A Brief Survey of Methods for Analytics over RDF Knowledge Graphs

Papadaki, Maria-Evangelia; Tzitzikas, Yannis; Mountantonakis, Michalis

doi:10.3390/analytics2010004

Cited by 5 publications

(2 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This phase refers to preparing the data in the knowledge graph to make it suitable for applying SP (Papadaki et al, 2023). We have two datasets: the rst one is our knowledge graph, which consists of triples; and the second one is the corresponding Wikipedia text data of the knowledge graph.…”

Section: Phase 1: Preprocessingmentioning

confidence: 99%

Enhancing Knowledge graph with Selectional Preferences

Torabian,

Radaei,

Minaei-Bidgoli

et al. 2023

Preprint

View full text Add to dashboard Cite

Knowledge graphs facilitate the extraction of knowledge from data and provide a comprehensive overview of all knowledge within departments, across departments, and global organizations. To enrich the extracted knowledge, several solutions have been proposed to complete the knowledge graph. This study investigates the effectiveness of using the selectional preferences technique to establish the dependency between triple entities in a knowledge graph. To this end, this paper proposes a three-phase approach, Selectional Preferences Knowledge Graph (SP-KG) to determine the relevance degree of all triple entities in the knowledge graph based on selectional preferences. The results demonstrate that the three-phase approach accurately identifies entity dependencies, which can be applied for knowledge extraction. Furthermore, this approach uses a Persian knowledge graph, which enhances the completeness of Persian language knowledge. Finally, the SP-KG model is evaluated on the SP-10K dataset proposed in state-of-the-art research to prove its accuracy. Similar results for both datasets indicate good performance. Glove and Word2Vec are also used to compare the proposed model with other methods. The accuracy results of the 2-way and 3-way pseudo-disambiguation demonstrate the high quality of the results produced by the proposed model.

show abstract

Section: Phase 1: Preprocessingmentioning

confidence: 99%

Enhancing Knowledge graph with Selectional Preferences

Torabian,

Radaei,

Minaei-Bidgoli

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…2) The Resource Description Framework (RDF) is a framework for representing information in the Web [3]. The core structure of that framework is a set of triples, each consisting of a subject, a predicate and an object [4]. A set of such triples form an RDF graph that can be visualized as a node and directed-arc diagram.…”

Section: Introductionmentioning

confidence: 99%

A Two-Phase Bootstrap Approach to Facilitate Conversion from Text to Semantic Graph

Abd-Elrahem,

El-gamal,

Abd-Elfattah

et al. 2024

Preprint

View full text Add to dashboard Cite

This paper presents a two-phase un-learnable approach to convert a text to its semantic graph. In the first phase: divide and conquer, the input text is divided into small pieces manageable by the available text to graph conversion tool (e.g.: Senna). This phase as such yields a collection of small subgraphs that don’t represent the entire text as a whole, however each individual small subgraph represents a corresponding piece of input text. In the second phase: focus attention, the underlying subgraphs are appended together by making use of a bootstrap algorithm to provide a strongly connected single graph that represents the entire input text. In the two phases, both SRL and RDF are considered and thoroughly explained. Accordingly, the corresponding two algorithms on divide and conquer and focus attention are bootstrapped (for both SRL and RDF), are evaluated and compared. The implementation of the such algorithms has indicated that this approach can be used with advantages of being simple, fast, straightforward and practical, which makes it attractive for those NLP researchers who are interested in converting texts to semantic graphs.

show abstract

Unifying Faceted Search and Analytics over RDF Knowledge Graphs

Papadaki,

Tzitzikas

2024

Knowl Inf Syst

View full text Add to dashboard Cite

A Brief Survey of Methods for Analytics over RDF Knowledge Graphs

Cited by 5 publications

References 84 publications

Enhancing Knowledge graph with Selectional Preferences

Enhancing Knowledge graph with Selectional Preferences

A Two-Phase Bootstrap Approach to Facilitate Conversion from Text to Semantic Graph

Unifying Faceted Search and Analytics over RDF Knowledge Graphs

Contact Info

Product

Resources

About