# Top-K nearest keyword search on large graphs

##### Citations

95 citations

### Additional excerpts

...Index Terms—Single-pair shortest path, KNN search, keyword search, road network, index, spatial databases Ç...

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...3.3 G-tree Construction In this section, we present how to construct the G-tree....

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48 citations

### Cites background or methods from "Top-K nearest keyword search on lar..."

...Such queries, known as spatial keyword queries, which find the top-k objects of interest in terms of both spatial proximity and textual relevance to the query, have been extensively studied in recent years [6][13][15][20][21][25][26][27]....

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...Technique Boolean keyword Continuous query Unknown path Static data objects Road network Safe region ROAD[13], G-tree[27], SP-tree[20], FBS[11] – – OA-kNN[21] – – YPK-CNN[24], CPM[16], GMA[17] CkNN[22] UNICONS[5] V∗-Diagram[18], MkSK[23], INS[14] LARC...

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...SP-tree [20] deals with the problem of keyword search on large graphs by introducing a shortest path tree, thus the network distances between results and query are approximated by tree distances....

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40 citations

### Cites background or methods from "Top-K nearest keyword search on lar..."

...(2) Both methods [4, 26] assume that the index can reside in main memory....

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...Given a graph G = (V,E) with vertex set V , and edge set E, the algorithm in [4] incurs a (2 log2 |V | − 1) approximation factor, which can be quite large given large values of |V |, and as shown in [26], the resulting error is significant in their empirical study in real graphs and good solutions can be missed....

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...The authors of [26] point out that the error introduced by the star summary in [4] can be large....

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...Both PMI and pivot-gs were implemented by the authors of [26]....

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...As pointed out in [4] and [26], some keyword queries in a network are generated from a vertex inside the network with an interest of looking for vertices in a near-vicinity of the network....

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39 citations

### Cites background from "Top-K nearest keyword search on lar..."

...The social distance is usually modeled as the shortest distance on the social graph [9], [10], [6], [5], [7]....

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...(1) Social Relevance: The social distance for two vertices v ↔ v′ is adopted as the shortest distance [9], [10], [6], [5]....

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20 citations

### Cites background from "Top-K nearest keyword search on lar..."

...[29] studied the top-k nearest keyword (k-NK) query over a graph....

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...Therefore, the studied problems in [28], [29] are different from that in this paper, and their proposed techniques cannot be directed used for solving our problem in this paper....

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...There are some works to study the variants of graph keyword search [28], [29]....

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##### References

3,267 citations

### "Top-K nearest keyword search on lar..." refers methods in this paper

...We use six metrics for evaluation: hit rate, Spearman’s rho [21], error, query time, index time, and index size....

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944 citations

### "Top-K nearest keyword search on lar..." refers background in this paper

...The answer substructure can be a tree [12, 3, 13, 8, 10, 9], a subgraph [16, 17] or a r-clique [14]....

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...k Interval [1, 1] [2, 3] [4, 5] [6, 6] [7, 8]...

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875 citations

### "Top-K nearest keyword search on lar..." refers background in this paper

...The answer substructure can be a tree [12, 3, 13, 8, 10, 9], a subgraph [16, 17] or a r-clique [14]....

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852 citations

### "Top-K nearest keyword search on lar..." refers background or methods in this paper

...[2, 6] is processed recursively by invoking partition(EEP(λ), [2, 6], (r, a),CT(λ)), and [7, 20] is processed by the other two child nodes c and b similarly....

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...16 17 19 Interval [1,2] 3 [4,5] 6 [7,10]...

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...We first process edge (r, a) with interval [2, 6], which divides the interval [1, 20] into three parts: [1, 1], [2, 6], and [7, 20]....

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...k Interval [1, 1] [2, 3] [4, 5] [6, 6] [7, 8]...

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...Using the techniques in [2], LCA(u, v) can be found in O(1) time using O(|V |) index space....

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585 citations

### "Top-K nearest keyword search on lar..." refers background in this paper

...The answer substructure can be a tree [12, 3, 13, 8, 10, 9], a subgraph [16, 17] or a r-clique [14]....

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...For the node h, its interval is [10, 18] because the preorder of h on T is 10 and the maximum preorder for all nodes on the subtree rooted at h is 18....

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