A Language Modeling Approach for Temporal Information Needs

Berberich, Klaus; Bedathur, Srikanta; Alonso, Omar; Weikum, Gerhard

doi:10.1007/978-3-642-12275-0_5

Cited by 122 publications

(178 citation statements)

References 20 publications

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“…Other studies used crowdsourcing to collect relevance judgments. For example, Berberich et al [14] used Amazon Mechanical Turk to collect queries and relevance assessments.…”

Section: Related Workmentioning

confidence: 99%

Quantifying retrieval bias in Web archive search

et al. 2017

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A Web archive usually contains multiple versions of documents crawled from the Web at different points in time. One possible way for users to access a Web archive is through full-text search systems. However, previous studies have shown that these systems can induce a bias, known as the retrievability bias, on the accessibility of documents in community-collected collections (such as TREC collections). This bias can be measured by analyzing the distribution of the retrievability scores for each document in a collection, quantifying the likelihood of a document's retrieval. We investigate the suitability of retrievability scores in retrieval systems that consider every version of a document in a Web archive as an independent document. We show that the retrievability of documents can vary for different versions of the same document and that retrieval systems induce biases to different extents. We quantify this bias for a retrieval system which is adapted to handle multiple versions of the same document. The retrieval system indexes each version of a document independently, and we refine the search results using two techniques to aggregate similar versions. The first approach is to collapse similar versions of a document based on content similarity. The second approach is to collapse all versions of the same document based on their URLs. In both cases, we found that the degree of bias Universiteit Utrecht, Utrecht, The Netherlands is related to the aggregation level of versions of the same document. Finally, we study the effect of bias across time using the retrievability measure. Specifically, we investigate whether the number of documents crawled in a particular year correlates with the number of documents in the search results from that year. Assuming queries are not inherently temporal in nature, the analysis is based on the timestamps of documents in the search results returned using the retrieval model for all queries. The results show a relation between the number of documents per year and the number of documents retrieved by the retrieval system from that year. We further investigated the relation between the queries' timestamps and the documents' timestamps. First, we split the queries into different time frames using a 1-year granularity. Then, we issued the queries against the retrieval system. The results show that temporal queries indeed retrieve more documents from the assumed time frame. Thus, the documents from the same time frame were preferred by the retrieval system over documents from other time frames.

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“…Other studies used crowdsourcing to collect relevance judgments. For example, Berberich et al [14] used Amazon Mechanical Turk to collect queries and relevance assessments.…”

Section: Related Workmentioning

confidence: 99%

Quantifying retrieval bias in Web archive search

et al. 2017

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show abstract

“…This important subfield of IR has the goal to improve search effectiveness by exploiting temporal information in documents and queries [11,12]. The temporal dimension leads to new challenges in query understanding [13], retrieval models [14,15] as well as temporal indexing [16,17]. However, most temporal indexing approaches treat documents as static texts with a certain validity, which does not account for the dynamics in Web archives as described above.…”

Section: Web Archive Searchmentioning

confidence: 99%

Accessing web archives from different perspectives with potential synergies

Holzmann¹,

Risse²

2017

Researchers, Practitioners and Their Use of the Archived Web

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Abstract. Web archives constitute a valuable source for researchers from many disciplines. However, their sheer size, the typically broad scope and their temporal dimension make them difficult to work with. We have identified three approaches to access and explore Web archives from different perspectives: user-, data-and graph-centric. In this paper we present related works on these three views as well as discuss their relations and potential synergies. Finally, we propose a generic analysis schema that outlines a systematic way to study Web archives by approaching them from different zoom levels corresponding to the three presented views.

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“…publication time of an article) and links associated with an object (in and out edges). To compute temporal similarity of temporal object Ot for XML document we adapted Berberich' T-rank [27] approach by changing the granularity to be objects in an XML document. Given a single www.ijacsa.thesai.org temporal expression qt in query Q, D is the document to be ranked, Berberich [27] equation defined in T-rank, as follows:…”

Section: Temporal Similarity Stmentioning

confidence: 99%

“…To normalize temporal similarity into range [0-1], the score is divided by 2. score t is used to denote the probability of generated query q t and object temporal T intervals which will be defined later. There are two ways to compute score t (q t |o t ): uncertainty-ignore and uncertainty-aware as defined by Berberich [27].…”

Section:   mentioning

confidence: 99%

TX-Kw: An Effective Temporal XML Keyword Search

Bin-Thalab¹,

El-Tazi²,

El-Sharkawi³

2013

IJACSA

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Abstract-Inspired by the great success of information retrieval (IR) style keyword search on the web, keyword search on XML has emerged recently. Existing methods cannot resolve challenges addressed by using keyword search in Temporal XML documents. We propose a way to evaluate temporal keyword search queries over Temporal XML documents. Moreover, we propose a new ranking method based on the time-aware IR ranking methods to rank temporal keyword search queries results. Extensive experiments have been conducted to show the effectiveness of our approach.

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A Language Modeling Approach for Temporal Information Needs

Cited by 122 publications

References 20 publications

Quantifying retrieval bias in Web archive search

Quantifying retrieval bias in Web archive search

Accessing web archives from different perspectives with potential synergies

TX-Kw: An Effective Temporal XML Keyword Search

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