Linked Data Exploration With RDF Surveyor

Vega-Gorgojo, Guillermo; Slaughter, Laura; Zernichow, Bjørn Marius von; Nikolov, Nikolay; Roman, Dumitru

doi:10.1109/access.2019.2956345

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…Among the first such Linked Data browsers the well-known ones are Haystack [19], Disco 6 and Tabulator 7 [20]. Examples of the more recent developments are Phuzzy.link 8 [21] and RDFSurveyor 9 [22].…”

Section: Related Workmentioning

confidence: 99%

Adapting the LodView RDF Browser for Navigation over the Multilingual Linguistic Linked Open Data Cloud

Kirillovich

Nikolaev

2022

2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)

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The paper is dedicated to the use of LodView for navigation over the multilingual Linguistic Linked Open Data cloud. First, we define the class of Pubby-like tools, that LodView belongs to, and clarify the relation of this class to the classes of URI dereferenciation tools, RDF browsers and LOD visualization tools. Second, we reveal several limitations of LodView that impede its use for the designated purpose, and propose improvements to be made for fixing these limitations. These improvements are: 1) resolution of Cyrillic URIs; 2) decoding Cyrillic URIs in Turtle representations of resources; 3) support of Cyrillic literals; 4) user-friendly URLs for RDF representations of resources; 5) support of hash URIs; 6) expanding nested resources; 7) support of RDF collections; 8) pagination of resource property values; and 9) support of L A T E X math notation. Third, we partially implement several of the proposed improvements.

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Section: Related Workmentioning

confidence: 99%

Adapting the LodView RDF Browser for Navigation over the Multilingual Linguistic Linked Open Data Cloud

Kirillovich

Nikolaev

2022

2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)

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“…Yus et al [44] adopted SPARQL-like query language to express user's requests to access local knowledge from the knowledge repositories. To efficiently explore RDF data, Gorgojo et al [17] proposed a tool, named RDF Surveyor, that can deal with massive datasets. Mehmood et al [31] presented an index-based approach, named QPPDs, to answer queries by exploiting linked data from multiple and distributed datasets.…”

Section: B Sparql Generationmentioning

confidence: 99%

Intelligent SPARQL Query Generation for Natural Language Processing Systems

Chen

Lu²,

2021

IEEE Access

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Developing question answering (QA) systems that process natural language is a popular research topic. Conventionally, when QA systems receive a natural language question, they choose useful words or phrases based on their parts-of-speech (POS) tags. In general, words tagged as nouns are mapped to class entities, words tagged as verbs are mapped to property entities, and words tagged as proper nouns are mapped to named entities, although the accuracy of entity type identification remains low. Afterward, the relationship between entity types as RDF types determines the first element to be a pivot word to generate the SPARQL (acronym for SPARQL protocol and RDF query language) query on the basis of the sequences by a specific graph or tree structure, such as dependence tree or directed acyclic graph (DAG). However, the generated SPARQL query is difficult to adapt to the given query request in that the sequences are decided by a fixed structure. Unlike in previous research, SPARQL generation occurs automatically according to the entity type identification and RDF type identification results. This study attempts to design a method that leverages machine learning to learn human experiences in entity type identification as well as RDF-type identification. We approach the problem as a multiclass classification problem and propose a two-stage maximum-entropy Markov model (MEMM). The first stage identifies the entity type and the second identifies the RDF type for the purpose of generating appropriate SPARQL queries to meet the query request. Along with the templates designed for the two-stage MEMM model, we develop an automatic question answering prototype system called QAWizard. The experimental results show that QAWizard outperforms all other systems in question answering when evaluated on Linked Data version 8 (QALD-8) metrics.INDEX TERMS Question answering system (QA), Parts-of-speech (POS), SPARQL query, Maximumentropy Markov model.

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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%

“…RDF surveyor [21]. It can receive any SPARQL endpoint as input, and it offers a class navigation browsing.…”

Section: Comparison With Related Systemsmentioning

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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Linked Data Exploration With RDF Surveyor

Cited by 6 publications

References 22 publications

Adapting the LodView RDF Browser for Navigation over the Multilingual Linguistic Linked Open Data Cloud

Adapting the LodView RDF Browser for Navigation over the Multilingual Linguistic Linked Open Data Cloud

Intelligent SPARQL Query Generation for Natural Language Processing Systems

RDFsim: Similarity-Based Browsing over DBpedia Using Embeddings

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