Resource Description Framework Databases Research Articles

Development of an integrated and inferenceable RDF database of glycan, pathogen and disease resources

Glycans are known to play extremely important roles in infections by viruses and pathogens. In fact, the SARS-CoV-2 virus has been shown to have evolved due to a single change in glycosylation. However, data resources on glycans, pathogens and diseases are not well organized. To accurately obtain such information from these various resources, we have constructed a foundation for discovering glycan and virus interaction data using Semantic Web technologies to be able to semantically integrate such heterogeneous data. Here, we created an ontology to encapsulate the semantics of virus-glycan interactions, and used Resource Description Framework (RDF) to represent the data we obtained from non-RDF related databases and data associated with literature. These databases include PubChem, SugarBind, and PSICQUIC, which made it possible to refer to other RDF resources such as UniProt and GlyTouCan. We made these data publicly available as open data and provided a service that allows anyone to freely perform searches using SPARQL. In addition, the RDF resources created in this study are available at the GlyCosmos Portal.

Natural language query formalization to SPARQL for querying knowledge bases using Rasa

The idea of data to be semantically linked and the subsequent usage of this linked data with modern computer applications has been one of the most important aspects of Web 3.0. However, the actualization of this aspect has been challenging due to the difficulties associated with building knowledge bases and using formal languages to query them. In this regard, SPARQL, a recursive acronym for standard query language and protocol for Linked Open Data and Resource Description Framework databases, is a most popular formal querying language. Nonetheless, writing SPARQL queries is known to be difficult, even for experts. Natural language query formalization, which involves semantically parsing natural language queries to their formal language equivalents, has been an essential step in overcoming this steep learning curve. Recent work in the field has seen the usage of artificial intelligence (AI) techniques for language modelling with adequate accuracy. This paper discusses a design for creating a closed domain ontology, which is then used by an AI-powered chat-bot that incorporates natural language query formalization for querying linked data using Rasa for entity extraction after intent recognition. A precision–recall analysis is performed using in-built Rasa tools in conjunction with our own testing parameters, and it is found that our system achieves a precision of 0.78, recall of 0.79 and F1-score of 0.79, which are better than the current state of the art.

Graph rewriting rules for RDF database evolution: optimizing side-effect processing

Purpose Graph rewriting concerns the technique of transforming a graph; it is thus natural to conceive its application in the evolution of graph databases. This paper aims to propose a two-step framework where rewriting rules formalize instance or schema changes, ensuring graph’s consistency with respect to constraints, and updates are managed by ensuring rule applicability through the generation of side effects: new updates which guarantee that rule application conditions hold. Design/methodology/approach This paper proposes Schema Evolution Through UPdates, optimized version (SetUpOPT), a theoretical and applied framework for the management of resource description framework (RDF)/S database evolution on the basis of graph rewriting rules. The framework is an improvement of SetUp which avoids the computation of superfluous side effects and proposes, via SetUpoptND, a flexible and extensible package of solutions to deal with non-determinism. Findings This paper shows graph rewriting into a practical and useful application which ensures consistent evolution of RDF databases. It introduces an optimised approach for dealing with side effects and a flexible and customizable way of dealing with non-determinism. Experimental evaluation of SetUpoptND demonstrates the importance of the proposed optimisations as they significantly reduce side-effect generation and limit data degradation. Originality/value SetUp originality lies in the use of graph rewriting techniques under the closed world assumption to set an updating system which preserves database consistency. Efficiency is ensured by avoiding the generation of superfluous side effects. Flexibility is guaranteed by offering different solutions for non-determinism and allowing the integration of customized choice functions.

Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF Databases

Triplestores or resource description framework (RDF) stores are purpose-built databases used to organise, store and share data with context. Knowledge extraction from a large amount of interconnected data requires effective tools and methods to address the complexity and the underlying structure of semantic information. We propose a method that generates an interpretable multilayered network from an RDF database. The method utilises frequent itemset mining (FIM) of the subjects, predicates and the objects of the RDF data, and automatically extracts informative subsets of the database for the analysis. The results are used to form layers in an analysable multidimensional network. The methodology enables a consistent, transparent, multi-aspect-oriented knowledge extraction from the linked dataset. To demonstrate the usability and effectiveness of the methodology, we analyse how the science of sustainability and climate change are structured using the Microsoft Academic Knowledge Graph. In the case study, the FIM forms networks of disciplines to reveal the significant interdisciplinary science communities in sustainability and climate change. The constructed multilayer network then enables an analysis of the significant disciplines and interdisciplinary scientific areas. To demonstrate the proposed knowledge extraction process, we search for interdisciplinary science communities and then measure and rank their multidisciplinary effects. The analysis identifies discipline similarities, pinpointing the similarity between atmospheric science and meteorology as well as between geomorphology and oceanography. The results confirm that frequent itemset mining provides an informative sampled subsets of RDF databases which can be simultaneously analysed as layers of a multilayer network.

RW-QAnswer: an assisting system for intelligent environments using semantic technology

We have seen rapid growth of Internet of Things (IoT) paradigm. Challenges of IoT include the need to obtain comprehensive environmental information using multiple and different types of sensors as well as the need to reduce the amount of bandwidth used by a large number of sensors. Especially when image sensors are used, a significant amount of bandwidth is used for sending images. In addition, the use of IoT devices need not be limited to experts who are good at handling sensors. For example, there is the home IoT system which is expected to be used at home by several users. Thus, it is necessary to have an interface which is easy to handle, even for people who are not good at handling data and sensors. To solve these problems, we use Resource Description Framework (RDF) used in the Semantic Web field as metadata of sensor data for comprehensive environmental information acquisition. Then, by linking with an existing RDF search system called QAnswer, which uses natural language, we create a system that enables sensor data search using natural language. Thus, we design an intelligent system which enables users to interact with sensors using natural language. By combining an RDF database and a server which controls the flow of messages, we then investigate the trade-off between the response time to a user’s request and the amount of bandwidth usage by messages. Our results show that in a sensor network using RDF, it is possible to reduce the amount of communication traffic by optimally transferring RDF and sensor data only on arrival of a request and this can be done without much increase in the communication latency.

Building self-clustering RDF databases using Tunable-LSH

The Resource Description Framework (RDF) is a W3C standard for representing graph-structured data, and SPARQL is the standard query language for RDF. Recent advances in information extraction, linked data management and the Semantic Web have led to a rapid increase in both the volume and the variety of RDF data that are publicly available. As businesses start to capitalize on RDF data, RDF data management systems are being exposed to workloads that are far more diverse and dynamic than what they were designed to handle. Consequently, there is a growing need for developing workload-adaptive and self-tuning RDF data management systems. To realize this objective, we introduce a fast and efficient method for dynamically clustering records in an RDF data management system. Specifically, we assume nothing about the workload upfront, but as SPARQL queries are executed, we keep track of records that are co-accessed by the queries in the workload and physically cluster them. To decide dynamically and in constant-time where a record needs to be placed in the storage system, we develop a new locality-sensitive hashing (LSH) scheme, Tunable-LSH. Using Tunable-LSH, records that are co-accessed across similar sets of queries can be hashed to the same or nearby physical pages in the storage system. What sets Tunable-LSH apart from existing LSH schemes is that it can auto-tune to achieve the aforementioned clustering objective with high accuracy even when the workloads change. Experimental evaluation of Tunable-LSH in an RDF data management system as well as in a standalone hashtable shows end-to-end performance gains over existing solutions.

A SPARQL query engine for binary-formatted IFC building models

Abstract To date, widely implemented and full-featured query languages for building models in their native exchange formats do not exist. While interesting proposals exist for querying Industry Foundation Classes (IFC) models, their functionality is often incomplete and their semantics not precisely defined. With the introduction of the ifcOWL ontology as an equivalent to the IFC schema in the Web Ontology Language (OWL), an option to represent such models in RDF (Resource Description Framework, a general information modeling method) is provided, and such models can be queried using SPARQL (SPARQL Protocol and RDF Query Language). The size of data sets in complex building projects, however, renders the use of clear-text encoded RDF infeasible in many cases. A SPARQL implementation, compatible with ifcOWL, is proposed, directly atop a standardized binary serialization format for IFC building models. This novel format is the binary equivalent of traditional IFC serialization formats but with more compact storage and less overhead than the graph serialization in RDF. The format is based on ISO 10303-26 and relies on an open standard for organizing large amounts of data: Hierarchical Data Format version 5 (HDF5). Due to hierarchical partitioning and fixed-length records, only small subsets of the data are read to answer queries, improving efficiency. A prototypical implementation of the query engine is provided in the Python programming language. In several realistic use cases, the proposed system performs equivalent to or better than the state of the art in SPARQL querying on building models. For large datasets, the proposed storage format results in files that are 2–3 times smaller than the current, most concise, RDF databases while offering a platform-neutral, containerized exchange file.

SPANG: a SPARQL client supporting generation and reuse of queries for distributed RDF databases

BackgroundToward improved interoperability of distributed biological databases, an increasing number of datasets have been published in the standardized Resource Description Framework (RDF). Although the powerful SPARQL Protocol and RDF Query Language (SPARQL) provides a basis for exploiting RDF databases, writing SPARQL code is burdensome for users including bioinformaticians. Thus, an easy-to-use interface is necessary.ResultsWe developed SPANG, a SPARQL client that has unique features for querying RDF datasets. SPANG dynamically generates typical SPARQL queries according to specified arguments. It can also call SPARQL template libraries constructed in a local system or published on the Web. Further, it enables combinatorial execution of multiple queries, each with a distinct target database. These features facilitate easy and effective access to RDF datasets and integrative analysis of distributed data.ConclusionsSPANG helps users to exploit RDF datasets by generation and reuse of SPARQL queries through a simple interface. This client will enhance integrative exploitation of biological RDF datasets distributed across the Web. This software package is freely available at http://purl.org/net/spang.

The SBOL Stack: A Platform for Storing, Publishing, and Sharing Synthetic Biology Designs.

Recently, synthetic biologists have developed the Synthetic Biology Open Language (SBOL), a data exchange standard for descriptions of genetic parts, devices, modules, and systems. The goals of this standard are to allow scientists to exchange designs of biological parts and systems, to facilitate the storage of genetic designs in repositories, and to facilitate the description of genetic designs in publications. In order to achieve these goals, the development of an infrastructure to store, retrieve, and exchange SBOL data is necessary. To address this problem, we have developed the SBOL Stack, a Resource Description Framework (RDF) database specifically designed for the storage, integration, and publication of SBOL data. This database allows users to define a library of synthetic parts and designs as a service, to share SBOL data with collaborators, and to store designs of biological systems locally. The database also allows external data sources to be integrated by mapping them to the SBOL data model. The SBOL Stack includes two Web interfaces: the SBOL Stack API and SynBioHub. While the former is designed for developers, the latter allows users to upload new SBOL biological designs, download SBOL documents, search by keyword, and visualize SBOL data. Since the SBOL Stack is based on semantic Web technology, the inherent distributed querying functionality of RDF databases can be used to allow different SBOL stack databases to be queried simultaneously, and therefore, data can be shared between different institutes, centers, or other users.

Advanced SPARQL querying in small molecule databases.

BackgroundIn recent years, the Resource Description Framework (RDF) and the SPARQL query language have become more widely used in the area of cheminformatics and bioinformatics databases. These technologies allow better interoperability of various data sources and powerful searching facilities. However, we identified several deficiencies that make usage of such RDF databases restrictive or challenging for common users.ResultsWe extended a SPARQL engine to be able to use special procedures inside SPARQL queries. This allows the user to work with data that cannot be simply precomputed and thus cannot be directly stored in the database. We designed an algorithm that checks a query against data ontology to identify possible user errors. This greatly improves query debugging. We also introduced an approach to visualize retrieved data in a user-friendly way, based on templates describing visualizations of resource classes. To integrate all of our approaches, we developed a simple web application.ConclusionsOur system was implemented successfully, and we demonstrated its usability on the ChEBI database transformed into RDF form. To demonstrate procedure call functions, we employed compound similarity searching based on OrChem. The application is publicly available at https://bioinfo.uochb.cas.cz/projects/chemRDF.Graphical

An efficient algorithm for updating regular expression indexes in RDF databases.

The Resource Description Framework (RDF) is widely used for sharing biomedical data, such as gene ontology or the online protein database UniProt. SPARQL is a native query language for RDF, featuring regular expressions in queries for which exact values are either irrelevant or unknown. The use of regular expression indexes in SPARQL query processing improves the performance of queries containing regular expressions by up to two orders of magnitude. In this study, we address the update operation for regular expression indexes in RDF databases. We identify major performance problems of straightforward index update algorithms and propose a new algorithm that utilises unique properties of regular expression indexes to increase performance. Our contributions can be summarised as follows: (1) we propose an efficient update algorithm for regular expression indexes in RDF databases, (2) we build a prototype system for the proposed algorithm in C++ and (3) we conduct extensive experiments demonstrating the improvement of our algorithm over the straightforward approaches by an order of magnitude.

AdaptRDF: adaptive storage management for RDF databases

PurposeThe purpose of this paper is to present a two‐phase approach for designing an efficient tailored but flexible storage solution for resource description framework (RDF) data based on its query workload characteristics.Design/methodology/approachThe approach consists of two phases. The vertical partitioning phase which aims of reducing the number of join operations in the query evaluation process, while the adjustment phase aims to maintain the efficiency of the performance of the query processing by adapting the underlying schema to cope with the dynamic nature of the query workloads.FindingsThe authors perform comprehensive experiments on two real‐world RDF datasets to demonstrate that the approach is superior to the state‐of‐the‐art techniques in this domain.Originality/valueThe main motivation behind the authors' approach is that several benchmarking studies have recently shown that each RDF dataset requires a tailored table schema in order to achieve efficient performance during query processing. None of the previous approaches have considered this limitation.

Processing SPARQL queries with regular expressions in RDF databases

BackgroundAs the Resource Description Framework (RDF) data model is widely used for modeling and sharing a lot of online bioinformatics resources such as Uniprot (dev.isb-sib.ch/projects/uniprot-rdf) or Bio2RDF (bio2rdf.org), SPARQL - a W3C recommendation query for RDF databases - has become an important query language for querying the bioinformatics knowledge bases. Moreover, due to the diversity of users’ requests for extracting information from the RDF data as well as the lack of users’ knowledge about the exact value of each fact in the RDF databases, it is desirable to use the SPARQL query with regular expression patterns for querying the RDF data. To the best of our knowledge, there is currently no work that efficiently supports regular expression processing in SPARQL over RDF databases. Most of the existing techniques for processing regular expressions are designed for querying a text corpus, or only for supporting the matching over the paths in an RDF graph.ResultsIn this paper, we propose a novel framework for supporting regular expression processing in SPARQL query. Our contributions can be summarized as follows. 1) We propose an efficient framework for processing SPARQL queries with regular expression patterns in RDF databases. 2) We propose a cost model in order to adapt the proposed framework in the existing query optimizers. 3) We build a prototype for the proposed framework in C++ and conduct extensive experiments demonstrating the efficiency and effectiveness of our technique.ConclusionsExperiments with a full-blown RDF engine show that our framework outperforms the existing ones by up to two orders of magnitude in processing SPARQL queries with regular expression patterns.

Tracking provenance semantics in heterogeneous execution systems

AbstractDigital artifacts result from complex, heterogeneous work processes involving content management, process execution, and curation. Accordingly, systems for tracking provenance of digital artifacts need to be able to integrate heterogeneous descriptions produced by loosely coupled or independent software components and work processes. In the approach described in this paper, two independently developed execution environments, D2K and CyberIntegrator, were instrumented by their developers to produce process and content descriptions in the form of resource description framework (RDF) statements. Using the open‐source Kowari RDF database, these heterogeneous semantic descriptions were integrated to demonstrate the general applicability of RDF databases to answering provenance‐related queries. The results suggest that the ‘open‐world’ semantic model provided by RDF, and the powerful query languages provided by RDF databases, can be extended to integrate a wide variety of heterogeneous provenance‐related information with minimal investment in new standard API's, metadata formats, and execution environments. Copyright © 2007 John Wiley & Sons, Ltd.

Annotea: an open RDF infrastructure for shared Web annotations

Annotea is a Web-based shared annotation system based on a general-purpose open resource description framework (RDF) infrastructure, where annotations are modeled as a class of metadata. Annotations are viewed as statements made by an author about a Web document. Annotations are external to the documents and can be stored in one or more annotation servers. One of the goals of this project has been to re-use as much existing W3C technology as possible. We have reached it mostly by combining RDF with XPointer, XLink, and HTTP. We have also implemented an instance of our system using the Amaya editor/browser and a generic RDF database, accessible through an Apache HTTP server. In this implementation, the merging of annotations with documents takes place within the client. The paper presents the overall design of Annotea and describes some of the issues we have faced and how we have solved them.