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117
Smart Query Definition for Content-Based Search in Large Sets of Graphs
, 2010
"... Graphs are used in various application areas such as chemical, social or shareholder network analysis. Finding relevant graphs in large graph databases is thereby an important problem. Such search starts with the definition of the query object. Defining the query graph quickly and effectively so tha ..."
Abstract
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Cited by 4 (2 self)
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Graphs are used in various application areas such as chemical, social or shareholder network analysis. Finding relevant graphs in large graph databases is thereby an important problem. Such search starts with the definition of the query object. Defining the query graph quickly and effectively so
Aging Effects on Query Flow Graphs for Query Suggestion
"... World Wide Web content continuously grows in size and importance. Furthermore, users ask Web search engines to satisfy increasingly disparate information needs. New techniques and tools are constantly developed aimed at assisting users in the interaction with the Web search engine. Query recommender ..."
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Cited by 4 (3 self)
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this hypothesis, we build different query flow graphs from the queries belonging to a large query log of a real-world search engine. Each query flow graph is built on distinct query log segments. Then, we generate recommendations on different sets of queries. Results are assessed both by means of human judgments
TopK Nearest Keyword Search on Large Graphs
"... It is quite common for networks emerging nowadays to have labels or textual contents on the nodes. On such networks, we study the problem of top-k nearest keyword (k-NK) search. In a network G modeled as an undirected graph, each node is attached with zero or more keywords, and each edge is assigned ..."
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Cited by 3 (0 self)
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It is quite common for networks emerging nowadays to have labels or textual contents on the nodes. On such networks, we study the problem of top-k nearest keyword (k-NK) search. In a network G modeled as an undirected graph, each node is attached with zero or more keywords, and each edge
PetroSPIRE: A multi-modal content-based retrieval system for petroleum applications
"... In this paper we present a novel content-based search application for petroleum exploration and production (E&P). The target application is specification of and search for geologically significant features to be extracted from 2-dimensional imagery acquired from oil well bores, in conjunction wi ..."
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with simple objects, form a library of searchable entities that can be used in an operational setting. Both object definition and search are accomplished using a web-based Java client, supporting image and parameter browsing, drag-and-drop query specification, and thumbnail viewing of query results. Initial
Keyword search in graphs: Finding r-cliques
- PVLDB
, 2011
"... ABSTRACT Keyword search over a graph finds a substructure of the graph containing all or some of the input keywords. Most of previous methods in this area find connected minimal trees that cover all the query keywords. Recently, it has been shown that finding subgraphs rather than trees can be more ..."
Abstract
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Cited by 17 (1 self)
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be more useful and informative for the users. However, the current tree or graph based methods may produce answers in which some content nodes (i.e., nodes that contain input keywords) are not very close to each other. In addition, when searching for answers, these methods may explore the whole graph
Query Evaluation with Constant Delay
"... I am grateful to Luc Segoufin who kindly accepted me to be his PhD student. He introduced me to the problem of query enumeration and encouraged me to look for the answers to all the questions that emerged during our collaboration. He was a truly great advisor, always supportive and available for dis ..."
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I am grateful to Luc Segoufin who kindly accepted me to be his PhD student. He introduced me to the problem of query enumeration and encouraged me to look for the answers to all the questions that emerged during our collaboration. He was a truly great advisor, always supportive and available
From Machu Picchu to “rafting the urubamba river”: Anticipating information needs via the Entity-Query Graph
- In Proc. WSDM, 2013
"... We study the problem of anticipating user search needs, based on their browsing activity. Given the current web page p that a user is visiting we want to recommend a small and diverse set of search queries that are relevant to the content of p, but also non-obvious and serendipitous. We introduce a ..."
Abstract
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Cited by 10 (1 self)
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We study the problem of anticipating user search needs, based on their browsing activity. Given the current web page p that a user is visiting we want to recommend a small and diverse set of search queries that are relevant to the content of p, but also non-obvious and serendipitous. We introduce a
Discovering Missing Click-through Query Language Information for Web Search
"... The click-through information in web query logs has been widely used for web search tasks. However, it usually suffers from the data sparseness problem, known as the missing/incomplete click problems, where large volume of pages receive few or no clicks. In this paper, we adapt two language modeling ..."
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further propose combining this content based approach with the random walk approach on the click graph to further reduce click-through sparseness for search. The second approach follows the query expansion method and utilizes the queries and their clicked web pages in the query logs to reconstruct a
Context-based search for 3d models
- In ACM SIGGRAPH Asia 2010 papers
, 2010
"... Figure 1: Scene modeling using a context search. Left: A user modeling a scene places the blue box in the scene and asks for models that belong at this location. Middle: Our algorithm selects models from the database that match the provided neighborhood. Right: The user selects a model from the list ..."
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Cited by 31 (3 self)
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it possible for designers to easily include existing content into new scenes. In this paper, we describe a method for context-based search of 3D scenes. We first downloaded a large set of scene graphs from Google 3D Warehouse. These scene graphs were segmented into individual objects. We also extracted tags
Results 1 - 10
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117
