Graph Compression Strategies for Instance-Focused Semantic Mining

Jiang, Xiaowei; Zhang, Xiang; Gao, Feifei; Pu, Chunan; Wang, Peng

doi:10.1007/978-3-642-54025-7_5

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…RDF summaries are defined in a rather restricted setting in [34]. The authors assume that all subjects and objects are typed, and that each has exactly one type; class and property URIs are not allowed in subject and object position, and no usage is made of possible schema triples.…”

Section: Other Structural Quotient Summariesmentioning

confidence: 99%

Summarizing semantic graphs: a survey

et al. 2018

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The explosion in the amount of the available RDF data has lead to the need to explore, query and understand such data sources. Due to the complex structure of RDF graphs and their heterogeneity, the exploration and understanding tasks are significantly harder than in relational databases, where the schema can serve as a first step toward understanding the structure. Summarization has been applied to RDF data to facilitate these tasks. Its purpose is to extract concise and meaningful information from RDF knowledge bases, representing their content as faithfully as possible. There is no single concept of RDF summary, and not a single but many approaches to build such summaries; each is better suited for some uses, and each presents specific challenges with respect to its construction. This survey is the first to provide a comprehensive survey of summarization method for semantic RDF graphs. We propose a taxonomy of existing works in this area, including also some closely related works developed prior to the adoption of RDF in the data management community; we present the concepts at the core of each approach and outline their main technical aspects and implementation. We hope the survey will help readers understand this scientifically rich area, and identify the most pertinent summarization method for a variety of usage scenarios.

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Section: Other Structural Quotient Summariesmentioning

confidence: 99%

Summarizing semantic graphs: a survey

et al. 2018

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“…The derived ID sequences are treated with Huffman encoding and PPMd 7-zip [359]. Next, Jiang et al [239] propose two schemes. First, they assign a type to each RDF object and subject and then reduce the number of vertices in the RDF graph by grouping and collapsing RDF entities with the same type.…”

Section: Generatingmentioning

confidence: 99%

Survey and Taxonomy of Lossless Graph Compression and Space-Efficient Graph Representations

Besta,

Hoefler

2018

Preprint

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Various graphs such as web or social networks may contain up to trillions of edges. Compressing such datasets can accelerate graph processing by reducing the amount of I/O accesses and the pressure on the memory subsystem. Yet, selecting a proper compression method is challenging as there exist a plethora of techniques, algorithms, domains, and approaches in compressing graphs. To facilitate this, we present a survey and taxonomy on lossless graph compression that is the first, to the best of our knowledge, to exhaustively analyze this domain. Moreover, our survey does not only categorize existing schemes, but also explains key ideas, discusses formal underpinning in selected works, and describes the space of the existing compression schemes using three dimensions: areas of research (e.g., compressing web graphs), techniques (e.g., gap encoding), and features (e.g., whether or not a given scheme targets dynamic graphs). Our survey can be used as a guide to select the best lossless compression scheme in a given setting.

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“…Jiang et.al. [30], Navlakha et al [31], and Tian et al [32] propose to construct instance-focused graph summaries of unweighted graphs by grouping similar nodes and edges to supernodes and super-edges. Although we reuse interesting ideas from these works, our approach is focused towards RDF/S KBs expressing richer semantics than conceptual schemas and XML and single instances.…”

Section: Related Workmentioning

confidence: 99%

Ontology understanding without tears: The summarization approach

Troullinou

Kondylakis

Daskalaki

et al. 2017

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Given the explosive growth in both data size and schema complexity, data sources are becoming increasingly difficult to use and comprehend. Summarization aspires to produce an abridged version of the original data source highlighting its most representative concepts. In this paper, we present an advanced version of the RDF Digest, a novel platform that automatically produces and visualizes high quality summaries of RDF/S Knowledge Bases (KBs). A summary is a valid RDFS graph that includes the most representative concepts of the schema, adapted to the corresponding instances. To construct this graph our algorithm exploits the semantics, the structure of the schema and the distribution of the corresponding data/instances to initially identify the most important nodes. Then we explore how to select the edges connecting these nodes by maximizing either locally or globally the importance of the selected edges. The performed evaluation demonstrates the benefits of our approach and the considerable advantages gained. Furthermore, we present our first steps into enabling summary exploration through extensible summaries.

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Graph Compression Strategies for Instance-Focused Semantic Mining

Cited by 7 publications

References 12 publications

Summarizing semantic graphs: a survey

Summarizing semantic graphs: a survey

Survey and Taxonomy of Lossless Graph Compression and Space-Efficient Graph Representations

Ontology understanding without tears: The summarization approach

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