Querying Web-Scale Information Networks Through Bounding Matching Scores

Abstract

Web-scale information networks containing billions of entities are common nowadays. Querying these networks can be modeled as a subgraph matching problem. Since information networks are incomplete and noisy in nature, it is important to discover answers that match exactly as well as answers that are similar to queries. Existing graph matching algorithms usually use graph indices to improve the efficiency of query processing. For web-scale information networks, it may not be feasible to build the graph indices due to the amount of work and the memory/storage required.In this paper, we propose an efficient algorithm for finding the best k answers for a given query without precomputing graph indices. The quality of an answer is measured by a matching score that is computed online. To speed up query processing, we propose a novel technique for bounding the matching scores during the computation. By using bounds, we can efficiently prune the answers that have low qualities without having to evaluate all possible answers. The bounding technique can be implemented in a distributed environment, allowing our approach to efficiently answer the queries on web-scale information networks. We demonstrate the effectiveness and the efficiency of our approach through a series of experiments on real-world information networks. The result shows that our bounding technique can reduce the running time up to two orders of magnitude comparing to an approach that does not use bounds.

Semantic locality‐based approximate knowledge graph query

“…10,17,30,31 A graph similarity search over a large number of graphs was defined in the work of Zheng et al 17 In addition, SAGA 30 presented an extension of graph edit distance to allow some special cases of node gaps, node mismatches and graph structural differences. Moreover, the structure similarity based top-k query was also studied in other works, 13,25,32 which adopts a bound-based index-free graph query method to return the best top-k results.…”

“…Multiple efforts have been devoted to the graph similarity search. The structure similarity‐based approaches are difficult to find the results that have different structures but describe the identical semantic meaning, such as the relations between Audi / Benz and Germany shown in Figure . The semantic similarity‐based approaches use the concept‐level or string edit distance to denote semantics without considering the real knowledge semantics.…”

Semantic Guided and Response Times Bounded Top-k Similarity Search over Knowledge Graphs

“…graph isomorphism SLQ [9] " % % transformation library NeMa [7] " " % structural similarity S4 [19] % " " structural patterns mining p-hom [20] " " % p-homomorphism GraB [11] % " % structural similarity QGA [13] " % "…”

“…As an example, consider that a user wants to find all cars produced in Germany. One can come up with a reasonable graph representation of this query as a query graph G Q , and identify the exact or approximate matches of G Q in a knowledge graph G using graph query models [7]- [11]. Some answers can be returned, such as <BMW 320, assembly, Germany>.…”

Prediction of Best Drug Indications in Healthcare Sector using Classification Algorithm

“…Jiahui Jinyz , Samamon Khemmaratz , Lixin Gaoz , Junzhou Luoy proposed [13]. It proposes an efficient algorithm for finding the best k answers for a given query without precomputing graph indices.…”