Graph-based Indexing Method for Searching in RDF Data

Kyu, Khin Myat; Oo, Aung Nway

doi:10.1109/aitc.2019.8920921

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…In addition to supporting chain query and star query, this method also optimizes query time by reducing the memory for connection operations and storing data, thereby minimizing query execution time. On this basis, Kyu et al [11] considered a search method based on RDF data graph structure, which can effectively reduce connection operations. It supported chain and star SPARQL queries, and was optimized for query performance.…”

Section: Related Workmentioning

confidence: 99%

RDF Subgraph Matching by Means of Star Decomposition

Wang¹,

Wang²,

Huang³

et al. 2022

Journal of Internet Technology

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<p>With the continuous development of the network, the scale of RDF data is becoming larger and larger. In the face of large-scale RDF data processing, the traditional database query method has been unable to meet the needs. Due to the limited characteristics of subgraph matching, most existing algorithms often have the phenomenon that many subgraphs are repeatedly traversed during the query process, resulting in a large number of intermediate result sets and low query efficiency. The core problem to be solved is how to efficiently match subgraphs. In order to improve the query efficiency of RDF subgraphs in massive RDF data graphs and solve the problem of repeated calculation of some graphs in the query process of RDF subgraphs, an RDF subgraph query algorithm based on star decomposition is proposed in this paper. The algorithm uses graph structure to decompose RDF subgraphs into stars and uses a custom node cost model to calculate the query order of the star subgraphs. By decomposing, the amount of communication among subgraphs is reduced, and the communication cost for query processing is lowered. Moreover, utilizing the query order for RDF subgraph matching can effectively reduce the generation of intermediate result sets and accelerate the efficiency of subgraph matching. On this basis, the performances of the proposed algorithm and several other widely used algorithms are compared and analyzed on two different datasets. Experiments show that the proposed algorithm has better advantages in database recreation, memory size, and execution efficiency. </p> <p> </p>

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Section: Related Workmentioning

confidence: 99%

RDF Subgraph Matching by Means of Star Decomposition

Wang¹,

Wang²,

Huang³

et al. 2022

Journal of Internet Technology

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“…The first research question (RQ1) examines the accuracy of RFSearch. In this perspective, we compare RFSearch with the state-of-the-art approaches (three graph-based retrieval approaches (G1Search [22], G2Search [23], and G3Search [24]) and two keyword-based retrieval approaches (K1Search [25] and K2Search [26])), respectively. We also compare RFSearch with the two baseline algorithms: random prediction algorithm and zero-rule algorithm.…”

Section: A Research Questionsmentioning

confidence: 99%

Random Forest Based Searching Approach for RDF

Soliman

2020

IEEE Access

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The blend of digital and physical worlds changed the Internet significantly. Accordingly, trends to collect, access, and deliver information have changed over the Web. Such changes raised the problems of information retrieval. Search engines retrieve requested information based on the provided keywords which is not an efficient way for rich information retrieval. Consequently, the fetching of the required information is difficult without understanding the syntax and semantics of the content. The multiple existing approaches to resolve this problem by exploiting linked data and semantic Web techniques. Such approaches serialize the content leveraging the Resource Description Framework (RDF) and process the queries using SPARQL to resolve the problem. However, an exact match between RDF content and query structure is required. Although it improves the keyword-based search, it does not provide probabilistic reasoning to find the relationship accuracy between the query and results. In this perspective, this paper proposes a machine learning (random forest) based approach to predict the fetching status of RDF by treating RDFs' requests as a classification problem. First, we preprocess the RDF to convert them into N-Triples format. Then, a feature vector is constructed for each RDF using the preprocessed RDF. After that, a random forest classifier is trained for the prediction of the fetching status of RDFs. The proposed approach is evaluated on an open-source DBpedia dataset. The 10-fold cross-validation results indicate that the performance of the proposed approach is accurate and surpasses the state-of-the-art.

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“…The authors claim that GSS helps speed up the spoken term detection compared to the traditional method. The authors of [64] proposed a graph-based indexing scheme for Resource Description Framework (RDF) data. RDF is the standard for describing computing resources in a network.…”

Section: Indexingmentioning

confidence: 99%

A Graph-Based Indexing Technique for Efficient Searching in Large Scale Textual Documents

Mohideen¹,

Hatha²

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Graph-based Indexing Method for Searching in RDF Data

Cited by 7 publications

References 6 publications

RDF Subgraph Matching by Means of Star Decomposition

RDF Subgraph Matching by Means of Star Decomposition

Random Forest Based Searching Approach for RDF

A Graph-Based Indexing Technique for Efficient Searching in Large Scale Textual Documents

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