Triple Store Research Articles

Metadata Management and Asset Exchange in the Agricultural Data Ecosystem of the Project Agri-Gaia

AbstractThe particularities of the agricultural ecosystem of cooperating partners suggest a highly distributed, open data architecture to support the exchange and re-use of data and data-driven applications. We describe the data strategy and the federated basic architecture of the Agri-Gaia ecosystem. We present an ontology-based approach to metadata management which extends a bucket store for arbitrary data storage by an RDF-based metadata graph store and employs widely-used domain ontologies as a conceptual basis. Data and service providers are free to extend the describing metadata at any time, according to their needs. The resulting set of interconnected platforms supports the publication, retrieval and secure sharing of exposable data under full control of their owners.

Logic and Commonsense-Guided Temporal Knowledge Graph Completion

A temporal knowledge graph (TKG) stores the events derived from the data involving time. Predicting events is extremely challenging due to the time-sensitive property of events. Besides, the previous TKG completion (TKGC) approaches cannot represent both the timeliness and the causality properties of events, simultaneously. To address these challenges, we propose a Logic and Commonsense-Guided Embedding model (LCGE) to jointly learn the time-sensitive representation involving timeliness and causality of events, together with the time-independent representation of events from the perspective of commonsense. Specifically, we design a temporal rule learning algorithm to construct a rule-guided predicate embedding regularization strategy for learning the causality among events. Furthermore, we could accurately evaluate the plausibility of events via auxiliary commonsense knowledge. The experimental results of TKGC task illustrate the significant performance improvements of our model compared with the existing approaches. More interestingly, our model is able to provide the explainability of the predicted results in the view of causal inference. The appendix, source code and datasets of this paper are available at https://github.com/ngl567/LCGE.

Theory and Practice of Relational-to-RDF Temporal Data Exchange and Query Answering

We consider the problem of answering temporal queries on RDF stores, in presence of atemporal RDFS domain ontologies, of relational data sources that include temporal information, and of rules that map the domain information in the source schemas into the target ontology. Our proposed practice-oriented solution consists of two rule-based domain-independent algorithms. The first algorithm materializes target RDF data via a version of data exchange that enriches both the data and the ontology with temporal information from the relational sources. The second algorithm accepts as inputs temporal queries expressed in terms of the domain ontology using a lightweight temporal extension of SPARQL, and ensures successful evaluation of the queries on the materialized temporally-enriched RDF data. To study the quality of the information generated by the algorithms, we develop a general framework that formalizes the relational-to-RDF temporal data-exchange problem. The framework includes a chase formalism and a formal solution for the problem of answering temporal queries in the context of relational-to-RDF temporal data exchange. In this article, we present the algorithms and the formal framework that proves correctness of the information output by the algorithms, and also report on the algorithm implementation and experimental results for two application domains.

Deep Active Alignment of Knowledge Graph Entities and Schemata

Knowledge graphs (KGs) store rich facts about the real world. In this paper, we study KG alignment, which aims to find alignment between not only entities but also relations and classes in different KGs. Alignment at the entity level can cross-fertilize alignment at the schema level. We propose a new KG alignment approach, called DAAKG, based on deep learning and active learning. With deep learning, it learns the embeddings of entities, relations and classes, and jointly aligns them in a semi-supervised manner. With active learning, it estimates how likely an entity, relation or class pair can be inferred, and selects the best batch for human labeling. We design two approximation algorithms for efficient solution to batch selection. Our experiments on benchmark datasets show the superior accuracy and generalization of DAAKG and validate the effectiveness of all its modules.

A knowledge graph-based deep learning framework for efficient content similarity search of Sustainable Development Goals data

Abstract Sustainable development denotes the enhancement of living standards in the present without compromising future generations’ resources. Sustainable Development Goals (SDGs) quantify the accomplishment of sustainable development and pave the way for a world worth living in for future generations. Scholars can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data, as intended by this work. We propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves (HSFCs) in order to semantically cluster new uncategorised SDG data and novel indicators, and efficiently place them in the environment of a distributed knowledge graph store. First, a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment, for retrieval of indicators and load-balancing along with an approach for data classification of entrant-indicators is described. Then, a thorough case study in a distributed knowledge graph environment experimentally evaluates our framework. The results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data, including intergovernmental organizations, government agencies and social welfare organizations. Our approach empowers SDG knowledge graphs for causal analysis, inference, and manifold interpretations of the societal implications of SDG-related actions, as data are accessed in reduced retrieval times. It facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching, as semantic cohesion of data is preserved.

A Universal Question-Answering Platform for Knowledge Graphs

Knowledge from diverse application domains is organized as knowledge graphs (KGs) that are stored in RDF engines accessible in the web via SPARQL endpoints. Expressing a well-formed SPARQL query requires information about the graph structure and the exact URIs of its components, which is impractical for the average user. Question answering (QA) systems assist by translating natural language questions to SPARQL. Existing QA systems are typically based on application-specific human-curated rules, or require prior information, expensive pre-processing and model adaptation for each targeted KG. Therefore, they are hard to generalize to a broad set of applications and KGs. In this paper, we propose KGQAn, a universal QA system that does not need to be tailored to each target KG. Instead of curated rules, KGQAn introduces a novel formalization of question understanding as a text generation problem to convert a question into an intermediate abstract representation via a neural sequence-to-sequence model. We also develop a just-in-time linker that maps at query time the abstract representation to a SPARQL query for a specific KG, using only the publicly accessible APIs and the existing indices of the RDF store, without requiring any pre-processing. Our experiments with several real KGs demonstrate that KGQAn is easily deployed and outperforms by a large margin the state-of-the-art in terms of quality of answers and processing time, especially for arbitrary KGs, unseen during the training.

A Hierarchical Parallel Graph Summarization Approach Based on Ranking Nodes

Graph summarization techniques are vital in simplifying and extracting enormous quantities of graph data. Traditional static graph structure-based summarization algorithms generally follow a minimum description length (MDL) style, and concentrate on minimizing the graph storage overhead. However, these methods also suffer from incomprehensive summary dimensions and inefficiency problems. In addition, the need for graph summarization techniques often varies among different graph applications, but an ideal summary method should generally retain the important characteristics of the key nodes in the final summary graph. This paper proposes a novel method based on ranking nodes, called HRNS, that follows a hierarchical parallel graph summarization approach. The HRNS first preprocesses the node ranking using a hybrid weighted importance strategy, and introduces the node importance factor into traditional MDL-based summarization algorithms; it then leverages a hierarchical parallel process to accelerate the summary computation. The experimental results obtained using both real and simulated datasets show that HRNS can efficiently extract nodes with high importance, and that the average importance over six datasets ranges from 0.107 to 0.167; thus, HRNS can achieve a significant performance gain on speedups, as the sum error ratios are also lower than the methods traditionally used.

Cluster-Based Joins for Federated SPARQL Queries

Federated RDF systems allow users to retrieve data from multiple independent sources without needing to have all the data in the same triple store. The performance of these systems can be poor for large and geographically distributed RDF data where network transfer costs are high. This article introduces CBTP-OL and CBTP-Nhop, two novel join algorithms that take advantage of network topology to decrease the cost of processing Basic Graph Pattern (BGP) SPARQL queries in a geographically distributed environment. Federation members are grouped in clusters, based on the network communication cost between the members, and the bulk of the join processing is pushed to the clusters. Our CBTP-OL and CBTL-Nhop algorithms use an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">overlap list</i> and, respectively, an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N-hop overlap list</i> , to efficiently compute join results from triples in different clusters. We implement our algorithms in the OpenRDF Sesame federated framework and use Apache Rya triple store instances as federation members. Experimental evaluation results show the advantages of our approach over existing techniques.

Realizing health data interoperability in low connectivity settings: The case of VODAN-Africa

VODAN Africa has produced FAIR data in low resource settings. Federated machine actionable data is available in a triple store for visiting. Interoperability facets (I1–I3) were followed to achieve semantic interoperability. Vertical interoperability was also realized with DHIS2.

Institutional Repositories in the Linked Open Data Cloud

We believe that the wealth of digital humanity research resources that is available in Institutional Repositories in Southern Africa is largely over-looked because it is not machine explorable as defined by Berners-Lee (2006). We argue that extracting research from Institutional Repositories, which Berners-Lee (2006) describes as One-Star Linked Data, and then enriching this research and storing it as tuples in a triple store (RDF data store) will expose the research to a greater audience; make it machine explorable; and integrate it with a broader network of Linked Data, known as the Linked Open Data Cloud (LODC). Our approach is to utilize software building blocks licensed under Free (Libre) Open Source Software (FLOSS) licences and to create a pipeline that can potentially transform any Institutional Repository into into a triple store, which can be published on the Internet as part of the LODC. Our transformation process embraces under-resourced institutions by utilizing FLOSS com- ponents and utilizing digital resources already published on the internet in order to promote the research from One-Star Linked data to Five-Star Linked Data in Berners-Lee (2006) taxonomy of linked data.

AAE: An active auto-estimator for improving graph storage

Graph has currently become an increasingly popular model in many real applications. The efficiency of graph storage is crucial for these applications. Generally speaking, the tuning task of graph storage relies on the database administrators to find the best graph storage. However, DBAs make the tuning decisions by mainly relying on their experiences and intuition. Due to the limitations of the DBAs, the tuning may have an uncertain performance and worse efficiency. In this paper, we observed that an estimator of the graph workload has the potential ability to guarantee the performance of tuning operations. Unfortunately, because of complex characteristics of the graph evaluation task, there exists no mature estimator for graph workload. We formulate the evaluation task of graph workload as a classification task and carefully design the feature engineering process, including graph data features, graph workload features and graph storage features. For this evaluation task, we propose an active auto-estimator (AAE) for the graph workload evaluation by combining the active learning and deep learning. We test the time efficiency and evaluation accuracy of AAE with two open source graph data, LDBC and Freebase. The experimental results show that our estimator could efficiently complete the graph workload evaluation in milliseconds.

Searching for Researchers: an Ontology-based NoSQL Database System Approach and Practical Implementation

This work presents the design and implementation of two web-based search systems, Busc@NIMA and Quem@PUC. Both systems allow the identification of research and development projects, besides existing competencies in laboratories and departments involving professors and researchers at PUC-Rio University. Our applications are based on a list of search-related terms that are matched to the dataset composed of PUC-Rio’s Lattes CVs offered courses, information from administrative systems, and specific keywords that are input by the professors/researchers themselves. To integrate all the needed data, we consider multiple database and search technologies, such as XML, RDF, TripleStores, and Relational Databases. Search results include professor’s name, academic papers, teaching activities, contact links, keywords, and laboratories of those involved with the subject represented by the set of keywords input. We describe the main features that show how our systems work.

HyperBit: A temporal graph store for fast answering queries

Relationships or interactions among entities interactions often have occurrence time. So, temporal graph is becoming a popular model to represent temporal data. Temporal graph is generally much larger than corresponding non-temporal graph because an non-temporal edge may have many corresponding temporal edges. It raises challenges for querying temporal graphs. Here, we present HyperBit, a temporal graph store which can answer temporal queries efficiently. HyperBit models temporal labeled graph as a series of updates or log records on graph. Then we design an efficient partition storage for log. Since it is costly to answer temporal queries using logs due to full scan of log, we propose an optimal algorithm to build some snapshots to speedup query processing. So, HyperBit can answer temporal queries by applying log records on the snapshot close to the time in query. HyperBit employs SPARQL instead of a new language to describe temporal queries. Thus, HyperBit can process non-temporal queries on temporal/non-temporal graphs. Extensive experiments show that HyperBit significantly outperforms RDF-3x, Jena-TDB in terms of update speed while it has a compact storage. When querying static graphs, HyperBit also outperforms RDF-3X, Jena-TDB by a wide margin and is on par with TripleBit. For temporal queries, HyperBit can easily handle billion graphs, maintaining linear time growth so that has excellent scalability.

Foundational patterns benchmark

Recently, there has been growing interest in the use of ontology as a fundamental tool for representing domain-specific conceptual models to improve the semantics, accuracy, and relevance of domain users’ query results. Although the amount of data has grown steadily over the past decade, much data shares similar characteristics that can be captured by a foundational ontology. In this paper, we show how queries based on a foundational ontology can be evaluated and their performance measured. We also present a Foundational Patterns benchmark to help select the most efficient triple memory and its layout. We evaluate the foundational benchmark with both generated and real datasets for state-of-the-art triple stores.

ORGANIZING SMART CITY DATA BASED ON 3D POINT CLOUD IN UNSTRUCTURED DATABASE – AN OVERVIEW

Abstract. The concept of the 3D smart city is an integration of smart cities and information technology. One of the data sources of a smart city is point cloud data that are produced from various data acquisition tools such as LiDAR, Terrestrial Laser Scanning, and Unmanned Aerial Vehicle. Due to the large size of point cloud data input, traditional databases could not handle the data efficiently. Alternatively, unstructured databases have become an option. Furthermore, data for smart city applications are considered being complex and large. Storing data in the unstructured database can easily be retrieved from various front ends such as web and mobile devices. However, unstructured databases do not have fixed schema and data types that often limit the uses of 3D point cloud data in relational databases. There are four categories of the data model in the unstructured database: document store, key-value, column store, and graph store. Each of the categories has different characteristics and approaches to handling data. Thus, this paper aims to summarise an overview of each category and determine the most suitable data organisation and environment for a 3D point cloud of a smart city. The overview will aid the developer or user select and comparing available data models in the unstructured database to handle 3D point clouds.

Clinical decision support system for hypertension medication based on knowledge graph

BackgroundHigh prevalence of hypertension and complicated medication knowledge have presented challenges to hypertension clinicians and general practitioners. Clinical decision support systems (CDSSs) are developed to aid clinicians in decision making. Current clinical knowledge is stored in fixed templates, which are not intuitive for clinicians and limit the knowledge reusability. Knowledge graphs (KGs) store knowledge in a way that is not only intuitive to humans but also processable by computers directly. However, existing medical KGs such as UMLS and CMeKG are general purpose and thus lack enough knowledge to enable hypertension medication. MethodsWe first construct a KG specific to hypertension medication according to the Chinese hypertension guideline and then develop the corresponding CDSS to implement hypertension medication and knowledge management. Current advances in knowledge graph representation and modelling are researched and applied in the complex medical knowledge representation. Traditional knowledge representation and KG representation are innovatively combined in the storage of the KG to enable convenient knowledge management and easy application by the CDSS. Along a predefined reasoning path in the KG, the CDSS finally accomplishes the hypertension medication by applying knowledge stored in the KG. 124 health records of a hypertension Chief Physician from Beijing Anzhen Hospital, Capital Medical University, are collected to evaluate the system metrics on the single drug recommendation task. Results and conclusionThe proposed CDSS has functions of medication knowledge graph management and hypertension medication decision support. With elaborate design on knowledge representation, knowledge management is intuitive and convenient. By virtue of the KG, medication recommendations are highly visualized and explainable. Experiments on 124 health records with 90% guideline compliance collected from hospitals in single class recommendation task achieve 91%, 83% and 77% on recall, hit@3 and MRR metrics respectively, which demonstrates the quality of the KG and effectiveness of the system.

Accelerating Large-Scale Graph Neural Network Training on Crossbar Diet

Resistive random-access memory (ReRAM)-based manycore architectures enable acceleration of graph neural network (GNN) inference and training. GNNs exhibit characteristics of both DNNs and graph analytics. Hence, GNN training/inferencing on ReRAM-based manycore architectures give rise to both computation and on-chip communication challenges. In this work, we leverage model pruning and efficient graph storage to reduce the computation and communication bottlenecks associated with GNN training on ReRAM-based manycore accelerators. However, traditional pruning techniques are either targeted for inferencing only, or they are not crossbar-aware. In this work, we propose a GNN pruning technique called DietGNN. DietGNN is a crossbar-aware pruning technique that achieves high accuracy training and enables energy, area, and storage efficient computing on ReRAM-based manycore platforms. The DietGNN pruned model can be trained from scratch without any noticeable accuracy loss. Our experimental results show that when mapped on to a ReRAM-based manycore architecture, DietGNN can reduce the number of crossbars by over 90% and accelerate GNN training by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\sim }{2}.{7}{\times }$ </tex-math></inline-formula> compared to its unpruned counterpart. In addition, DietGNN reduces energy consumption by more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\sim }{3}.{5}{\times }$ </tex-math></inline-formula> compared to the unpruned counterpart.

Robust and fuzzy ensemble framework via spectral learning for random projection-based fuzzy-c-means clustering

The ensembles of random projection-based fuzzy-c-means (RP-FCM) can handle high-dimensional data efficiently. However, the performance of these ensemble frameworks is still hindered by some issues, such as misaligned membership matrices, information loss of co-similar matrices, large storage space, unstable ensemble results due to the additional re-clustering, etc. To address these issues, we propose a robust and fuzzy ensemble framework via spectral learning for RP-FCM clustering. After using random projection to generate different dimensional datasets and obtaining the membership matrices via fuzzy-c-means, we first convert these membership matrices into regularized graphs and approximates the affinity matrices of these graphs by spectral matrices. This step not only avoids the alignment problems of membership matrices but also excludes the storage of large-scale graphs. The spectral matrices of the same size are used as the features of membership matrices for the ensemble, avoiding the possible information loss by applying co-similar matrix transformations. More importantly, an optimization model is designed in our framework to learn the fusion of spectral features. In this model, the proportion of each base clustering is adjusted adaptively through a fuzzification exponent, and the effect of outliers is also suppressed by a robust norm. Finally, the Laplacian rank constraint in the model guarantees the ensemble can achieve the exact final partition. An efficient algorithm for this model is derived, and its time complexity and convergence are also analyzed. Competitive experimental results on benchmark data demonstrate the effectiveness of the proposed ensemble framework in comparison to state-of-the-art methods.

Semantic Web Application: Tourist Assisting Web Application

Abstract: In addition to traditional web technologies, semantic web technology allows the implementation of the "web of things" and "web of linked data," enabling both machines and humans to access the web. The ultimate objective is to allow machines to do increasingly complex tasks and to design systems that can support network interactions. This goal may be realized by merging several linked data technologies, such as RDF, SPARQL, OWL, and SKOS (W3C Org, 2014). In this project, the authors integrated semantic web technologies into the software to illustrate their use. This study describes in detail the design and functionality of the apps, as well as how the semantic web technology principles were implemented. The authors followed the principles and best practices outlined in W3C documents and directed by the first author. The authors exerted a great deal of work to evaluate the acquired data and have begun the project by locating suitable datasets from various data sources (heterogeneous data sources). Following this, more attempts were made to cleanse and verify the data. This generated dataset was subsequently pushed to the selected triple store on application start-up. The ontology design and project architecture are described in full in this study. The project's stack and implementation has been discussed in this report.

FAIR Digital Objects in Official Statistics

FAIR Digital Objects in Official Statistics