Graph-Query Suggestions for Knowledge Graph Exploration

Numerous large knowledge graphs, such as DBpedia, Wikidata, Yago and Freebase, have been developed in the last decade, which contain millions of facts about various entities in the world. These knowledge graphs have proven to be incredibly useful for intelligent Web search, question understanding, in-context advertising, social media mining, and biomedicine. As some researchers have pointed out, a knowledge graph is not just a graph database, but it should have a layer of conceptual knowledge, which is usually represented as a set of first-order rules. However, it is challenging to automatically extract first-order rules from large knowledge graphs. In particular, traditional models are usually unable to handle rule learning in large knowledge graphs. This chapter aims to present state-of-the-art techniques and models on learning first-order rules using representation learning. By first recalling some basics of rule learning in knowledge graphs, we will introduce useful techniques of embedding-based rule learning through major models such as RLvLR and TyRuLe, which embed paths in knowledge graphs into latent spaces for candidate rule search. Then we evaluate the rule learning efficiency and the quality of automatically learned rules by applying them in link prediction. Before the chapter is concluded, we will also discuss some future research problems in the area.

Knowledge graphs (KGs) are essential resources for many applications including Web search and question answering. As KGs are often automatically constructed, they may contain incorrect facts. Detecting them is a crucial, yet extremely expensive task. Prominent solutions detect and explain inconsistency in KGs with respect to accompanying ontologies that describe the KG domain of interest. Compared to machine learning methods they are more reliable and human-interpretable but scale poorly on large KGs. In this paper, we present a novel approach to dramatically speed up the process of detecting and explaining inconsistency in large KGs by exploiting KG abstractions that capture prominent data patterns. Though much smaller, KG abstractions preserve inconsistency and their explanations. Our experiments with large KGs (e.g., DBpedia and Yago) demonstrate the feasibility of our approach and show that it significantly outperforms the popular baseline.

Knowledge Graphs (KGs) represent heterogeneous domain knowledge on the Web and within organizations. There exist shapes constraint languages to define validating shapes to ensure the quality of the data in KGs. Existing techniques to extract validating shapes often fail to extract complete shapes, are not scalable, and are prone to produce spurious shapes. To address these shortcomings, we propose the Quality Shapes Extraction (QSE) approach to extract validating shapes in very large graphs, for which we devise both an exact and an approximate solution. QSE provides information about the reliability of shape constraints by computing their confidence and support within a KG and in doing so allows to identify shapes that are most informative and less likely to be affected by incomplete or incorrect data. To the best of our knowledge, QSE is the first approach to extract a complete set of validating shapes from WikiData. Moreover, QSE provides a 12x reduction in extraction time compared to existing approaches, while managing to filter out up to 93% of the invalid and spurious shapes, resulting in a reduction of up to 2 orders of magnitude in the number of constraints presented to the user, e.g., from 11,916 to 809 on DBpedia.

Scheduling satellite tasks requires intricate coordination of numerous interconnected activities, posing challenges for ground station operators who struggle to decipher autonomous decision-making systems without explanatory responses. This paper advocates for explainable artificial intelligence to address this gap, leveraging large language models and knowledge graphs to enhance the transparency of scheduling logic. The study explores how operators can retrieve relevant information through natural language queries by integrating language processing with knowledge graphs as core data structures. This approach allows for manual access to knowledge graphs, supplemented with textual explanations, and enables operators to explore different scheduling scenarios, thus improving system robustness and adaptability. Additionally, this paper analyzes how current knowledge graphs and natural language processing techniques are utilized and how they can enhance explainability in satellite scheduling. It investigates the modeling of satellite schedules and environmental data within the knowledge graph. The study introduces a novel approach for generating categorized queries, executable code for knowledge graph data extraction via language modeling, and providing explanatory answers. These are evaluated for correctness, validity, and linguistic quality, demonstrating the effectiveness of this approach.

Smart city systems are increasingly built with data analytics and machine learning techniques, basing on massive data sets. They have a heavy impact on human behavior and quality of life, and thus need to deliver a controllable and sufficiently transparent experience for the users.The aim of my work is to make smart city systems more interoperable and explainable, involving data visualization and communication techniques, sensor data processing, as well as the associated intelligent data value chain production and consumption. Here, the data and the information are shared employing Knowledge Graphs, that are becoming a key enabler for large-scale processing of massive collections of interrelated facts. Examples include the Google Knowledge Graph with dozens of billion facts, dataCommons, DBPedia, YAGO, and Knowledge Vault, a very large scale probabilistic knowledge graph created with information extraction methods for unstructured or semi-structured information. Specifically, Knowledge Graphs provide the means of development of the newest methods for data management, data fusion, data merging, and graph and network optimization and modeling, serving as a source of high quality data and a base for information integration.In particular, Knowledge Graphs help to infer new relationships out of existing facts, giving context and meaning to the content, and can be used in applications. For example, the data generated by a computer vision system could be semantically represented and shared across numerous systems, taking into account the needs and requirements of these systems, as well as the context, provenance, licensing and consent aspects of the generated data. I demonstrate Knowledge Graphs-based methods in advanced smart city applications from the domains such as automation and construction of buildings, energy efficiency, tourism, transport.

Learning structured embeddings of knowledge graphs with generative adversarial framework

While there has been a trend in the last decades for publishing large-scale and highly-interconnected Knowledge Graphs (KGs), their users often get overwhelmed by the task of understanding their content as a result of their size and complexity. Data profiling approaches have been proposed to summarize large KGs into concise and meaningful representations, so that they can be better explored, processed, and managed. Profiles based on schema patterns represent each triple in a KG with its schema-level counterpart, thus covering the entire KG with profiles of considerable size. In this paper, we provide empirical evidence that profiles based on schema patterns, if explored with suitable mechanisms, can be useful to help users understand the content of big and complex KGs. ABSTAT provides concise pattern-based profiles and comes with faceted interfaces for profile exploration. Using this tool we present a user study based on query completion tasks. We demonstrate that users who look at ABSTAT profiles formulate their queries better and faster than users browsing the ontology of the KGs. The latter is a pretty strong baseline considering that many KGs do not even come with a specific ontology to be explored by the users. To the best of our knowledge, this is the first attempt to investigate the impact of profiling techniques on tasks related to knowledge graph understanding with a user study.

Top-[formula omitted] star queries on knowledge graphs through semantic-aware bounding match scores

Currently knowledge graph has been widely applied to various fields. Although the data scale is large, there is still a lot of useful but implicit information in it. Thus, a powerful reasoning system is required to derive these data. However, current reasoning systems cannot accomplish this task very well. On the one hand, stand-alone reasoning systems cannot meet the demand of large data. On the other hand, the reasoning ability of existing distributed reasoning systems is limited because of the lack of expressive inference rules. In this paper, we propose and implement a distributed reasoning system KGRL for knowledge graph based on OWL2 RL. It has a more powerful reasoning ability due to more expressive rules. It also supports optimization for redundant data. Besides, a rule-based algorithm is designed to find the inconsistent data. Experimental results show that KGRL can derive more implicit information efficiently compared to other reasoning systems. Moreover, KGRL is capable of eliminating redundant data, which can reduce the storage of knowledge graph by an average of 42%. Finally, KGRL also performs well for the detection of inconsistencies in knowledge graph.

The prosperous development of cognition and human-level intelligence have made the creation of large-scale domain knowledge graphs (KGs) a hot research field. Entity alignment (EA), which discovers entity pairs that represent the same real object in different KGs, is the key technology for building large scale knowledge graph. Existing EA methods rely mainly on the vector representation of the entity and ignoring the semantic features of an entity in a KG, result in poor alignment quality. To address this limitation, we propose a new EA framework based on knowledge embeddings (KEs) and type matching constraints. By embedding two KGs into a unified vector space, we combine the similarity of the entity vector and the degree of the entity type matching to perform more accurate cross-KG EA. The experimental results show that our proposed method has very good performance on both cross-KG and cross-language data sets. On the DBP-YG dataset, in terms of Hit@1, Hits@10, and MRR, our methods have an average increase of 27% over the baseline method MTransE. In most cases, our method significantly improves the accuracy of EA compared with state-of-the-art methods.

Relational graph neural networks have garnered particular attention to encode graph context in knowledge graphs (KGs). Although they achieved competitive performance on small KGs, how to efficiently and effectively utilize graph context for large KGs remains an open problem. To this end, we propose the Relation-based Embedding Propagation (REP) method. It is a post-processing technique to adapt pre-trained KG embeddings with graph context. As relations in KGs are directional, we model the incoming head context and the outgoing tail context separately. Accordingly, we design relational context functions with no external parameters. Besides, we use averaging to aggregate context information, making REP more computation-efficient. We theoretically prove that such designs can avoid information distortion during propagation. Extensive experiments also demonstrate that REP has significant scalability while improving or maintaining prediction quality. Particularly, it averagely brings about 10% relative improvement to triplet-based embedding methods on OGBL-WikiKG2 and takes 5%-83% time to achieve comparable results as the state-of-the-art GC-OTE.

Semantic technologies are a key enabler for Knowledge 4.0. Specifically, knowledge graphs have caused significant practical implications for managing knowledge in the digital economy. While most semantic technologies originate from the vision of representing the existing Web in a machine-processable format, it’s most notable success so far are large cross-domain knowledge graphs. They are created by collaborative human modelling and linking of structured and semi-structured data. So far, they exhibit only little but still very powerful semantics, which have shown benefits for numerous applications. This chapter introduces the latest innovations in modelling knowledge using knowledge graphs and explains how those knowledge graphs enable value creation by making unstructured content, like text documents accessible by machines and humans. Finally, we show how semantic technologies help to make hard- and software components in cyber physical systems interoperable.

Inferring missing links in knowledge graphs (KG) has attracted a lot of attention from the research community. In this paper, we tackle a practical query answering task involving predicting the relation of a given entity pair. We frame this prediction problem as an inference problem in a probabilistic graphical model and aim at resolving it from a variational inference perspective. In order to model the relation between the query entity pair, we assume that there exists an underlying latent variable (paths connecting two nodes) in the KG, which carries the equivalent semantics of their relations. However, due to the intractability of connections in large KGs, we propose to use variation inference to maximize the evidence lower bound. More specifically, our framework (Diva) is composed of three modules, i.e. a posterior approximator, a prior (path finder), and a likelihood (path reasoner). By using variational inference, we are able to incorporate them closely into a unified architecture and jointly optimize them to perform KG reasoning. With active interactions among these sub-modules, Diva is better at handling noise and coping with more complex reasoning scenarios. In order to evaluate our method, we conduct the experiment of the link prediction task on multiple datasets and achieve state-of-the-art performances on both datasets.

Knowledge graphs (KGs) have become ubiquitous publicly available knowledge sources, and are nowadays covering an ever increasing array of domains. However, not all knowledge represented is useful or pertaining when considering a new application or specific task. Also, due to their increasing size, handling large KGs in their entirety entails scalability issues. These two aspects asks for efficient methods to extract subgraphs of interest from existing KGs. To this aim, we introduce KGPrune, a Web Application that, given seed entities of interest and properties to traverse, extracts their neighboring subgraphs from Wikidata. To avoid topical drift, KGPrune relies on a frugal pruning algorithm based on analogical reasoning to only keep relevant neighbors while pruning irrelevant ones. The interest of KGPrune is illustrated by two concrete applications, namely, bootstrapping an enterprise KG and extracting knowledge related to looted artworks.

Triple Pattern Fragments (TPFs) are a novel interface for accessing data in knowledge graphs on the web. So far, work on performance evaluation and optimization has focused mainly on SPARQL query execution over TPF servers. However, in order to devise querying techniques that efficiently access large knowledge graphs via TPFs, we need to identify and understand the variables that influence the performance of TPF servers on a fine-grained level. In this work, we assess the performance of TPFs by measuring the response time for different requests and analyze how the requests’ properties, as well as the TPF server configuration, may impact the performance. For this purpose, we developed the Triple Pattern Fragment Profiler to determine the performance of TPF server. The resource is openly available at https://doi.org/10.5281/zenodo.1211621. To this end, we conduct an empirical study over four large knowledge graphs in different server environments and configurations. As part of our analysis, we provide an extensive evaluation of the results and focus on the impact of the variables: triple pattern type, answer cardinality, page size, backend and the environment type on the response time. The results suggest that all variables impact on the measured response time and allow for deriving suggestions for TPF server configurations and query optimization.