RDF Graph Summarization Based on Approximate Patterns

Zneika, Mussab; Lucchese, Claudio; Vodislav, Dan; Kotzinos, Dimitris

doi:10.1007/978-3-319-43862-7_4

Cited by 13 publications

(9 citation statements)

References 23 publications

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“…For example, the user can use direct approaches that extract immediate properties of resources (Christodoulou et al , 2015; Eddamiri et al , 2019), or immediate resources (Paulheim and Fümkranz, 2012), or even walks and paths (Ristoski and Paulheim, 2016; De Vries, 2013). Furthermore, the user could either: extract the incoming and outgoing relations and entities such as Depth Limited Crawling (DLC) (Grimnes et al , 2008; Lösch et al ., 2012; Bach, 2008); extract the frequent patterns from the RDF graph (Čebirić et al , 2015; Zneika et al , 2015; Zneika et al , 2016); extract a sequential pattern mining process (d’Aquin and Jay, 2013) to explore and add additional information from the linked open data; and extract a subgraph by generating a set of logical rules from RDF data and remove triples (Joshi et al , 2012; Joshi et al , 2013; Eddamiri et al , 2018a). …”

Section: Related Workmentioning

confidence: 99%

RDF graph mining for cluster-based theme identification

Eddamiri

Benghabrit

Zemmouri

2020

IJWIS

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Purpose The purpose of this paper is to present a generic pipeline for Resource Description Framework (RDF) graph mining to provide a comprehensive review of each step in the knowledge discovery from data process. The authors also investigate different approaches and combinations to extract feature vectors from RDF graphs to apply the clustering and theme identification tasks. Design/methodology/approach The proposed methodology comprises four steps. First, the authors generate several graph substructures (Walks, Set of Walks, Walks with backward and Set of Walks with backward). Second, the authors build neural language models to extract numerical vectors of the generated sequences by using word embedding techniques (Word2Vec and Doc2Vec) combined with term frequency-inverse document frequency (TF-IDF). Third, the authors use the well-known K-means algorithm to cluster the RDF graph. Finally, the authors extract the most relevant rdf:type from the grouped vertices to describe the semantics of each theme by generating the labels. Findings The experimental evaluation on the state of the art data sets (AIFB, BGS and Conference) shows that the combination of Set of Walks-with-backward with TF-IDF and Doc2vec techniques give excellent results. In fact, the clustering results reach more than 97% and 90% in terms of purity and F-measure, respectively. Concerning the theme identification, the results show that by using the same combination, the purity and F-measure criteria reach more than 90% for all the considered data sets. Originality/value The originality of this paper lies in two aspects: first, a new machine learning pipeline for RDF data is presented; second, an efficient process to identify and extract relevant graph substructures from an RDF graph is proposed. The proposed techniques were combined with different neural language models to improve the accuracy and relevance of the obtained feature vectors that will be fed to the clustering mechanism.

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Section: Related Workmentioning

confidence: 99%

RDF graph mining for cluster-based theme identification

Eddamiri

Benghabrit

Zemmouri

2020

IJWIS

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“…4. Pattern-mining-based approaches [33,14,34], which are based on extracting frequent patterns from the RDF graph, then composing them to build an approximated summary graph.…”

Section: Related Workmentioning

confidence: 99%

“…As we have already mentioned in section 3 the RDF graph summarization could be grouped into four main categories. Based on the results reported in the literature we have chosen three of the most well performing RDF graph summarization algorithms [17,12,33] according to their authors. Our selection of these algorithms was also based on specific properties and features that they demonstrate: (a) they do not require the presence of RDF schema (triples) in order to work properly, (b) they work on both homo-and heterogeneous KBs, (c) they provide statistical information about the available data (which can be used to estimate a query's expected results' size), and (d) they provide a summary graph that is considerably smaller than the original graph.…”

Section: Representative Algorithms For Validating the Quality Frameworkmentioning

confidence: 99%

“…Zneika et al [33,34] present an approach for RDF graph summarization based on mining a set of approximate graph patterns is presented. It aims at extract-ing the best approximate RDF graph patterns that describe the input dataset and it works in three independent steps that are described below.…”

Section: Representative Algorithms For Validating the Quality Frameworkmentioning

confidence: 99%

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Quality metrics for RDF graph summarization

Zneika

Vodislav

Kotzinos

2019

Self Cite

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RDF Graph Summarization pertains to the process of extracting concise but meaningful summaries from RDF Knowledge Bases (KBs) representing as close as possible the actual contents of the KB. RDF Summarization allows for better exploration and visualization of the underlying RDF graphs, optimizstion of queries or query evaluation in multiple steps, better understanding of connections in Linked Datasets and many other applications. In the literature, there are efforts reported presenting algorithms for extracting summaries from RDF KBs. These efforts though provide different results while applied on the same KB, thus a way to compare the produced summaries and decide on their quality, in the form of a quality framework, is necessary. So in this work, we propose a comprehensive Quality Framework for RDF Graph Summarization that would allow a better, deeper and more complete understanding of the quality of the different summaries and facilitate their comparison. We work at two levels: the level of the ideal summary (or ideal schema) of the KB that could be provided by an expert user and the level of the instances contained by the KB. For the first level, we are computing how close the proposed summary is to the ideal solution (when this is available) by computing its precision and recall against the ideal solution. For the second level, we are computing if the existing instances are covered (i.e. can be retrieved) and in what degree by the proposed summary. We use our quality framework to test the results of three of the best RDF Graph Summarization algorithms, when summarizing different (in terms of content) and diverse (in terms of total size and number of instances, classes and predicates) KBs and we present comparative results for them. We conclude this work by discussing these results and the suitability of the proposed quality framework in order to get useful insights for the quality of the presented results.

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“…the set of subgraphs matching the graph pattern. In [22], the authors use FCA in order to summarise RDF graphs. From an RDF graph, they build a formal context where objects are resources and attributes are classes and relations.…”

Section: Related Workmentioning

confidence: 99%

Using Redescriptions and Formal Concept Analysis for Mining Definitions in Linked Data

Reynaud

Toussaint

Napoli

2019

Formal Concept Analysis

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In this article, we compare the use of Redescription Mining (RM) and Association Rule Mining (ARM) for discovering class definitions in Linked Open Data (LOD). RM is aimed at mining alternate descriptions from two datasets related to the same set of individuals. We reuse RM for providing category definitions in DBpedia in terms of necessary and sufficient conditions (NSC). Implications and AR can be jointly used for mining category definitions still in terms of NSC. In this paper, we firstly, recall the basics of redescription mining and make precise the principles of definition discovery. Then we detail a series of experiments carried out on datasets extracted from DBpedia. We analyze the different outputs related to RM and ARM applications, and we discuss the strengths and limitations of both approaches. Finally, we point out possible improvements of the approaches.

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RDF Graph Summarization Based on Approximate Patterns

Cited by 13 publications

References 23 publications

RDF graph mining for cluster-based theme identification

RDF graph mining for cluster-based theme identification

Quality metrics for RDF graph summarization

Using Redescriptions and Formal Concept Analysis for Mining Definitions in Linked Data

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