Towards Interactive Analytics over RDF Graphs

Papadaki, Maria-Evangelia; Spyratos, Nicolas; Tzitzikas, Yannis

doi:10.3390/a14020034

Cited by 13 publications

(9 citation statements)

References 33 publications

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“…These approaches use several techniques for retrieving the best match results, by exploiting Information Retrieval (IR) techniques [18,19], and/or by adapting existing IR systems, like Elasticsearch, to the needs of RDF, for example, see [1,2,20] and others. As regards (c), there are several interactive information access systems, including browsing systems, such as [3,4,21] and also systems that can aid users that are not familiar with query languages to access the RDF knowledge base, for example, faceted search [6,7,13], interactive analytics services [22,23] and also systems for assisting the query building process, such as the system A-Qub [8]. Finally, regarding (d) Natural Language interface systems [24], where the input and output is given in natural language, and it returns short and precise answers, that is, through conversational access and Question Answering systems [25][26][27][28].…”

Section: Access Systems Over Rdfmentioning

confidence: 99%

RDFsim: Similarity-Based Browsing over DBpedia Using Embeddings

2021

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Browsing has been the core access method for the Web from its beginning. Analogously, one good practice for publishing data on the Web is to support dereferenceable URIs, to also enable plain web browsing by users. The information about one URI is usually presented through HTML tables (such as DBpedia and Wikidata pages) and graph representations (by using tools such as LODLive and LODMilla). In most cases, for an entity, the user gets all triples that have that entity as subject or as object. However, sometimes the number of triples is numerous. To tackle this issue, and to reveal similarity (and thus facilitate browsing), in this article we introduce an interactive similarity-based browsing system, called RDFsim, that offers “Parallel Browsing”, that is, it enables the user to see and browse not only the original data of the entity in focus, but also the K most similar entities of the focal entity. The similarity of entities is founded on knowledge graph embeddings; however, the indexes that we introduce for enabling real-time interaction do not depend on the particular method for computing similarity. We detail an implementation of the approach over specific subsets of DBpedia (movies, philosophers and others) and we showcase the benefits of the approach. Finally, we report detailed performance results and we describe several use cases of RDFsim.

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Section: Access Systems Over Rdfmentioning

confidence: 99%

RDFsim: Similarity-Based Browsing over DBpedia Using Embeddings

2021

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“…Several tools and platforms have been developed for exploring and visualizing RDF and linked data 14 – 21 , but a common thread in these systems is the use of a typology to define charts (e.g., bar charts, pie charts, scatter plots). Extensive research in data visualization has illuminated the deeper structure underlying most data graphics wherein graphical primitives known as data marks (e.g., point, line, area, text) have properties that can be encoded through channels (e.g., position, color, size, opacity) by mapping data attributes along discrete or continuous scales 22 , 23 .…”

Section: Introductionmentioning

confidence: 99%

FAIR and Interactive Data Graphics from a Scientific Knowledge Graph

et al. 2022

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Graph databases capture richly linked domain knowledge by integrating heterogeneous data and metadata into a unified representation. Here, we present the use of bespoke, interactive data graphics (bar charts, scatter plots, etc.) for visual exploration of a knowledge graph. By modeling a chart as a set of metadata that describes semantic context (SPARQL query) separately from visual context (Vega-Lite specification), we leverage the high-level, declarative nature of the SPARQL and Vega-Lite grammars to concisely specify web-based, interactive data graphics synchronized to a knowledge graph. Resources with dereferenceable URIs (uniform resource identifiers) can employ the hyperlink encoding channel or image marks in Vega-Lite to amplify the information content of a given data graphic, and published charts populate a browsable gallery of the database. We discuss design considerations that arise in relation to portability, persistence, and performance. Altogether, this pairing of SPARQL and Vega-Lite—demonstrated here in the domain of polymer nanocomposite materials science—offers an extensible approach to FAIR (findable, accessible, interoperable, reusable) scientific data visualization within a knowledge graph framework.

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“…Among the sources of the knowledge graph (KG), source selection methods have been proposed, regardless of its application, in the topic of data integration that is represented in three main approaches: materialization ( Papadaki, Tzitzikas & Spyratos, 2020 ; Papadaki, Spyratos & Tzitzikas, 2021 ), mediation ( Farré, Varga & Almar, 2019 ; Ekaputra et al, 2017 ), and virtual ( Endris et al, 2017 ; Algosaibi, 2021 ). The mediation approach, among these approaches, creates a balance between the cost of storage and time response.…”

Section: Introductionmentioning

confidence: 99%

Schema and content aware classification for predicting the sources containing an answer overcorpusand knowledge graphs

Asadifar

Kahani

Shekarpour

2022

PeerJ Computer Science

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Today, several attempts to manage question answering (QA) have been made in three separate areas: (1) knowledge-based (KB), (2) text-based and (3) hybrid, which takes advantage of both prior areas in extracting the response. On the other hand, in question answering on a large number of sources, source prediction to ensure scalability is very important. In this paper, a method for source prediction is presented in hybrid QA, involving several KB sources and a text source. In a few hybrid methods for source selection, including only one KB source in addition to the textual source, prioritization or heuristics have been used that have not been evaluated so far. Most methods available in source selection services are based on general metadata or triple instances. These methods are not suitable due to the unstructured source in hybrid QA. In this research, we need data details to predict the source. In addition, unlike KB federated methods that are based on triple instances, we use the behind idea of mediated schema to ensure data integration and scalability. Results from evaluations that consider word, triple, and question level information, show that the proposed approach performs well against a few benchmarks. In addition, the comparison of the proposed method with the existing approaches in hybrid and KB source prediction and also QA tasks has shown a significant reduction in response time and increased accuracy.

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Towards Interactive Analytics over RDF Graphs

Cited by 13 publications

References 33 publications

RDFsim: Similarity-Based Browsing over DBpedia Using Embeddings

RDFsim: Similarity-Based Browsing over DBpedia Using Embeddings

FAIR and Interactive Data Graphics from a Scientific Knowledge Graph

Schema and content aware classification for predicting the sources containing an answer overcorpusand knowledge graphs

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