ObjectRank: Authority-Based Keyword Search in Databases

By Andrey Balmin, Vagelis Hristidis, Yannis Papakonstantinou. Very Large Data Bases Conference, 2004

555 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/de96d1fb17d6c3779241fbf90005b92062781457

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Information Systems

In Signal Processing

Citations in the corpus

Fast Random Walk Graph Kernel (2012, background): “There are also many works on measuring the similarity between nodes on the same graph, e.g., random walk with restart [27, 39], sinkaugmented delivered current [8], cycle free effective conductance [22], survivable network [13], direction-aware proximity [38], ObjectRank [2], RelationalRank [11], Si”
Mining significant semantic locations from GPS data (2010, background): “, [4]) and that random walks have been applied to undirected graphs (e.”
From "Think Like a Vertex" to "Think Like a Graph" (2013, methodology): “This category includes algorithms like HITS [13], PageRank [5], and its variations, such as ObjectRank [2].”
HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks (2013, methodology): “ObjectRank [20] applies authority-based ranking to keyword search in l abe ed graphs and PopRank [21] proposes a domain-independent object-level link analysis model.”
Fast Random Walk with Restart and Its Applications (2006, methodology): “Many applications take random walk a nd related methods as the building block, including PageRank [21], personalized PageRank [13], SimRan k [16], neighborhood formulation in bipartite graphs [25], content-based image retrieval [14], cross modal c rrelation discovery [22], the BANKS system [2], Obje”
An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation (2010, background): “Examples include spam detection [23], topic-oriented crawling [24], keyword searching in relational databases [25], and measuring citation impact factor [26].”
Correlating financial time series with micro-blogging activity (2012, background): “[32] extend the “Object Rank Algorithm” [1] to consider different types of vertices.”
Bidirectional Expansion For Keyword Search on Graph Databases (2005, methodology): “We note that ObjectRank [2] also uses a similar in-memory structure to create its indices.”
Finding Top-k Min-Cost Connected Trees in Databases (2007, background): “The recent studies on supporting IR style queries in RDBMS include DBXPlore [1], IR-Style [17], DISCOVER [18], ObjectRank [4], BANKS-I [6], and BANKS-II [25].”
Relational retrieval using a combination of path-constrained random walks (2010, background): “For example, there is much incentive to express the AND relation (Balmin et al. 2004): e.”
ClusCite: effective citation recommendation by information network-based clustering (2014, methodology): “In the DBLP dataset [25], paper ObjectRank [3] (132 citations) got 47 citations from VLDB but only 12 from WWW, while RankSVM [14] (250 citations) obtained only 27 citations from VLDB but 109 from WWW, implying the bias of authority in different interest groups.”
TripleRank: Ranking Semantic Web Data by Tensor Decomposition (2009, methodology): “Thus, ObjectRank lacks the flexibility of the original approach which can be easily applied to arbitrary Semantic Web graphs without such upfront assignments.”
Random walk with restart: fast solutions and applications (2008, methodology): “Many applications take random walk and related methods as the building block, including PageRank [23], personalized PageRank [13], SimRank [15], neighborhood formulation in bipartite graphs [28], content-based image retrieval [14], cross modal correlation discovery [25], the BANKS system [2], Object”
BLINKS: ranked keyword searches on graphs (2007, background): “Focusing on search effectiveness, that is, how well the ranking function satis es users' intention, several papers [16, 2, 13, 23] adopt respective IR-style answer-tree ranking strategies to enhance semantics of answers.”
PathSim (2011, background): “ObjectRank [1] and PopRank [13] first noticed that heterogeneous relationships could affect the random walk, and assigned different propagation factors to each type of object relationship to either derive a revised version of P-PageRank (ObjectRank) or a global PageRank (PopRank).”
PageRank beyond the Web (2014, unknown): “ObjectRank is an interesting type of database query [Balmin et al., 2004].”