Query Graph Research Articles

Outlining and Filling: Hierarchical Query Graph Generation for Answering Complex Questions Over Knowledge Graphs

Query graph construction aims to construct the correct executable SPARQL on the KG to answer natural language questions. Although recent methods have achieved good results using neural network-based query graph ranking, they suffer from three new challenges when handling more complex questions: 1) complicated SPARQL syntax, 2) huge search space, and 3) locally ambiguous query graphs. In this paper, we provide a new solution. As a preparation, we extend the query graph by treating each SPARQL clause as a subgraph consisting of vertices and edges and define a unified graph grammar called AQG to describe the structure of query graphs. Based on these concepts, we propose a novel end-to-end model that performs hierarchical autoregressive decoding to generate query graphs. The high-level decoding generates an AQG as a constraint to prune the search space and reduce the locally ambiguous query graph. The bottom-level decoding accomplishes the query graph construction by selecting appropriate instances from the preprepared candidates to fill the slots in the AQG. The experimental results show that our method greatly improves the SOTA performance on complex KGQA benchmarks. Equipped with pre-trained models, the performance of our method is further improved, achieving SOTA for all three datasets used.

Lynx: A Graph Query Framework for Multiple Heterogeneous Data Sources

Graph model are increasingly popular among modern applications for its ability to model complex relationships between entities. Users tend to query the data as a graph with graph operations (e.g., graph navigation and exploration). However, a large fraction of the data resides in relational databases or other storage systems. Challenges arise in uniformly querying multiple heterogeneous data sources as a graph. Traditional solutions are limited by time-consuming data integration, expensive development effort, and incomplete query requirements. Thus, we developed Lynx, a general graph query framework, to simplify querying graph data by converting complex statements into basic graph operations. Instead of connecting directly to the data sources, Lynx retrieves data through user-implemented interfaces for those graph operations. We demonstrate Lynx's capabilities through real-world scenarios, showcasing Lynx's ability to process graph queries on multiple heterogeneous data sources and also to be used as a generic graph query engine development framework.

VisualNeo: Bridging the Gap between Visual Query Interfaces and Graph Query Engines

Visual Graph Query Interfaces (VQIs) empower non-programmers to query graph data by constructing visual queries intuitively. Devising efficient technologies in Graph Query Engines (GQEs) for interactive search and exploration has also been studied for years. However, these two vibrant scientific fields are traditionally independent of each other, causing a vast barrier for users who wish to explore the full-stack operations of graph querying. In this demonstration, we propose a novel VQI system built upon Neo4j called VisualNeo that facilities an efficient subgraph query in large graph databases. VisualNeo inherits several advanced features from recent advanced VQIs, which include the data-driven gui design and canned pattern generation. Additionally, it embodies a database manager module in order that users can connect to generic Neo4j databases. It performs query processing through the Neo4j driver and provides an aesthetic query result exploration.

Visual Analytics of Multidimensional Oral Health Surveys: Data Mining Study.

Oral health surveys largely facilitate the prevention and treatment of oral diseases as well as the awareness of population health status. As oral health is always surveyed from a variety of perspectives, it is a difficult and complicated task to gain insights from multidimensional oral health surveys. We aimed to develop a visualization framework for the visual analytics and deep mining of multidimensional oral health surveys. First, diseases and groups were embedded into data portraits based on their multidimensional attributes. Subsequently, group classification and correlation pattern extraction were conducted to explore the correlation features among diseases, behaviors, symptoms, and cognitions. On the basis of the feature mining of diseases, groups, behaviors, and their attributes, a knowledge graph was constructed to reveal semantic information, integrate the graph query function, and describe the features of intrigue to users. A visualization framework was implemented for the exploration of multidimensional oral health surveys. A series of user-friendly interactions were integrated to propose a visual analysis system that can help users further achieve the regulations of oral health conditions. A visualization framework is provided in this paper with a set of meaningful user interactions integrated, enabling users to intuitively understand the oral health situation and conduct in-depth data exploration and analysis. Case studies based on real-world data sets demonstrate the effectiveness of our system in the exploration of oral diseases.

Brain-Inspired Search Engine Assistant Based on Knowledge Graph.

Search engines can quickly respond to a hyperlink list according to query keywords. However, when a query is complex, developers need to repeatedly refine search keywords and open a large number of web pages to find and summarize answers. Many research works of question and answering (Q&A) system attempt to assist search engines by providing simple, accurate, and understandable answers. However, without original semantic contexts, these answers lack explainability, making them difficult for users to trust and adopt. In this article, a brain-inspired search engine assistant named DeveloperBot based on knowledge graph is proposed, which aligns to the cognitive process of humans and has the capacity to answer complex queries with explainability. Specifically, DeveloperBot first constructs a multilayer query graph by splitting a complex multiconstraint query into several ordered constraints. Then, it models a constraint reasoning process as a subgraph search process inspired by a spreading activation model of cognitive science. In the end, novel features of the subgraph are extracted for decision-making. The corresponding reasoning subgraph and answer confidence are derived as explanations. The results of the decision-making demonstrate that DeveloperBot can estimate answers and answer confidences with high accuracy. We implement a prototype and conduct a user study to evaluate whether and how the direct answers and the explanations provided by DeveloperBot can assist developers' information needs.

Full-Power Graph Querying: State of the Art and Challenges

Graph databases are enjoying enormous popularity, through both their RDF and Property Graphs (PG) incarnations, in a variety of applications. To query graphs, query languages provide structured, as well as unstructured primitives. While structured queries allow expressing precise information needs, they are unsuited for exploring unfamiliar datasets, as they require prior knowledge of the schema and structure of the dataset. Prior research on keyword search in graph databases do not suffer from this limitation. However, keyword queries do not allow expressing precise search criteria when users do know some.This tutorial (1.5 hours) builds a continuum between structured graph querying through languages such as SPARQL and GPML, a recently proposed standard for PG querying, on one hand, and graph keyword search, on the other hand. In this space between querying and information retrieval, we analyze the features of modern query languages that go toward unstructured search, discuss their strength, limitations, and compare their computational complexity. In particular, we focus on (i) lessons learned from the rich literature of graph keyword search, in particular with respect to result scoring; (ii) language mechanisms for integratingbothcomplex structured querying and powerful methods to search for connections users do not know in advance. We conclude by discussing the open challenges and future work directions.

An efficient approach for faster matching of approximate patterns in graphs

Graphs have proven to be an efficient problem representation scheme in many real-world applications and can serve to address mining of patterns in large volumes of data. This work addresses the data management issue in graph databases for shortest path queries, verification of reachability, and pattern matching queries. The key issue is to match all possible patterns in a large data graph that matches a user-provided pattern, known as the query graph. The large size of the graph makes the problem complex when searching for all patterns that are similar to the user’s query. For a given query, a match occurs when a graph with n vertices has the same label as the corresponding vertices in the query graph. This work addresses such pattern matching query problems on large graphs and presents an efficient algorithm to mine all patterns that satisfies the given query. The current work presents a novel Fast Graph Pattern (FGP) mining algorithm that performs a label-specific walk on a graph and stores all vertices that are visited during the walk and hashes them on a labeled root. Experiments are performed using five benchmark datasets, and the proposed approach is compared with three state-of-the-art methods. The obtained results suggest better performance of the current proposal by achieving five times quicker results.

Developing Energy Access Ontology using Protege Tool

In this internet-based world where a large amount of information exists on the World Wide Web (WWW), the information retrieval systems are mainly centred on topic-based classification. Numerous efforts and time have been consumed for relevant information extraction through major search engines if the data is not organised correctly. Ontologies proved to be an effective technique for representing and retrieving information, which is the key idea in semantic web applications. Ontologies not only help in efficient knowledge representation and information retrieval, but it also helps in mapping the hidden knowledge about a subject. This paper discusses the process and method of building ontology on the “Energy access” domain. The methodology is based on the tools used in developing the ontology. Several tools are used to create an ontology. Protégé is a most popular tool for ontology editing and for developing ontology.1 In this paper, various aspects like super class and subclass hierarchy, creating a subclass, instances for class illustration, query retrieval process, visualisation, and graph views have been demonstrated by using protégé software.

A Hybrid Virtual Assistant for Legal Domain Based on Information Retrieval and Knowledge Graphs

Virtual assistants have gained popularity across various domains, including the legal field, where they serve to offer guidance and aid in the form of law retrieval. In this research, our aim is to develop a legal virtual assistant that combines knowledge graphs (KGs) and information retrieval (IR) techniques. This hybrid approach allows us to provide accurate answers extracted from structured interconnected data while simultaneously cater to a diverse range of legal inquiries. We categorize these inquiries into a few distinct use cases: definition lookup, law component lookup, sanctions, and domain knowledge. Our system encompasses a chatbot platform, knowledge graph querying, and information retrieval. Specifically, we construct a VA system over a legal knowledge graph pertaining to the Indonesian Act concerning Manpower or Labor (UU Ketenagakerjaan) and the Indonesian Act concerning the Creation of Jobs (UU Cipta Kerja). This marks the creation of the first legal virtual assistant in the Indonesian context that combines KG and IR methodologies. To evaluate the effectiveness of our prototype system, we conduct tests using a variety of labor law-related questions, ranging in difficulty. The integration of knowledge graphs and information retrieval proves to significantly improve the support provided for a wide range of potential applications in the legal field.

PH-CF: A Phased Hybrid Algorithm for Accelerating Subgraph Matching Based on CPU-FPGA Heterogeneous Platform

Nowadays, more and more data are represented and stored by a graph structure, and subgraph matching is a fundamental problem in a variety of scientific machine learning and industrial applications, such as remote sensing image registration, industrial inspection, etc. Due to the NP-hard problem of subgraph matching, the explosive growth of graph data, the disadvantages of high energy consumption, and the high overhead of CPU and GPU platforms, computing subgraph matching is becoming more and more challenging. To alleviate this problem, we propose a phased hybrid algorithm to accelerate the enumeration task of subgraph matching, called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PH-CF</monospace> , based on the CPU-FPGA heterogeneous platform. This approach can make full use of the pipeline and data flow mechanism, low power consumption, and configurable characteristics of FPGA. First, the matching order of query vertices automatically selects GQL or RI methods according to the sparsity of the data (query) graph. Second, a candidate vertex auxiliary data structure set partitioning method is designed to effectively realize the load balance of multiple computing units at the FPGA and CPU host sides. Third, FPGA's pipeline and data flow mechanism is used to accelerate the enumeration phase of subgraph matching. Experimental results on real-world and synthetic datasets show that the performance of the <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PH-CF</monospace> outperforms the state-of-the-arts. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PH-CF</monospace> can obtain the average performance improvement of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$16.07\times$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$38.61\times$</tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$11.46\times$</tex-math></inline-formula> over CFL, CECI, and DP-iso, respectively. Moreover, our approach has good stability and robustness on various datasets.

Learning to Count Isomorphisms with Graph Neural Networks

Subgraph isomorphism counting is an important problem on graphs, as many graph-based tasks exploit recurring subgraph patterns. Classical methods usually boil down to a backtracking framework that needs to navigate a huge search space with prohibitive computational cost. Some recent studies resort to graph neural networks (GNNs) to learn a low-dimensional representation for both the query and input graphs, in order to predict the number of subgraph isomorphisms on the input graph. However, typical GNNs employ a node-centric message passing scheme that receives and aggregates messages on nodes, which is inadequate in complex structure matching for isomorphism counting. Moreover, on an input graph, the space of possible query graphs is enormous, and different parts of the input graph will be triggered to match different queries. Thus, expecting a fixed representation of the input graph to match diversely structured query graphs is unrealistic. In this paper, we propose a novel GNN called Count-GNN for subgraph isomorphism counting, to deal with the above challenges. At the edge level, given that an edge is an atomic unit of encoding graph structures, we propose an edge-centric message passing scheme, where messages on edges are propagated and aggregated based on the edge adjacency to preserve fine-grained structural information. At the graph level, we modulate the input graph representation conditioned on the query, so that the input graph can be adapted to each query individually to improve their matching. Finally, we conduct extensive experiments on a number of benchmark datasets to demonstrate the superior performance of Count-GNN.

Reconnaissance Techniques and Industrial Control System Tactics Knowledge Graph

In the initial stages of industrial control system (ICS) penetration testing, pentesters conduct reconnaissance by using various tools including Nmap, Shodan, Maltego, Google, Google Hacking Database (GHDB), Recon-ng and more. Testers use various reconnaissance techniques (RTs) within the tools to directly access ICS devices. Many novice ICS-pentesters stop their reconnaissance work upon successfully accessing an ICS device. However, continuing to conduct reconnaissance after initial access can lead to pentesters finding even more information to find more ICS devices, ICS networks, and ways to make ICS exploitation more effective. Our research motivation stems from finding ways to explicitly model the continuation of using RTs once an ICS device is accessed. Knowledge graphs offer an approach for linking RTs together and creating chains of RTs.&#x0D; &#x0D; MITRE ATT&amp;CK ICS provides a matrix of ICS adversarial behaviours. The matrix consists of main exploit tactics and techniques used to accomplish these tactics. Example techniques include ICS alarm suppression, blocking command messages, starting a device, and stopping services. ATT&amp;CK ICS also provides ICS data sources that defenders use to detect the adversarial techniques. Application logs, files, logon sessions, network traffic, and operational databases represent some of the ICS data sources. We reasoned that if adversaries could find the ICS data sources and discover the ability to modify the data sources, then adversaries could cover their tracks to successfully carry out ICS tactics. For example, ICS attackers could modify log entries to hide the attacker’s steps or ICS attackers could delete alarm notifications that showed that ICS attackers changed ICS settings.&#x0D; &#x0D; In this work in progress research, we used knowledge-graph modelling techniques to link together RTs with ICS data sources, the ability to modify the data sources, the ability to then cover tracks of ICS techniques, and the impact of techniques on accomplishing ICS tactics. We named the graph RT-ICS Graph. With knowledge graph queries and shortest-path algorithms run over the RT-ICS graph, we showed how RTs can explicitly lead to impacts on adversaries carrying out ICS tactics. The accomplishment of ICS tactics can cause severe damage or harm.

Graph Convolution Network over Dependency Structure Improve Knowledge Base Question Answering

Knowledge base question answering (KBQA) can be divided into two types according to the type of complexity: questions with constraints and questions with multiple hops of relationships. Previous work on knowledge base question answering have mostly focused on entities and relations. In a multihop question, it is insufficient to focus solely on topic entities and their relations since the relation between words also contains some important information. In addition, because the question contains constraints or multiple relationships, the information is difficult to capture, or the constraints are missed. In this paper, we applied a dependency structure to questions that capture relation information (e.g., constraint) between the words in question through a graph convolution network. The captured relation information is integrated into the question for re-encoding, and the information is used to generate and rank query graphs. Compared with existing sequence models and query graph generation models, our approach achieves a 0.8–3% improvement on two benchmark datasets.

Using knowledge graphs to infer gene expression in plants.

Climate change is already affecting ecosystems around the world and forcing us to adapt to meet societal needs. The speed with which climate change is progressing necessitates a massive scaling up of the number of species with understood genotype-environment-phenotype (G×E×P) dynamics in order to increase ecosystem and agriculture resilience. An important part of predicting phenotype is understanding the complex gene regulatory networks present in organisms. Previous work has demonstrated that knowledge about one species can be applied to another using ontologically-supported knowledge bases that exploit homologous structures and homologous genes. These types of structures that can apply knowledge about one species to another have the potential to enable the massive scaling up that is needed through in silico experimentation. We developed one such structure, a knowledge graph (KG) using information from Planteome and the EMBL-EBI Expression Atlas that connects gene expression, molecular interactions, functions, and pathways to homology-based gene annotations. Our preliminary analysis uses data from gene expression studies in Arabidopsis thaliana and Populus trichocarpa plants exposed to drought conditions. A graph query identified 16 pairs of homologous genes in these two taxa, some of which show opposite patterns of gene expression in response to drought. As expected, analysis of the upstream cis-regulatory region of these genes revealed that homologs with similar expression behavior had conserved cis-regulatory regions and potential interaction with similar trans-elements, unlike homologs that changed their expression in opposite ways. This suggests that even though the homologous pairs share common ancestry and functional roles, predicting expression and phenotype through homology inference needs careful consideration of integrating cis and trans-regulatory components in the curated and inferred knowledge graph.

PG-Schema: Schemas for Property Graphs

Property graphs have reached a high level of maturity, witnessed by multiple robust graph database systems as well as the ongoing ISO standardization effort aiming at creating a new standard Graph Query Language (GQL). Yet, despite documented demand, schema support is limited both in existing systems and in the first version of the GQL Standard. It is anticipated that the second version of the GQL Standard will include a rich DDL. Aiming to inspire the development of GQL and enhance the capabilities of graph database systems, we propose PG-Schema, a simple yet powerful formalism for specifying property graph schemas. It features PG-Schema with flexible type definitions supporting multi-inheritance, as well as expressive constraints based on the recently proposed PG-Keys formalism. We provide the formal syntax and semantics of PG-Schema, which meet principled design requirements grounded in contemporary property graph management scenarios, and offer a detailed comparison of its features with those of existing schema languages and graph database systems.

Theories and Principles Matter: Towards Visually Appealing and Effective Abstraction of Property Graph Queries

Existing visual abstraction of a property graph query by representing it as a labeled atomic graph (LAG) has great potential to democratize the usage of property graph databases as it enables user-friendly visual query formulation without demanding the need to learn a property graph query language e.g., Cypher. Unfortunately, existing LAG-based query interfaces do not embrace HCI principles and psychology theories to inform their design and as a result may have adverse impact on their usability and aesthetics. In this paper, we depart from the classical theory- and principles-oblivious LAG abstraction to present a novel theory-informed visual abstraction called labeled composite graph (LCG) to address this limitation. It realizes a novel and extensible visual shape definition language called VEDA to create and maintain an LCG systematically, guided by a variety of theories and principles from HCI, visualization and psychology. We build a novel LCG-based visual property graph query interface for Cypher called SIERRA and demonstrate through a user study its superiority to an industrial-strength LAG-based query interface for property graphs w.r.t. usability, aesthetics and efficient query formulation.

Efficient GPU-Accelerated Subgraph Matching

Subgraph matching is a basic operation in graph analytics, finding all occurrences of a query graph Q in a data graph G. A common approach is to first filter out non-candidate vertices in G, and then order the vertices in Q to enumerate results. Recent work has started to utilize the GPU to accelerate subgraph matching. However, the effectiveness of current GPU-based filtering and ordering methods is limited, and the result enumeration often runs out of memory quickly. To address these problems, we propose EGSM, an efficient approach to GPU-based subgraph matching. Specifically, we design a data structure Cuckoo trie to support dynamic maintenance of candidates for filtering, and order query vertices based on estimated numbers of candidate vertices on the fly. Furthermore, we perform a hybrid breadth-first and depth-first search with memory management for result enumeration. Consequently, EGSM significantly outperforms the state-of-the-art GPU-accelerated algorithms, including GSI and CuTS.

BatchHL+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{+}$$\\end{document}: batch dynamic labelling for distance queries on large-scale networks

Many real-world applications operate on dynamic graphs to perform important tasks. In this article, we study batch-dynamic algorithms that are capable of updating distance labelling efficiently in order to reflect the effects of rapid changes on such graphs. To explore the full pruning potentials, we first characterize the minimal set of vertices being affected by batch updates. Then, we reveal patterns of interactions among different updates (edge insertions and edge deletions) and leverage them to design pruning rules for reducing update search space. These interesting findings lead us to developing a new batch-dynamic method, called BatchHL+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^+$$\\end{document}, which can dynamize labelling for distance queries much more efficiently than existing work. We provide formal proofs for the correctness and minimality of BatchHL+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^+$$\\end{document} which are non-trivial and require a delicate analysis of patterns of interactions. Empirically, we have evaluated the performance of BatchHL+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^+$$\\end{document} on 15 real-world networks. The results show that BatchHL+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^+$$\\end{document} significantly outperforms the state-of-the-art methods with up to 3 orders of magnitude faster in reflecting updates of rapidly changing graphs for distance queries.

Trajectory Prediction for Automated Vehicles on Roads With Lanes Partially Covered by Ice or Snow

Snow and ice films obscure lane lines during the winter, leading to inaccurate trajectory predictions and reducing a driver's ability to guide the vehicle. The full use of observable information to continuously infer trajectories is an urgent issue in trajectory prediction tasks. In this paper, a neighboring vehicle trajectory prediction method based on graph neural networks (GNNs) is proposed, the lane line positions and attribute information in high-definition (HD) maps are extracted, and an ice and snow mask mechanism that simulates instances in which lane lines are covered is introduced. Because graph aggregation with a GNN enables information exchange between nodes, a graph query mechanism that integrates local graph information and guides prediction results based on the local states of the nearest observable nodes is proposed. In addition, a multihead attention mechanism projects neighboring states to the graph, describing interactions between vehicles and the state of the traffic flow, and a two-layer graph attention network (GAT) enables information aggregation and describes node correlations. Experiments on the nuScenes dataset show that our prediction method outperforms state-of-the-art prediction systems, including Traj++, SG-NET, and P2T, when lane lines are occluded. Vehicle trajectories on roads covered by ice and snow can be accurately predicted based on observable information.

Query cost estimation in graph databases via emphasizing query dependencies by using a neural reasoning network

SummaryWith the increasing complexity of graph queries, query cost estimation has become a key challenge in graph databases. Accurate estimation results are critical for database administrators or database management systems to perform query processing or optimization tasks. An efficient and accurate estimation model can improve the estimation quality and make the produced results credible. Although learning‐based methods have been applied in query cost estimation, most of them are directed at relational queries and cannot be directly used for graph queries. Furthermore, most estimation approaches focus on the correlations between predicates or columns. The dependencies between query schema and query filter conditions and the correlation between query schema are ignored. In this study, we construct a novel deep learning model composed of reasoning and retrieval processes that can accurately capture the potential logical relationships in graph queries. This solves the above problems to some extent. In addition, we propose a query estimation framework that divides the estimation task into query workload generation, training data collection, feature extraction and encoding, and estimation model construction. The results of the experiment on real‐world datasets show that our estimation model can improve the estimation quality and outperforms other compared deep learning models in terms of estimation accuracy.