Triple Store Research Articles

Storage and Query of Drug Knowledge Graphs Using Distributed Graph Databases: A Case Study

Background: Distributed graph databases are a promising method for storing and conducting complex pathway queries on large-scale drug knowledge graphs to support drug research. However, there is a research gap in evaluating drug knowledge graphs’ storage and query performance based on distributed graph databases. This study evaluates the feasibility and performance of distributed graph databases in managing large-scale drug knowledge graphs. Methods: First, a drug knowledge graph storage and query system is designed based on the Nebula Graph database. Second, the system’s writing and query performance is evaluated. Finally, two drug repurposing benchmarks are used to provide a more extensive and reliable assessment. Results: The performance of distributed graph databases surpasses that of single-machine databases, including data writing, regular queries, constrained queries, and concurrent queries. Additionally, the advantages of distributed graph databases in writing performance become more pronounced as the data volume increases. The query performance benefits of distributed graph databases also improve with the complexity of query tasks. The drug repurposing evaluation results show that 78.54% of the pathways are consistent with currently approved drug treatments according to repoDB. Additionally, 12 potential pathways for new drug indications are found to have literature support according to DrugRepoBank. Conclusions: The proposed system is able to construct, store, and query a large graph of multisource drug knowledge and provides reliable and explainable drug–disease paths for drug repurposing.

Scalable and High-Performance Large-Scale Dynamic Graph Storage and Processing System

Existing in-memory graph storage systems that rely on DRAM have scalability issues because of the limited capacity and volatile nature of DRAM. The emerging persistent memory (PMEM) offers us a chance to solve these issues through its larger capacity and non-volatile characteristics. However, simply adapting existing DRAM-based graph storage systems to PMEM would result in inefficient PMEM stores and accesses, including high read and write amplification to PMEM, imbalanced work division for PMEM accesses, and costly remote PMEM access across NUMA nodes. These issues severely limit the performance of large graph processing. In this paper, we aim to achieve scalable and high-performance graph processing in PMEM. We first propose an XPLine-friendly graph storage model that uses vertex-centric graph buffering, hierarchical vertex buffer managing, and in-place vertex block merging to optimize PMEM graph storage. Furthermore, we develop a scalable graph processing model that leverages multi-threaded work dividing and NUMA-friendly graph accessing to optimize PMEM graph accesses. Based on these techniques, we implement XPGraph , a PMEM-based graph storage system for large-scale evolving graphs, and several variants for different system settings. Our experiments demonstrate that XPGraph surpasses the state-of-the-art in-memory graph storage system on a PMEM-based system by 3.07 × to 4.99 × in update performance and up to 5.87 × in query performance, and performs much better in highly parallel multi-threaded scenarios.

High-Performance Graph Storage and Mutation for Graph Processing and Streaming

High-Performance Graph Storage and Mutation for Graph Processing and Streaming

A machine learning potential construction based on radial distribution function sampling.

Sampling reference data is crucial in machine learning potential (MLP) construction. Inadequate coverage of local configurations in reference data may lead to unphysical behaviors in MLP-based molecular dynamics (MLP-MD) simulations. To address this problem, this study proposes a new on-the-fly reference data sampling method called radial distribution function (RDF)-based data sampling for MLP construction. This method detects and extracts anomalous structures from the trajectories of MLP-MD simulations by focusing on the shapes of RDFs. The detected structures are added to the reference data to improve the accuracy of the MLP. This method allows us to realize a reasonable MLP construction for liquid water with minimal additional data. We prepare data from an H2O molecular cluster system and verify whether the constructed MLPs are practical for bulk water systems. MLP-MD simulations without RDF-based data sampling show unphysical behaviors, such as atomic collisions. In contrast, after applying this method, we obtain MLP-MD trajectories with features, such as RDF shapes and angle distributions, that are comparable to those of ab initio MD simulations. Our simulation results demonstrate that the RDF-based data sampling approach is useful for constructing MLPs that are robust to extrapolations from molecular cluster systems to bulk systems without any specialized know-how.

AutoGMap: Learning to Map Large-Scale Sparse Graphs on Memristive Crossbars.

The sparse representation of graphs has shown great potential for accelerating the computation of graph applications (e.g., social networks and knowledge graphs) on traditional computing architectures (CPU, GPU, or TPU). But, the exploration of large-scale sparse graph computing on processing-in-memory (PIM) platforms (typically with memristive crossbars) is still in its infancy. To implement the computation or storage of large-scale or batch graphs on memristive crossbars, a natural assumption is that a large-scale crossbar is demanded, but with low utilization. Some recent works question this assumption; to avoid the waste of storage and computational resource, the fixed-size or progressively scheduled "block partition" schemes are proposed. However, these methods are coarse-grained or static and are not effectively sparsity-aware. This work proposes the dynamic sparsity-aware mapping scheme generating method that models the problem with a sequential decision-making model, and optimizes it by reinforcement learning (RL) algorithm (REINFORCE). Our generating model [long short-term memory (LSTM), combined with the dynamic-fill scheme] generates remarkable mapping performance on the small-scale graph/matrix data (complete mapping costs 43% area of the original matrix) and two large-scale matrix data (costing 22.5% area on qh882 and 17.1% area on qh1484). Our method may be extended to sparse graph computing on other PIM architectures, not limited to the memristive device-based platforms.

A review of graph neural networks and pretrained language models for knowledge graph reasoning

Knowledge Graph (KG) stores human knowledge facts in an intuitive graphical structure but faces challenges such as incomplete construction or inability to handle new knowledge. Knowledge Graph Reasoning (KGR) can make KGs more accurate, complete, and trustworthy to support various artificial intelligence applications better. Currently, the popular KGR methods are based on graph neural networks (GNNs). Recent studies have shown that hybrid logic rules and synergized pre-trained language models (PLMs) can enhance the GNN-based KGR methods. These methods mainly focus on data sparsity, insufficient knowledge evolution patterns, multi-modal fusion, and few-shot reasoning. Although many studies have been conducted, there are still few review papers that comprehensively summarize and explore KGR methods related to GNNs, logic rules, and PLMs. Therefore, this paper provides a comprehensive review of GNNs and PLMs for KGR based on a large number of high-quality papers. To present a clear overview of KGR, we propose a general framework. Specifically, we first introduce the KG preparation. Then we provide an overview of KGR methods, in which we categorize KGR methods into GNNs-based, logic rules-enhanced, and pre-trained language models-enhanced KGR methods. Furthermore, we also compare and analyze the GNN-based KGR methods in two scenarios. Moreover, we also present the application of KGR in different fields. Finally, we discuss the current challenges and future research directions for KGR.

Analyzing workload trends for boosting triple stores performance

The Resource Description Framework (RDF) is widely used to model web data. The scale and complexity of the modeled data emphasized performance challenges on the RDF-triple stores. Workload adaption is one important strategy to deal with those challenges on the storage level. Current workload-adaption approaches lack the necessary generalization of the problem and only optimize part of the storage layer with the workload (mostly the replication). This creates a big performance gap within other data structures (e.g. indexes and cache) that could heavily benefit from the same workload adaption strategy. Moreover, the workload statistics are built collectively in most of the current approaches. Thus, the analysis process is unaware of whether workloads’ items are old or recent. However, that does not simulate the temporal trends that exist naturally in user queries which causes the analysis process to lag behind the rapid workload development. We present a novel universal adaption approach to the storage management of a distributed RDF store. The system aims to find optimal data assignments to the different indexes, replications, and join cache within the limited storage space. We present a cost model based on the workload that often contains frequent patterns. The workload is dynamically and continuously analyzed to evaluate predefined rules considering the benefits and costs of all options of assigning data to the storage structures. The objective is to reduce query execution time by letting different data containers compete on the limited storage space. By modeling the workload statistics as time series, we can apply well-known smoothing techniques allowing the importance of the workload to decay over time. That allows the universal adaption to stay tuned with potential changes in the workload trends.

Poligras: Policy-Based Graph Summarization

Large graphs are ubiquitous. Their sizes, rates of growth, and complexity, however, have significantly outpaced human capabilities to ingest and make sense of them. As a cost-effective graph simplification technique, graph summarization is aimed to reduce large graphs into concise, structure-preserving, and quality-enhanced summaries readily available for efficient graph storage, processing, and visualization. Concretely, given a graph G , graph summarization condenses G into a succinct representation comprising (1) a supergraph with supernodes representing disjoint sets of vertices of G and superedges depicting aggregate-level connections between supernodes, and (2) a set of correction edges that help reconstruct G losslessly from the supergraph. Existing graph summarization solutions offer non-optimal graph summaries and are time-demanding in real-world large graphs. In this paper, we propose a learning-enhanced graph summarization approach, Poligras ( Poli cy-based gra ph summarization), to model the most critical computational component in graph summarization: supernode selection and merging. Specifically, we design a probabilistic policy learned and optimized by neural networks for efficient optimal supernode pair selection. As the first learning-enhanced, scalable graph summarization method, Poligras achieves significantly improved performance over state-of-the-art graph summarization solutions in real-world large graphs.

Graph Summarization: Compactness Meets Efficiency

As the volume and ubiquity of graphs increase, a compact graph representation becomes essential for enabling efficient storage, transfer, and processing of graphs. Given a graph, the graph summarization problem asks for a compact representation that consists of a summary graph and the corrections, such that we can recreate the original graph from the representation exactly. Although this problem has been studied extensively, the existing works either trade summary compactness for efficiency, or vice versa. In particular, a well-known greedy method provides the most compact summary but incurs prohibitive time cost, while the state-of-the-art algorithms with practical overheads are more than 20% behind in summary compactness in our comparison with the greedy method. This paper presents Mags and Mags-DM, two algorithms that aim to bridge the compactness and efficiency in graph summarization. Mags adopts the existing greedy paradigm that provides state-of-the-art compactness, but significantly improves its efficiency with a novel algorithm design. Meanwhile, Mags-DM follows a different paradigm with practical efficiency and overcomes its limitations in compactness. Moreover, both algorithms can support parallel computing environments. We evaluate Mags and Mags-DM on graphs up to billion-scale and demonstrate that they achieve state-of-the-art in both compactness and efficiency, rather than in one of them. Compared with the method that offers state-of-the-art compactness, Mags and Mags-DM have a small difference (< 0.1% and < 2.1%) in compactness. For efficiency, Mags is on average 11.1x and 4.2x faster than the two state-of-the-art algorithms with practical overheads, while Mags-DM can further reduce the running time by 13.4x compared with Mags. This shows that graph summarization algorithms can be made practical while still offering a compact summary.

GENTI: GPU-Powered Walk-Based Subgraph Extraction for Scalable Representation Learning on Dynamic Graphs

Graph representation learning is an emerging task for effectively embedding graph-structured data with learned features. Among them, Subgraph-based GRL (SGRL) methods have demonstrated better scalability and expressiveness for large-scale tasks. The core challenge of applying SGRL to dynamic graphs lies in accommodating the extraction of subgraphs to evolving data with efficient computation. To address the efficiency bottleneck, we propose GENTI, a GPU-oriented SGRL algorithm for dynamic graphs. Our approach mainly improves the critical subgraph extraction stage by disentangling it into two phases, namely neighbor sampling and subgraph gathering, which are respectively performed on CPU and GPU in an asynchronous fashion. The design favorably eliminates the dependence of feature learning on subgraph extraction, and is capable of exploiting the GPU's batch processing ability to remarkably boost computations throughout the pipeline. Dedicated data structures are designed for efficiently managing the dynamic graph storage and conforming efficient subgraph operations. Extensive empirical results on various real-world dynamic graphs show that GENTI achieves up to 30× faster in subgraph extraction time than the state-of-the-art walk-based methods and up to 26× acceleration in overall learning time, while maintaining comparable prediction performance. In particular, it is able to complete learning on the largest available graph of 1.3 billion edges within 24 hours, while all other baselines exhibit prohibitive overhead.

Declarative generation of RDF-star graphs from heterogeneous data

RDF-star has been proposed as an extension of RDF to make statements about statements. Libraries and graph stores have started adopting RDF-star, but the generation of RDF-star data remains largely unexplored. To allow generating RDF-star from heterogeneous data, RML-star was proposed as an extension of RML. However, no system has been developed so far that implements the RML-star specification. In this work, we present Morph-KGCstar, which extends the Morph-KGC materialization engine to generate RDF-star datasets. We validate Morph-KGCstar by running test cases derived from the N-Triples-star syntax tests and we apply it to two real-world use cases from the biomedical and open science domains. We compare the performance of our approach against other RDF-star generation methods (SPARQL-Anything), showing that Morph-KGCstar scales better for large input datasets, but it is slower when processing multiple smaller files.

Spruce: a Fast yet Space-saving Structure for Dynamic Graph Storage

Dynamic graphs have been gaining increasing popularity across various application domains. With the growing size of these graphs, the update performance as well as space occupancy is becoming a crucial aspect of dynamic graph storage. Although existing dynamic graph systems can handle massive streaming updates (e.g., insertions and deletions), they cannot achieve both high throughput and low memory footprint. Drawing inspiration from the basic operations of the van Emde Boas (vEB) tree in double-logarithmic time, we designed Spruce, a high-performance yet space-saving in-memory structure to store dynamic graphs. Spruce uses a compact representation to construct the tree-like multilevel structure, which shares the common prefixes of vertices and has no merging or splitting of nodes to achieve the requirements of low memory consumption and high-efficiency dynamic operations. Furthermore, Spruce incorporates a read-optimized concurrency protocol, which refines ROWEX and Optimistic Locking, to facilitate efficient simultaneous read/write operations. Our experiment demonstrates that compared to Sortledton (the best of competitors), Spruce is up to 2.4X faster in ingesting graph updates, while saving up to 38.5% of memory space. As for graph analytics, Spruce shows high adaptability to different analytical workloads, and achieves comparable performance to other state-of-the-art dynamic graph structures.

Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores

Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores

LUBM4OBDA: Benchmarking OBDA Systems with Inference and Meta Knowledge

Ontology-based data access focuses on enabling query evaluation over heterogeneous relational databases according to the model represented by an ontology. The relationships between the ontology and the data sources are commonly defined with declarative mappings, which are used by systems to perform SPARQL-to-SQL query translation or to generate RDF dumps from the relational databases. Besides the potential homogenization of data because of using an ontology, some additional advantages of this paradigm are that it may allow applying reasoning thanks to the ontology, as well as querying for meta knowledge, which describes statements with information such as provenance or certainty. In this paper, (i) we adapt a widely used RDF graph store benchmark, namely LUBM, for ontology-based data access, (ii) extend the benchmark for the evaluation of queries that exploit meta knowledge, and (iii) apply it for performance evaluation of state-of-the-art declarative mapping systems. Our proposal, the LUBM4OBDA Benchmark, considers inference capabilities that are not covered by previous ontology-based data access benchmarks, and it is the first one for the evaluation of meta knowledge and the RDF-star data model. The experimental evaluation shows that current virtualization systems cannot handle some advanced inference tasks, and that optimizations are needed to scale RDF-star materialization.

A simple and efficient approach to unsupervised instance matching and its application to linked data of power plants

Knowledge graphs store and link semantically annotated data about real-world entities from a variety of domains and on a large scale. The World Avatar is based on a dynamic decentralised knowledge graph and on semantic technologies to realise complex cross-domain scenarios. Accurate computational results for such scenarios require the availability of complete, high-quality data. This work focuses on instance matching — one of the subtasks of automatically populating the knowledge graph with data from a wide spectrum of external sources. Instance matching compares two data sets and seeks to identify instances (data, records) referring to the same real-world entity. We introduce AutoCal, a new instance matcher which does not require labelled data and runs out of the box for a wide range of domains without tuning method-specific parameters. AutoCal achieves results competitive to recently proposed unsupervised matchers from the field of Machine Learning. We also select an unsupervised state-of-the-art matcher from the field of Deep Learning for a thorough comparison. Our results show that neither AutoCal nor the state-of-the-art matcher is superior regarding matching quality while AutoCal has only moderate hardware requirements and runs 2.7 to 60 times faster. In summary, AutoCal is specifically well-suited to be used in an automated environment. We present its prototypical integration into the World Avatar and apply AutoCal to the domain of power plants which is relevant for practical environmental scenarios of the World Avatar.

Knowledge graph‐driven data processing for business intelligence

AbstractWith proliferation of Big Data, organizational decision making has also become more complex. Business Intelligence (BI) is no longer restricted to querying about marketing and sales data only. It is more about linking data from disparate applications and also churning through large volumes of unstructured data like emails, call logs, social media, News, and so on in an attempt to derive insights that can also provide actionable intelligence and better inputs for future strategy making. Semantic technologies like knowledge graphs have proved to be useful tools that help in linking disparate data sources intelligently and also enable reasoning through complex networks that are created as a result of this linking. Over the last decade the process of creation, storage, and maintenance of knowledge graphs have sufficiently matured, and they are now making inroads into business decision making also. Very recently, these graphs are also seen as a potential way to reduce hallucinations of large language models, by including these during pre‐training as well as generation of output. There are a number of challenges also. These include building and maintaining the graphs, reasoning with missing links, and so on. While these remain as open research problems, we present in this article a survey of how knowledge graphs are currently used for deriving business intelligence with use‐cases from various domains.This article is categorized under: Algorithmic Development > Text Mining Application Areas > Business and Industry

Towards fair and personalized federated recommendation

Recommender systems have gained immense popularity in recent years for predicting users’ interests by learning embeddings. The majority of existing recommendation approaches, represented by graph neural network-based recommendation algorithms, rely on centralized storage of user-item graphs for model learning, which raises privacy issues in the process of collecting and sharing user data. Federated recommendation can mitigate privacy concerns by preventing the server from collecting sensitive data from clients while there still exist unfairness and personalization issues. To address these challenges, we propose a novel framework named Fair and Personalized Federated Recommendation (FPFR). On the client-side, the soft attention mechanism is designed to learn the representation of user/item by combining interaction and attribute information, and the filter network is combined to better characterize user preferences. On the server-side, we cluster users into different groups and learn personalized models for each user. Then, we select representative users from each group to participate in the global model parameters update. Finally, the fairness of federated recommendation is implemented by adding the fairness constraint to recommendation loss. We conduct experiments on five real-world recommendation datasets, and the results demonstrate that the proposed FPFR not only balances group fairness and recommendation accuracy but also improves personalization.

Multi-hop path reasoning over sparse temporal knowledge graphs based on path completion and reward shaping

Multi-hop path reasoning plays a crucial role in temporal knowledge graph reasoning, aiming to infer deep complex relationships and obtain interpretable reasoning results. Previous reasoning models primarily focus on designing temporal knowledge graphs with rich paths between entities but struggle to handle path sparsity, known as sparse temporal knowledge graphs. Sparse temporal knowledge graphs store only essential knowledge information, resulting in missing connection paths between certain entities and encountering issues of sparse rewards and information scarcity. To tackle these challenges, this paper proposes a multi-hop path reasoning model over sparse temporal knowledge graph based on path completion and reward shaping (STKGR-PR). STKGR-PR dynamically completes missing paths using a temporal embedding model to alleviate path sparsity. To tackle the issue of sparse rewards caused by reduced hit rates, we propose semantic composition-based reasoning path embeddings derived from relation embeddings and timestamp embeddings for reward shaping. Considering the limited information contained in sparse temporal knowledge graphs, we incorporate time vectors into the embedding and multi-hop path reasoning models to enhance the accuracy of reasoning paths and results. Experimental results conducted on six sparse temporal datasets sampled from ICEWS14 and ICEWS515–05 demonstrate that STKGR-PR outperforms state-of-the-art multi-hop path reasoning models over temporal knowledge graphs across all evaluation metrics, while ensuring interpretability.

NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams

Existing Graph Neural Network (GNN) training frameworks have been designed to help developers easily create performant GNN implementations. However, most existing GNN frameworks assume that the input graphs are static, but ignore that most real-world graphs are constantly evolving. Though many dynamic GNN models have emerged to learn from evolving graphs, the training process of these dynamic GNNs is dramatically different from traditional GNNs in that it captures both the spatial and temporal dependencies of graph updates. This poses new challenges for designing dynamic GNN training frameworks. First, the traditional batched training method fails to capture real-time structural evolution information. Second, the time-dependent nature makes parallel training hard to design. Third, it lacks system supports for users to efficiently implement dynamic GNNs. In this paper, we present NeutronStream, a framework for training dynamic GNN models. NeutronStream abstracts the input dynamic graph into a chronologically updated stream of events and processes the stream with an optimized sliding window to incrementally capture the spatial-temporal dependencies of events. Furthermore, NeutronStream provides a parallel execution engine to tackle the sequential event processing challenge to achieve high performance. NeutronStream also integrates a built-in graph storage structure that supports dynamic updates and provides a set of easy-to-use APIs that allow users to express their dynamic GNNs. Our experimental results demonstrate that, compared to state-of-the-art dynamic GNN implementations, NeutronStream achieves speedups ranging from 1.48X to 5.87X and an average accuracy improvement of 3.97%.

The Design of Triple Store and Query Processing on GPU for Large Scale Resource Description Framework Data

The Resource Description Framework (RDF) is commonly used as a standard for data interchange on the web. Due to the current big data era, its size is prone to increase drastically. In order to speed up the large RDF data query, we propose a novel RDF data representation along with the RDF query algorithm utilizing GPU processing. We also present the representation that is suitable for RDF data and GPU processing, the indexing approach, the querying process in the GPU and other techniques that increase the efficiency such as pre-upload filtering, ID assignment by using the term similarity of term vector, and dimensional reduction to transform back to term ID. The experiments show that the developed framework utilizes the storage only 1/6 of the original one and can reduce the querying time. The speedup obtained can be up to 29.57 when compared with the RDF-3X system and 45.23 when compared to using gStore, a graph data store.