Indexing Data on the Web: A Comparison of Schema-Level Indices for Data Search

Blume, Till; Scherp, Ansgar

doi:10.1007/978-3-030-59051-2_18

Cited by 4 publications

(8 citation statements)

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“…Neighbor triple. SchemEX [27], SchemEX+U+I [4], ABSTAT [41], LODeX [2], and Loupe [31] summarize vertices s and s based on a common type set and common properties linking to vertices with the same type sets. This means, in order to compute the schema of one vertex s, also the type sets of outgoing neighbors Γ + (s) are required to be equivalent, i. e., we compare neighbor triples.…”

Section: Subgraph Featuresmentioning

confidence: 99%

“…In contrast to SemSets [11], these approaches do not use the neighbor vertex identifiers Γ + (s) but, instead, use the type set T (o) for each o ∈ Γ + (s). SchemEX [27], SchemEX+U+I [4], ABSTAT [41], LODeX [2], and Loupe [31] combine type sets T (s), property sets P (s), and neighbor type sets T (o) using predicate paths (see below). The summary models were developed for data source search and exploration.…”

Section: Subgraph Featuresmentioning

confidence: 99%

“…A predicate path compares via which path of predicates a vertex is connected to neighboring vertices' schema structures. For example, SchemEX [27], SchemEX+U+I [4], ABSTAT [41], LODeX [2], and Loupe [31] consider which property links to which type set. TermPicker [39] follows a different strategy to integrate the schema of neighboring vertices.…”

Section: Subgraph Featuresmentioning

confidence: 99%

“…Structural graph summaries are usually an order of magnitude smaller than the input graph but are equivalent to the original graph regarding the chosen structural features [4]. Structural graph summaries do not include statistical approaches such as sampling or pattern-mining approaches [10].…”

Section: Introductionmentioning

confidence: 99%

“…The problem with this plethora of existing summary models is that each model defines its own data structure that is designed for solving only a specific task [2,11,19,31,33,35,39,41]. Our observation is that the different tasks cannot be sufficiently supported by any of the existing structural graph summaries [4]. Furthermore, it is difficult to compare structural graph summaries, as they were designed, implemented, and evaluated in isolation, for their individual task only and using different queries, datasets, graph models, and metrics.…”

Section: Introductionmentioning

confidence: 99%

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FLUID: A common model for semantic structural graph summaries based on equivalence relations

Blume

Richerby

Scherp

2021

Theoretical Computer Science

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Summarization is a widespread method for handling very large graphs. The task of structural graph summarization is to compute a concise but meaningful synopsis of the key structural information of a graph. As summaries may be used for many different purposes, there is no single concept or model of graph summaries. We have studied existing structural graph summaries for large-scale (semantic) graphs. Despite their different concepts and purposes, we found commonalities in the graph structures they capture. We use these commonalities to provide for the first time a formally defined common model, FLUID (FLexible graph sUmmarIes for Data graphs), that allows us to flexibly define structural graph summaries. FLUID allows graph summaries to be quickly defined, adapted, and compared for different purposes and datasets. To this end, FLUID provides features of structural summarization based on equivalence relations such as distinction of types and properties, direction of edges, bisimulation, and inference. We conduct a detailed complexity analysis of the features provided by FLUID. We show that graph summaries defined with FLUID can be computed in the worst case in time O(n 2 ) w.r.t. n, the number of edges in the data graph. An empirical analysis of large-scale web graphs with billions of edges indicates a typical running time of Θ(n). Based on the formal FLUID model, one can quickly define and modify various structural graph summaries from the literature and beyond.

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