VGStore: A Multimodal Extension to SPARQL for Querying RDF Scene Graph

Semantic Web technology has successfully facilitated many RDF models with rich data representation methods. It also has the potential ability to represent and store multimodal knowledge bases such as multimodal scene graphs. However, most existing query languages, especially SPARQL, barely explore the implicit multimodal relationships like semantic similarity, spatial relations, etc. We first explored this issue by organizing a large-scale scene graph dataset, namely Visual Genome, in the RDF graph database. Based on the proposed RDF-stored multimodal scene graph, we extended SPARQL queries to answer questions containing relational reasoning about color, spatial, etc. Further demo (i.e., VGStore) shows the effectiveness of customized queries and displaying multimodal data.Comment: ISWC 2022 Posters, Demos, and Industry Track

Knowledge discovery from RDF data stored in NoSQL databases

Currently, the existence of large amounts of data suggests the use of tools capable of processing them and facilitate the process of finding new knowledge. The discovery of new facts that were not previously explicit in data can be crucial to decision-making processes. In this article, we present a survey on Semantic Web standards, stores of RDF data (Resource Description Framework) and inference mechanisms available in RDF stores. The main goal is to report how inference can be applied and derive new facts from existing data. For this purpose, we demonstrate inferences obtained from a set of predefined rules over data about scientific publications stored in a NoSQL database designated of MarkLogic.Atualmente, a existência de grandes volumes de dados sugere a utilização de ferramentas capazes de os processar e de facilitar o processo de descoberta de novo conhecimento. A descoberta de novos factos, que não estavam explícitos anteriormente, pode ser crucial para os processos de tomada de decisão. Nesse contexto, este artigo apresenta uma revisão da literatura relevante relativa a normas da Web Semântica, repositórios de dados RDF (Resource Description Framework) e mecanismos de inferência disponíveis em repositórios RDF. O objetivo principal é o de relatar como é que a inferência pode ser aplicada e derivar novos factos a partir dos dados já existentes. Para esse efeito, são apresentadas inferências obtidas a partir de um conjunto de regras pré-definidas sobre dados de publicações científicas armazenados numa base de dados NoSQL designada de MarkLogicFCT - Fundação para a Ciência e a Tecnologia(PTDC/COM-INF/28284/2017 e UID/CEC/00319/2019

GSI: GPU-friendly Subgraph Isomorphism

Subgraph isomorphism is a well-known NP-hard problem that is widely used in many applications, such as social network analysis and query over the knowledge graph. Due to the inherent hardness, its performance is often a bottleneck in various real-world applications. Therefore, we address this by designing an efficient subgraph isomorphism algorithm leveraging features of GPU architecture, such as massive parallelism and memory hierarchy. Existing GPU-based solutions adopt a two-step output scheme, performing the same join process twice in order to write intermediate results concurrently. They also lack GPU architecture-aware optimizations that allow scaling to large graphs. In this paper, we propose a GPU-friendly subgraph isomorphism algorithm, GSI. Different from existing edge join-based GPU solutions, we propose a Prealloc-Combine strategy based on the vertex-oriented framework, which avoids joining-twice in existing solutions. Also, a GPU-friendly data structure (called PCSR) is proposed to represent an edge-labeled graph. Extensive experiments on both synthetic and real graphs show that GSI outperforms the state-of-the-art algorithms by up to several orders of magnitude and has good scalability with graph size scaling to hundreds of millions of edges.Comment: 15 pages, 17 figures, conferenc

A design space for RDF data representations

RDF triplestores' ability to store and query knowledge bases augmented with semantic annotations has attracted the attention of both research and industry. A multitude of systems offer varying data representation and indexing schemes. However, as recently shown for designing data structures, many design choices are biased by outdated considerations and may not result in the most efficient data representation for a given query workload. To overcome this limitation, we identify a novel three-dimensional design space. Within this design space, we map the trade-offs between different RDF data representations employed as part of an RDF triplestore and identify unexplored solutions. We complement the review with an empirical evaluation of ten standard SPARQL benchmarks to examine the prevalence of these access patterns in synthetic and real query workloads. We find some access patterns, to be both prevalent in the workloads and under-supported by existing triplestores. This shows the capabilities of our model to be used by RDF store designers to reason about different design choices and allow a (possibly artificially intelligent) designer to evaluate the fit between a given system design and a query workload

Semanttiset verkkokyselyt: Tiedon louhinnasta tiedon poimintaan

Semanttisilla verkkoteknologioilla (RDF, RDFS, OWL) luodaan käsitteitä määritteleviä sanastoja sekä tuetaan sanastoja hyödyntäviä sovelluksia. Linkitetyn tiedon periaatteita on ollut mahdollista toteuttaa jo hyvän aikaa, mutta verkkodatan koneluettava kuvaaminen RDF-tiedostoilla on edelleen harvinaista julkishallinnollisten toimijoiden ulkopuolella. Semanttisten verkkoteknologioiden mahdollistama verkottuneen tiedon aikakausi on alkanut hiipien ja useimpien huomaamatta. Tässä työssä tutkitaan, kuinka semanttisia verkkoteknologioita voidaan hyödyntää kansalaisia suoraan koskettavissa sovelluskohteissa. Erilaiset kyselytutkimukset liittyen asiakastyytyväisyyteen, työhyvinvointiin ja tieteellisen tutkimusaineiston kokoamiseen ovat yleisiä. Monivalintatehtävä on perinteinen kyselyjen formaatti, joka tekee tilastodatan tuottamisesta helppoa. Tämä karsii kuitenkin vastaajan ilmaisuvoiman minimiin, kun tehtävänä on valita reagointivaihtoehto annettuun väitteeseen. Semanttisilla verkkoteknologioilla voidaan esittää koneluettavaa kuvaustietoa myös kyselyvastauksesta ja tämän voi toteuttaa vastaaja itse. Tällöin vastaajalla on käytössään valmiiden vaihtoehtojen sijasta laaja käsitesanasto, eli ontologia. Tästä valitut asiasanat kertovat monivalintatehtävää tarkemmalla resoluutiolla, mikä on vastaajan suhtautuminen annettuun asiaan. Vapaista tekstisyötteistä etsitään tilastoitavaa dataa sekä toistuvia kaavoja tiedonlouhinnan menetelmillä. Näin on tutkittu muun muassa tieteellisiä artikkeleita ja sosiaalisen median julkaisuja. Semanttisilla verkkoteknologioilla data voidaan koota merkityssisällöltään tunnettuihin kategorioihin jo tiedonkeräämisen vaiheessa. Tässä työssä menettelyä luonnehditaan semanttiseksi verkkokyselyksi. Esimerkkinä käytetään Vaalikone-verkkosovellusta, jossa monivalintatehtävien sijasta vaaliehdokkaat vastaavat kysymyksiin ontologian käsitteistä muodostetuilla argumenteilla. Vastaukset visualisoidaan käsitekarttojen tapaan. Tämän työn tulosten mukaan menetelmä on haasteellinen sovelluksen käytettävyyden näkökulmasta. Toiminta on luonteeltaan käsitteellistä mallintamista, joka on kognitiivisesti vaativaa. Semanttisen verkkokyselyn rakennetta yksinkertaistamalla ja ilmaisunvapautta rajoittamalla kognitiivista kynnystä voidaan kuitenkin madaltaa