Neuro-Symbolic Query Optimization in Knowledge Graphs

This chapter delves into the emerging field of neuro-symbolic query optimization for knowledge graphs (KGs), presenting a comprehensive exploration of how neural and symbolic techniques can be integrated to enhance query processing. Traditional query optimizers in knowledge graphs rely heavily on symbolic methods, utilizing dataset summaries, statistics, and cost models to select efficient execution plans. However, these approaches often suffer from misestimations and inaccuracies, particularly when dealing with complex queries or large-scale datasets. Recent advancements have introduced neural models, which capture non-linear aspects of query optimization, offering promising alternatives to purely symbolic methods. In this chapter, we introduce neuro-symbolic query optimizers, a novel approach that combines the strengths of symbolic reasoning with the adaptability of neural computation. We discuss the architecture of these hybrid systems, highlighting the interplay between neural and symbolic components to improve the optimizer's ability to navigate the search space and produce efficient execution plans. Additionally, the chapter reviews existing neural components tailored for optimizing queries over knowledge graphs and examines the limitations and challenges in deploying neuro-symbolic query optimizers in real-world environments.

Towards Semantically Enriched Embeddings for Knowledge Graph Completion

Embedding based Knowledge Graph (KG) Completion has gained much attention over the past few years. Most of the current algorithms consider a KG as a multidirectional labeled graph and lack the ability to capture the semantics underlying the schematic information. In a separate development, a vast amount of information has been captured within the Large Language Models (LLMs) which has revolutionized the field of Artificial Intelligence. KGs could benefit from these LLMs and vice versa. This vision paper discusses the existing algorithms for KG completion based on the variations for generating KG embeddings. It starts with discussing various KG completion algorithms such as transductive and inductive link prediction and entity type prediction algorithms. It then moves on to the algorithms utilizing type information within the KGs, LLMs, and finally to algorithms capturing the semantics represented in different description logic axioms. We conclude the paper with a critical reflection on the current state of work in the community and give recommendations for future directions.

Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks

Cardinality Estimation over Knowledge Graphs (KG) is crucial for query optimization, yet remains a challenging task due to the semi-structured nature and complex correlations of typical Knowledge Graphs. In this work, we propose GNCE, a novel approach that leverages knowledge graph embeddings and Graph Neural Networks (GNN) to accurately predict the cardinality of conjunctive queries. GNCE first creates semantically meaningful embeddings for all entities in the KG, which are then integrated into the given query, which is processed by a GNN to estimate the cardinality of the query. We evaluate GNCE on several KGs in terms of q-Error and demonstrate that it outperforms state-of-the-art approaches based on sampling, summaries, and (machine) learning in terms of estimation accuracy while also having lower execution time and less parameters. Additionally, we show that GNCE can inductively generalise to unseen entities, making it suitable for use in dynamic query processing scenarios. Our proposed approach has the potential to significantly improve query optimization and related applications that rely on accurate cardinality estimates of conjunctive queries.

On the Applicability of Synthetic Data for Re-Identification

This contribution demonstrates the feasibility of applying Generative Adversarial Networks (GANs) on images of EPAL pallet blocks for dataset enhancement in the context of re-identification. For many industrial applications of re-identification methods, datasets of sufficient volume would otherwise be unattainable in non-laboratory settings. Using a state-of-the-art GAN architecture, namely CycleGAN, images of pallet blocks rotated to their left-hand side were generated from images of visually centered pallet blocks, based on images of rotated pallet blocks that were recorded as part of a previously recorded and published dataset. In this process, the unique chipwood pattern of the pallet block surface structure was retained, only changing the orientation of the pallet block itself. By doing so, synthetic data for re-identification testing and training purposes was generated, in a manner that is distinct from ordinary data augmentation. In total, 1,004 new images of pallet blocks were generated. The quality of the generated images was gauged using a perspective classifier that was trained on the original images and then applied to the synthetic ones, comparing the accuracy between the two sets of images. The classification accuracy was 98% for the original images and 92% for the synthetic images. In addition, the generated images were also used in a re-identification task, in order to re-identify original images based on synthetic ones. The accuracy in this scenario was up to 88% for synthetic images, compared to 96% for original images. Through this evaluation, it is established, whether or not a generated pallet block image closely resembles its original counterpart.

TokTrack: A Complete Token Provenance and Change Tracking Dataset for the English Wikipedia

We present a dataset that contains every instance of all tokens (~ words) ever written in undeleted, non-redirect English Wikipedia articles until October 2016, in total 13,545,349,787 instances. Each token is annotated with (i) the article revision it was originally created in, and (ii) lists with all the revisions in which the token was ever deleted and (potentially) re-added and re-deleted from its article, enabling a complete and straightforward tracking of its history. This data would be exceedingly hard to create by an average potential user as it is (i) very expensive to compute and as (ii) accurately tracking the history of each token in revisioned documents is a non-trivial task. Adapting a state-of-the-art algorithm, we have produced a dataset that allows for a range of analyses and metrics, already popular in research and going beyond, to be generated on complete-Wikipedia scale; ensuring quality and allowing researchers to forego expensive text-comparison computation, which so far has hindered scalable usage. We show how this data enables, on token-level, computation of provenance, measuring survival of content over time, very detailed conflict metrics, and fine-grained interactions of editors like partial reverts, re-additions and other metrics, in the process gaining several novel insights.