In Situ Insights

Lv, Yuanhua; Fuxman, Ariel

doi:10.1145/2766462.2767696

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…Contextual exploration [13,23] is closely related to several information retrieval problems: entity linking and search, relevance feedback, and content recommendation.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Knowledge Bases for Contextual Entity Exploration

Lee

Fuxman

Zhao

et al. 2015

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Users today are constantly switching back and forth from applications where they consume or create content (such as e-books and productivity suites like Microsoft Office and Google Docs) to search engines where they satisfy their information needs. Unfortunately, though, this leads to a suboptimal user experience as the search engine lacks any knowledge about the content that the user is authoring or consuming in the application. As a result, productivity suites are starting to incorporate features that let the user "explore while they work".Existing work in the literature that can be applied to this problem takes a standard bag-of-words information retrieval approach, which consists of automatically creating a query that includes not only the target phrase or entity chosen by the user but also relevant terms from the context. While these approaches have been successful, they are inherently limited to returning results (documents) that have a syntactic match with the keywords in the query.We argue that the limitations of these approaches can be overcome by leveraging semantic signals from a knowledge graph built from knowledge bases such as Wikipedia. We present a system called Lewis for retrieving contextually relevant entity results leveraging a knowledge graph, and perform a large scale crowdsourcing experiment in the context of an e-reader scenario, which shows that Lewis can outperform the state-of-the-art contextual entity recommendation systems by more than 20% in terms of the MAP score.

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“…Contextual exploration [13,23] is closely related to several information retrieval problems: entity linking and search, relevance feedback, and content recommendation.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Knowledge Bases for Contextual Entity Exploration

Lee

Fuxman

Zhao

et al. 2015

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Self Cite

View full text Add to dashboard Cite

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Processing Long Queries Against Short Text

Zhang

Fan

et al. 2017

ACM Trans. Inf. Syst.

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Many real applications in real-time news stream advertising call for efficient processing of long queries against short text. In such applications, dynamic news feeds are regarded as queries to match against an advertisement (ad) database for retrieving the k most relevant ads. The existing approaches to keyword retrieval cannot work well in this search scenario when queries are triggered at a very high frequency. To address the problem, we introduce new techniques to significantly improve search performance. First, we devise a two-level partitioning for tight upper bound estimation and a lazy evaluation scheme to delay full evaluation of unpromising candidates, which can bring three to four times performance boosting in a database with 7 million ads. Second, we propose a novel rank-aware block-oriented inverted index to further improve performance. In this index scheme, each entry in an inverted list is assigned a rank according to its importance in the ad. Then, we introduce a block-at-a-time search strategy based on the index scheme to support a much tighter upper bound estimation and a very early termination. We have conducted experiments with real datasets, and the results show that the rank-aware method can further improve performance by an order of magnitude.

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In Situ Insights

Cited by 2 publications

References 35 publications

Leveraging Knowledge Bases for Contextual Entity Exploration

Leveraging Knowledge Bases for Contextual Entity Exploration

Processing Long Queries Against Short Text

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