to discern between correct and incorrect knowledge. After the fact checking model has been trained and evaluated, it has to be applied to a set of candidate triples, and those that are considered correct are added to the KG as new knowledge. However, this process needs a set of candidate triples of a reasonable size that represents possible new knowledge, in order to be evaluated by the fact checking task and, if considered to be correct, added to the KG, enriching it. Current approaches for selecting candidate triples for their correctness checking either use the full set possible missing candidate triples (and thus provide no filtering) or apply very basic rules to filter out unlikely candidates, which may have a negative effect on the completion performance as very few candidate triples are filtered out. In this paper we present CHAI, a method for producing more complex rules that are able to filter candidate triples by combining a set of criteria to optimize a fitness function. Our experiments show that CHAI is able to generate rules that, when applied, yield smaller candidate sets than similar proposals while still including promising candidate triples. •

Reliability-Based Aggregation of Heterogeneous Knowledge to Assist Operators in Manufacturing

“…Also, it relates to the problem of knowledge graph completion, as candidate triples have to be checked whether to be included in the knowledge graph or not. Consequently, work done on filtering candidate triples by Borrego et al [9] is conceptually related. However, the main difference is that in knowledge graph completion candidate triples are usually generated based on information contained in the knowledge graph, while in our case they are provided by process data and operator information gained in a manufacturing process.…”

The Impact of Negative Triple Generation Strategies and Anomalies on Knowledge Graph Completion

“…Borrego et al [6] propose CHAI to automatically discover a set of rules to discard triples that are likely to be negative during testing. The discovery process consists of, for a given predicate, combining a set of instance-level criteria to optimize a fitness function that learns disjunctive rules.…”

“…Completion algorithms aim to automatically identify missing triples to be added to an existing graph [11]. To accomplish their goal, these algorithms typically train a model that takes candidate triples as input and outputs whether they are correct (positive) and should be added to the graph, or incorrect (negative) and should be discarded [6]. The knowledge graph at hand must thus be divided into a training, a validation (optionally) and a test splits (subgraphs) [2].…”

Towards the smart use of embedding and instance features for property matching

“…Such knowledge graphs (KG) physically integrate numerous entities with their properties (attributes) and relationships as well as associated metadata about entity types and relationship types in a graph-like structure [28]. Many companies (including Google, Facebook, and Amazon) are increasingly relying on the integrated and curated information in knowledge graphs and there is also an increasing amount of research on KG creation [2], [6], [10], [23], [31], [33], [35], [37] and KG exploitation, e.g. for question answering [16], [38].…”