QPPDs: Querying Property Paths Over Distributed RDF Datasets

Mehmood, Qaiser; Saleem, Muhammad; Sahay, Ratnesh; Ngomo, Axel-Cyrille Ngonga; d’Aquin, Mathieu

doi:10.1109/access.2019.2930416

Cited by 6 publications

(10 citation statements)

References 27 publications

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“…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. Liu et al [27] translated the natural language query into SPARQL query to represent the query intention as a corresponding query graph.…”

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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Section: B Sparql Generationmentioning

confidence: 99%

Intelligent SPARQL Query Generation for Natural Language Processing Systems

Chen

Lu²,

2021

IEEE Access

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“…The second step enumerates the reachable properties in the network. Algorithmic tools such as RDF chain search [32] and querying property paths over distributed RDF datasets (QPPDs) [33] can be used in this step. In the methodology section, we describe a more efficient, network-focused method, developed specifically according to the nature of RDF datasets.…”

Section: Introductionmentioning

confidence: 99%

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

Honti

Abonyi

2021

Mathematics

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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.

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“…These datasets are interlinked and heterogeneous in nature. Practitioners often need to find the paths between different entities that can lead to various datasets [41]. In such cases, the single graph-based path finding approaches cannot be applied.…”

Section: Introductionmentioning

confidence: 99%

“…One of the most well-know challenge with partial evaluation is the large number of intermediate results. Moreover, complex queries suffer performance bottleneck [41] when evaluating on large-scale distributed RDF graphs. In the context of distributed paths, to assemble these partial paths into complete paths is also a challenge.…”

Section: Introductionmentioning

confidence: 99%

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Efficient distributed path computation on RDF knowledge graphs using partial evaluation

Mehmood

Saleem²,

Jha

et al. 2021

World Wide Web

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A key property of Linked Data is the representation and publication of data as interconnected labelled graphs where different resources linked to each other form a network of meaningful information. Searching these important relationships between resourceswithin single or distributed graphs -can be reduced to a pathfinding or navigation problem, i.e., looking for chains of intermediate nodes. SPARQL1.1, the current standard query language for RDF-based Linked Data defines a construct -called Property Paths (PPs) -to navigate between entities within a single graph. Since Linked Data technologies are naturally aimed at decentralised scenarios, there are many cases where centralising this data is not feasible or even not possible for querying purposes. To address these problems, we propose a SPARQL PP-based graph processing approach -dubbed DpcLD -where users can execute SPARQL PP queries and find paths distributed across multiple, connected graphs exposed as SPARQL endpoints. To execute the distributed path queries we implemented an index-free, cache-based query engine that communicates with a shared algorithm running on each remote endpoint, and computes the distributed paths. In this paper, we highlight the way in which this approach exploits and aggregates partial paths, within a distributed environment, to produce complete results. We perform extensive experiments to demonstrate the performance of our approach on two datasets: One representing 10 million triples from the DBPedia SPARQL benchmark, and another full benchmark dataset corresponding to 124 million triples. We also perform a scalability test of our approach using real-world genomics datasets distributed across multiple endpoints. We compare our distributed approach with other distributed and centralized pathfinding approaches, showing that it outperforms other distributed approaches by orders of magnitude, and provides a good trade-off for cases when the data cannot be centralised.

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QPPDs: Querying Property Paths Over Distributed RDF Datasets

Cited by 6 publications

References 27 publications

Intelligent SPARQL Query Generation for Natural Language Processing Systems

Intelligent SPARQL Query Generation for Natural Language Processing Systems

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

Efficient distributed path computation on RDF knowledge graphs using partial evaluation

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