Template Based Semantic Similarity for Security Applications

Aleman-Meza, Boanerges; Halaschek-Wiener, Christian; Sahoo, Satya S.; Sheth, Amit P.; Arpinar, I. Budak

doi:10.1007/11427995_77

Cited by 14 publications

(22 citation statements)

References 1 publication

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“…Table (1) gives all the symbols used in the paper. Following standard notation, we use calligraphic for subgraphs (e.g., Hq, G, H), bold capitals for matrices (e.g., W, A), and an arrow for column vector (e.g., ai).…”

Section: Discussionmentioning

confidence: 99%

“…The former is necessary to estimate our proposed goodness function g(), as shown in equation (1). There are fast algorithms to compute or partially pre-compute the desirable proximity scores ri,j for every pair of nodes (i, j).…”

Section: Preliminaries: Random Walks and Cepsmentioning

confidence: 99%

“…It can be seen that g(Hq, i) is the contribution of node i, k to the total goodness function in Equation (1)…”

Section: Seed-findermentioning

confidence: 99%

“…There has been significant work on inexact graph matching [18,21,10,22,5,1], on matching attributed graphs [21,18,10,3,22,5], and on matching in the single-graph setting [3,22,5,1]. However, there are relatively few algorithms that combine the three to tackle inexact matching in large, attributed graphs [6,22,5,1].…”

Section: Related Workmentioning

confidence: 99%

“…However, there are relatively few algorithms that combine the three to tackle inexact matching in large, attributed graphs [6,22,5,1]. Furthermore, while these algorithms employ various optimizations to mitigate the computational complexity of the problem, they all exhibit super-linear complexity in the worst case.…”

Section: Related Workmentioning

confidence: 99%

See 4 more Smart Citations

Fast best-effort pattern matching in large attributed graphs

Tong

Faloutsos

Gallagher

et al. 2007

Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

221

146

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We focus on large graphs where nodes have attributes, such as a social network where the nodes are labelled with each person's job title. In such a setting, we want to find subgraphs that match a user query pattern. For example, a 'star' query would be, "find a CEO who has strong interactions with a Manager, a Lawyer, and an Accountant, or another structure as close to that as possible". Similarly, a 'loop' query could help spot a money laundering ring.Traditional SQL-based methods, as well as more recent graph indexing methods, will return no answer when an exact match does not exist. Our method can find exact-, as well as near-matches, and it will present them to the user in our proposed 'goodness' order. For example, our method tolerates indirect paths between, say, the 'CEO' and the 'Accountant' of the above sample query, when direct paths do not exist. Its second feature is scalability. In general, if the query has nq nodes and the data graph has n nodes, the problem needs polynomial time complexity O(n nq ), which is prohibitive. Our G-Ray ("Graph X-Ray") method finds high-quality subgraphs in time linear on the size of the data graph.Experimental results on the DLBP author-publication graph (with 356K nodes and 1.9M edges) illustrate both the effectiveness and scalability of our approach. The results agree with our intuition, and the speed is excellent. It takes 4 seconds on average for a 4-node query on the DBLP graph.

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Section: Discussionmentioning

confidence: 99%

Section: Preliminaries: Random Walks and Cepsmentioning

confidence: 99%

“…It can be seen that g(Hq, i) is the contribution of node i, k to the total goodness function in Equation (1)…”

Section: Seed-findermentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Fast best-effort pattern matching in large attributed graphs

Tong

Faloutsos

Gallagher

et al. 2007

Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

221

146

View full text Add to dashboard Cite

show abstract

No-but-semantic-match: computing semantically matched xml keyword search results

et al. 2017

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Users are rarely familiar with the content of a data source they are querying, and therefore cannot avoid using keywords that do not exist in the data source. Traditional systems may respond with an empty result, causing dissatisfaction, while the data source in effect holds semantically related content. In this paper we study this no-but-semantic-match problem on XML keyword search and propose a solution which enables us to present the top-k semantically related results to the user. Our solution involves two steps: (a) extracting semantically related candidate queries from the original query and (b) processing candidate queries and retrieving the top-k semantically related results. Candidate queries are generated by replacement of nonmapped keywords with candidate keywords obtained from an ontological knowledge base. Candidate results are scored using their cohesiveness and their similarity to the original query. Since the number of queries to process can be large, with each result having to be analyzed, we propose pruning techniques to retrieve the top-k results efficiently. We develop two query processing algorithms based on our pruning techniques. Further, we exploit a property of the candidate queries to propose a technique for processing multiple queries in batch, which improves the performance substantially. Extensive experiments on two real datasets verify the effectiveness and efficiency of the proposed approaches.

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‘Instant Noodle Crisis’: understanding tourist and public sentiments towards collective tourist environmentally irresponsible behaviour in a tourist destination in China

Wu,

Xu,

et al. 2023

Current Issues in Tourism

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Template Based Semantic Similarity for Security Applications

Cited by 14 publications

References 1 publication

Fast best-effort pattern matching in large attributed graphs

Fast best-effort pattern matching in large attributed graphs

No-but-semantic-match: computing semantically matched xml keyword search results

‘Instant Noodle Crisis’: understanding tourist and public sentiments towards collective tourist environmentally irresponsible behaviour in a tourist destination in China

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