Extracting semantic relations from query logs

By Ricardo A. Baeza-Yates, Alessandro Tiberi. Knowledge Discovery and Data Mining, 2007

378 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3d25371a6f06e992f573aff73a5de89a6d09be5b

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In Artificial Intelligence

In Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

Discovering and understanding word level user intent in Web search queries (2015, background): “One such line of research is on the automatic acquisition of attributes of classes or instances [36, 48, 49, 50, 51, 52, 53, 54, 55].”
Click-through-based cross-view learning for image search (2014, background): “In [1], the authors introduced another vectorial representation for the queries without considering the content information.”
The Technology Behind Personal Digital Assistants: An overview of the system architecture and key components (2017, background): “Recently, there has been a surge of interest in exploiting these knowledge sources, especially the RDF semantic knowledge bases, to reduce the manual work required for expanding conversational systems to cover new domains, intents, or slots [41]–[43], [51] or improving existing experiences [44], [45”
Query Expansion Techniques for Information Retrieval: a Survey (2017, neutral): “For example, reference [19] represents queries in a graph based vector space model (query-click bipartite graph) and analyzes the graph constructed by query logs.”
Protecting privacy when mining and sharing user data (2012, methodology): “While a query click graph is not as rich a dataset as the actual search log, many computations can still be performed on the click graph with results similar to the computations starting with the actual search log [18, 40].”
Data services leveraging Bing's data assets (2016, background): “While there is signiﬁcant work on ﬁnding similar/related queries [3, 4, 12], there is little work on mining synonyms with the above precise deﬁnition of synonymity (i.e., alternate ways of referring to the exact same entity ).”
Clickthrough-based translation models for web search: from word models to phrase models (2010, methodology): “Clickthrough data have been exploited for this purpose [3, 34].”
The query-flow graph: model and applications (2008, background): “Baeza-Yates and Tiberi [2] study a weighted version of the cover graph.”
Mining Query Logs: Turning Search Usage Data into Knowledge (2010, methodology): “Differently from what have been done in the analysis of multitasking in search sessions, Baeza-Yates and Tiberi [19] and Baeza-Yates [21] consider queries as a whole, and study how queries and clicks combined can help in determining relations between queries.”