Scalable aggregate keyword query over knowledge graph

Hu, Xin; Duan, Jiangli; Dang, Depeng

doi:10.1016/j.future.2020.02.011

Cited by 15 publications

(7 citation statements)

References 42 publications

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“…The optimal results obtained by general inverse rule model and the effective prediction of link is attained when the proposed model is applied on the datasets FB15k-237 and WN18RR. The reverse relation in the dataset is achieved from the relevant sub-datasets of FB15k-237 and WN18RR 20 . The information about the dataset is explained in the Table 1.…”

Section: Resultsmentioning

confidence: 99%

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Knowledge graph embedding via the properties of mapping and dynamic matrix

Devi¹,

Kalaivani²

2021

IJST

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Objective:The main objective of this research is constructing a knowledge graph using a superior model that leverage the knowledge through relational mapping properties and the matrix constructed dynamically based on the changes in the relational mapping. Methods: This research work proposes symbolic objects in the knowledge graph that are signified by two vectors, the first vector denotes the entity/relation and the vector denote the matrix which is constructed with the help of mapping properties. The proposed methodology uses only minimum parameters and avoids multiplication processes which make the proposed scheme effective in the large scale graphs. The matrix representation with the assistance of mapping properties reduces the computational complication and the time requires to attain the knowledge is also minimized. Findings: The empirical research on knowledge graph mining gives significant insights and it is applied on benchmark datasets namely FB15K-237 and WN18RR. The performance are evaluated in terms of precision and recall in percentage.

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Section: Resultsmentioning

confidence: 99%

“…The latent factor model (LFM) (20,21) adopts the structures of head entity is orthogonal to the tail entity, whereas the head is mapped in a definite relation space. The scoring function is determined by,…”

Section: Latent Factor Modelmentioning

confidence: 99%

Knowledge graph embedding via the properties of mapping and dynamic matrix

Devi¹,

Kalaivani²

2021

IJST

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“…The method was evaluated using the QALD-6 dataset; it obtained a precision of 0.85 and a recall of 0.59. • Scalable Aggregate Keyword Query over knowledge graph (SAKQ) [25] is another approach for querying RDF data by building query graphs from keywords.…”

Section: • Semantic Interpretation Of User Queries For Questionmentioning

confidence: 99%

“…Other works use the complete RDF graph by applying different techniques for reducing the problem space. For example, PCQA [16] uses customised lexicons to restrict predicates by the type of entities involved in the triple, whereas SAKQ [25] and Semantics-driven Keyword Search over Knowledge Graph [26] create a new version of the original RDF graph by relating only the predicates and the entity types. Although this type of information should be defined in a domain ontology, these approaches are useful when there is no ontology available, or, if available, the ontology does not have the required property domain and range definitions.…”

Section: A Precision Of Semankeymentioning

confidence: 99%

“…The practical problem of this approach is that humans do not usually input complete, well-written questions in natural language. Finally, semantic searchers [17][18][19][20][21][22][23][24][25][26][27][28] use keywords, adapting the most usual method for querying the web. The ambiguity of natural language affects the performance of keyword-based approaches, and a common solution has been to query using a controlled natural language vocabulary with a concrete syntax [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

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Semankey: A Semantics-Driven Approach for Querying RDF Repositories Using Keywords

2021

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The Web of Data aims at linking Internet data repositories. Semantic Web technologies make data easily readable by computer agents, enabling the automation of complex tasks and facilitating data integration. They facilitate the achievement of the Web of Data in which users can query the connected datasets in the search engine style, i.e. by using keywords. However, querying semantic repositories in a friendly way, not requiring the mastering of query languages such as SPARQL, is still a challenging task. In this work, we present Semankey, an approach for the automatic building of SPARQL queries from a list of keywords entered by the user. Semankey identifies semantic entities in the keywords by using a domain ontology to interpret the query meaning and automatically builds a set of queries by connecting the entities through the relationships described in the ontology and by applying query size-based heuristics. The main contributions of Semankey are the use of query filters and the generation of multiple SPARQL queries derived from the different interpretations of the given input, according to the underlying domain ontology. We used the data from the Question Answering over Linked Data challenge for evaluating our approach in different execution modes and for analysing the query trees generated, obtaining a precision of 0.52 and a recall of 0.60 when considering the best answer provided per test case.

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Distributed Storage and Query for Domain Knowledge Graphs

Shan

Shi

et al. 2021

Communications in Computer and Information Science

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The development of knowledge graph needs the support of a vast quantity of data. However, the amount of data increases rapidly is placing increasing demands on machines. Centralized data storage requires high-performance hosts to store data, which is costly and have single point of failure. Distributed data storage can reduce the cost of the machine greatly, and there is no single point of failure, but it has requirements for partition and storage of data collection. In the knowledge storage of specific domain, the way of graph data partition and storage vary from the different domain knowledge. To solve the above problems, a scheme of graph partition and distributed storage for domain-specific knowledge graphs is proposed. The proposed graph partition scheme pays attention to the correlation between the data, and divides the nodes affiliated each other into the same or similar partition. A distributed aggregation storage scheme is designed, which makes full use of cluster performance and solves the problem of data consistency during data insertion and update. The proposed distributed storage scheme based on HBase combines Neo4j to realize visual query effectively. Experimental results show the efficiency and the effectiveness of the proposed method in partition time, the number of edge-cut and update time.

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Scalable aggregate keyword query over knowledge graph

Cited by 15 publications

References 42 publications

Knowledge graph embedding via the properties of mapping and dynamic matrix

Knowledge graph embedding via the properties of mapping and dynamic matrix

Semankey: A Semantics-Driven Approach for Querying RDF Repositories Using Keywords

Distributed Storage and Query for Domain Knowledge Graphs

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