Graph-Based Methods for Ontology Summarization: A Survey

Pouriyeh, Seyedamin; Allahyari, Mehdi; Liu, Qingxia; Cheng, Gong; Arabnia, Hamid R.; Atzori, Maurizio; Kochut, Krys J.

doi:10.1109/aike.2018.00020

Cited by 14 publications

(8 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Document summarisation is divided into two as abstracting and extractive [13]. In extractive summarization, the words or sentences in the text's abstract are retained without being changed, whereas abstractive summarization explains the basic information expressed in the text by creating different words and sentences [24][25]. Summarizations made by human hand are mostly not considered to be extractive.…”

Section: Methodsmentioning

confidence: 99%

CatSumm: Extractive Text Summarization based on Spectral Graph Partitioning and Node Centrality

Uçkan

Hark

Karcı

2021

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

View full text Add to dashboard Cite

In this paper, we introduce CatSumm (Cengiz, Ali, Taner Summarization), a novel method for multi-document document summarisation. The suggested method forms a summarization according to three main steps: Representation of input texts, the main stages of the CatSumm model, and sentence scoring. A Text Processing software, is introduced and used to protect the semantic loyalty between word groups at stage of representation of input texts. Spectral Sentence Clustering (SSC), one of the main stages of the CatSumm model, is the summarization process obtained from the proportional values of the sub graphs obtained after spectral graph segmentation. Obtaining super edges is another of the main stages of the method, with the assumption that sentences with weak values below a threshold value calculated by the standard deviation (SD) cannot be included in the summary. Using the different node centrality methods of the CatSumm approach, it forms the sentence rating phase of the recommended summarising approach, determining the significant nodes and hence significant nodes. Finally, the result of the CatSumm method for the purpose of text summarisation within the in the research was measured ROUGE metrics on the Document Understanding Conference (DUC-2004, DUC-2002 datasets. The presented model produced 44.073%, 53.657%, and 56.513% summary success scores for abstracts of 100, 200 and 400 words, respectively.

show abstract

Section: Methodsmentioning

confidence: 99%

CatSumm: Extractive Text Summarization based on Spectral Graph Partitioning and Node Centrality

Uçkan

Hark

Karcı

2021

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

View full text Add to dashboard Cite

show abstract

“…to return a summary of a knowledge graph in the form of a subset of triples. In this way, they reduce its size and give an insight into what is inside the knowledge graph through a relevant subgraph [73,25]. There exist some summarisation methods that focus on the ontology level.…”

Section: Modelling Solutions To Common Modelling Problemsmentioning

confidence: 99%

Empirical Ontology Design Patterns

Carriero

2024

Studies on the Semantic Web

View full text Add to dashboard Cite

show abstract

“…An ontology snippet distills the most important information from an ontology schema and forms an abridged version [43,42]. Existing methods often represent an ontology schema as a graph, and apply some centrality-based measures to identify the most important terms or axioms as an ontology snippet [34,35]. It is possible to adapt these methods to generate snippets for an RDF dataset because it can be viewed as an RDF graph to process.…”

Section: Snippets For Ontology Schemasmentioning

confidence: 99%

A Framework for Evaluating Snippet Generation for Dataset Search

Wang

Chen

et al. 2019

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Reusing existing datasets is of considerable significance to researchers and developers. Dataset search engines help a user find relevant datasets for reuse. They can present a snippet for each retrieved dataset to explain its relevance to the user's data needs. This emerging problem of snippet generation for dataset search has not received much research attention. To provide a basis for future research, we introduce a framework for quantitatively evaluating the quality of a dataset snippet. The proposed metrics assess the extent to which a snippet matches the query intent and covers the main content of the dataset. To establish a baseline, we adapt four state-of-the-art methods from related fields to our problem, and perform an empirical evaluation based on real-world datasets and queries. We also conduct a user study to verify our findings. The results demonstrate the effectiveness of our evaluation framework, and suggest directions for future research.

show abstract

Graph-Based Methods for Ontology Summarization: A Survey

Cited by 14 publications

References 19 publications

CatSumm: Extractive Text Summarization based on Spectral Graph Partitioning and Node Centrality

CatSumm: Extractive Text Summarization based on Spectral Graph Partitioning and Node Centrality

Empirical Ontology Design Patterns

A Framework for Evaluating Snippet Generation for Dataset Search

Contact Info

Product

Resources

About