Leveraging temporal dynamics of document content in relevance ranking

Elsas, Jonathan L.; Dumais, Susan T.

doi:10.1145/1718487.1718489

Cited by 64 publications

(47 citation statements)

References 24 publications

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“…Dong et al identify breaking news queries by training a learning to rank model with temporal features extracted from a page index such as the time stamp of when the page was created, last updated, or linked to [8]. Elsas et al analyzed the temporal dynamics of content changes in order to rank documents for navigational queries [10]. More related to the topic of query intent analysis, Metzler et al [17] propose to analyse query logs in order to find base queries that are normally qualified by a year, in order to improve search results for implicit year qualified queries.…”

Section: Related Workmentioning

confidence: 99%

Timely Semantics: A Study of a Stream-Based Ranking System for Entity Relationships

Fischer

Blanco

Mika

et al. 2015

The Semantic Web - ISWC 2015

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In recent years, search engines have started presenting se-mantically relevant entity information together with document search results. Entity ranking systems are used to compute recommendations for related entities that a user might also be interested to explore. Typically, this is done by ranking relationships between entities in a semantic knowledge graph using signals found in a data source as well as type annotations on the nodes and links of the graph. However, the process of producing these rankings can take a substantial amount of time. As a result, entity ranking systems typically lag behind real-world events and present relevant entities with outdated relationships to the search term or even outdated entities that should be replaced with more recent relations or entities. This paper presents a study using a real-world stream-processing based implementation of an entity ranking system, to understand the effect of data timeliness on entity rankings. We describe the system and the data it processes in detail. Using a longitudinal case-study, we demonstrate (i) that low-latency, large-scale entity relationship ranking is feasible using moderate resources and (ii) that stream-based entity ranking improves the freshness of related entities while maintaining relevance. Abstract. In recent years, search engines have started presenting semantically relevant entity information together with document search results. Entity ranking systems are used to compute recommendations for related entities that a user might also be interested to explore. Typically, this is done by ranking relationships between entities in a semantic knowledge graph using signals found in a data source as well as type annotations on the nodes and links of the graph. However, the process of producing these rankings can take a substantial amount of time. As a result, entity ranking systems typically lag behind real-world events and present relevant entities with outdated relationships to the search term or even outdated entities that should be replaced with more recent relations or entities. This paper presents a study using a real-world stream-processing based implementation of an entity ranking system, to understand the effect of data timeliness on entity rankings. We describe the system and the data it processes in detail. Using a longitudinal case-study, we demonstrate (i) that low-latency, large-scale entity relationship ranking is feasible using moderate resources and (ii) that stream-based entity ranking improves the freshness of related entities while maintaining relevance.

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Section: Related Workmentioning

confidence: 99%

Timely Semantics: A Study of a Stream-Based Ranking System for Entity Relationships

Fischer

Blanco

Mika

et al. 2015

The Semantic Web - ISWC 2015

View full text Add to dashboard Cite

show abstract

“…A number of ranking models exploiting temporal information have been proposed, including [7,10,33,54,74,87]. In [74], Li and Croft incorporated time into language models, called time-based language models, by assigning a document prior using an exponential decay function of the publication time of document, i.e., the creation date.…”

Section: Related Workmentioning

confidence: 99%

Time-aware approaches to information retrieval

Kanhabua

2012

SIGIR Forum

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In this thesis, we address major challenges in searching temporal document collections. In such collections, documents are created and/or edited over time. Examples of temporal document collections are web archives, news archives, blogs, personal emails and enterprise documents. Unfortunately, traditional IR approaches based on term-matching only can give unsatisfactory results when searching temporal document collections. The reason for this is twofold: the contents of documents are strongly time-dependent, i.e., documents are about events happened at particular time periods, and a query representing an information need can be time-dependent as well, i.e., a temporal query.Our contributions in this thesis are different time-aware approaches within three topics in IR: content analysis, query analysis, and retrieval and ranking models. In particular, we aim at improving the retrieval effectiveness by 1) analyzing the contents of temporal document collections, 2) performing an analysis of temporal queries, and 3) explicitly modeling the time dimension into retrieval and ranking.Leveraging the time dimension in ranking can improve the retrieval effectiveness if information about the creation or publication time of documents is available. In this thesis, we analyze the contents of documents in order to determine the time of non-timestamped documents using temporal language models. We subsequently employ the temporal language models for determining the time of implicit temporal queries, and the determined time is used for re-ranking search results in order to improve the retrieval effectiveness.We study the effect of terminology changes over time and propose an approach to handling terminology changes using time-based synonyms. In addition, we propose different methods for predicting the effectiveness of temporal queries, so that a particular query enhancement technique can be performed to improve the overall performance. When the time dimension is incorporated into ranking, documents will be ranked according to both textual and temporal similarity. In this case, time uncertainty should also be taken into account. Thus, we propose a ranking model that considers the time uncertainty, and improve ranking by combining multiple features using learning-to-rank techniques.Through extensive evaluation, we show that our proposed time-aware approaches outperform traditional retrieval methods and improve the retrieval effectiveness in searching temporal document collections.

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“…In a recent work, Elsas and Dumais (2010) evaluate the relationship between document dynamics and relevance ranking. Using a collection of top ranked web documents, the authors establish a connection between content change patterns and document relevance.…”

Section: Related Workmentioning

confidence: 99%

Term weighting based on document revision history

Nunes

Ribeiro

David

2011

J. Am. Soc. Inf. Sci.

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In real-world information retrieval systems, the underlying document collection is rarely stable or definite. This work is focused on the study of signals extracted from the content of documents at different points in time for the purpose of weighting individual terms in a document. The basic idea behind our proposals is that terms that have existed for a longer time in a document should have a greater weight. We propose four term weighting functions that use each document's history to estimate a current term score. To evaluate this thesis, we conduct three independent experiments using a collection of documents sampled from Wikipedia. In the first experiment we use data from Wikipedia to judge each set of terms. In a second experiment we use an external collection of tags from a popular social bookmarking service as a gold standard. In the third experiment, we crowdsource user judgments to collect feedback on term preference. Across all experiments results consistently support our thesis. We show that temporally aware measures, specifically the proposed revision term frequency and revision term frequency span, outperform a term weighting measure based on raw term frequency alone.

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Leveraging temporal dynamics of document content in relevance ranking

Cited by 64 publications

References 24 publications

Timely Semantics: A Study of a Stream-Based Ranking System for Entity Relationships

Timely Semantics: A Study of a Stream-Based Ranking System for Entity Relationships

Time-aware approaches to information retrieval

Term weighting based on document revision history

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