Aggregated search

Kopliku, Arlind; Pinel-Sauvagnat, Karen; Boughanem, Mohand

doi:10.1145/2523817

Cited by 55 publications

(5 citation statements)

References 102 publications

(113 reference statements)

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“…A search paradigm adopted to address the issues related to the use of SPARQL is keyword search. Keyword-based methods have gained importance over time both in research and in industry as a paradigm to facilitate the access to structured data (Bast, Buchhold & Haussmann, 2016;Kopliku, Pinel-Sauvagnat & Boughanem, 2014;Yu, Qin & Chang, 2010).…”

Section: Search Over Rdfmentioning

confidence: 99%

Search, access, and explore life science nanopublications on the Web

Giachelle¹,

Dosso²,

Silvello³

2021

PeerJ Computer Science

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Nanopublications are Resource Description Framework (RDF) graphs encoding scientific facts extracted from the literature and enriched with provenance and attribution information. There are millions of nanopublications currently available on the Web, especially in the life science domain. Nanopublications are thought to facilitate the discovery, exploration, and re-use of scientific facts. Nevertheless, they are still not widely used by scientists outside specific circles; they are hard to find and rarely cited. We believe this is due to the lack of services to seek, find and understand nanopublications’ content. To this end, we present the NanoWeb application to seamlessly search, access, explore, and re-use the nanopublications publicly available on the Web. For the time being, NanoWeb focuses on the life science domain where the vastest amount of nanopublications are available. It is a unified access point to the world of nanopublications enabling search over graph data, direct connections to evidence papers, and scientific curated databases, and visual and intuitive exploration of the relation network created by the encoded scientific facts.

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Section: Search Over Rdfmentioning

confidence: 99%

Search, access, and explore life science nanopublications on the Web

Giachelle¹,

Dosso²,

Silvello³

2021

PeerJ Computer Science

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“…The relevance of a vertical depends basically on two factors, the relevance of the documents within the vertical collection and on the user's intent to the vertical (Vertical Intent). For the first one, to make vertical selection decision, various approaches use traditional machine learning techniques to combine different sources of evidence that can be found in Query features (features depend only on the query) [6][7][8][9], in Vertical features (features depend only on the vertical) [2,5,21,22] and in Vertical-Query features (features aim to measure relationships between the vertical and the query, and are therefore unique to the vertical-query pair) [2,7,[22][23][24]. Indeed, among all these works, there are those that integrate the content from a single vertical [3].…”

Section: Related Workmentioning

confidence: 99%

Vertical intent prediction approach based on Doc2vec and convolutional neural networks for improving vertical selection in aggregated search

Achsas¹,

Nfaoui²

2020

IJECE

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Vertical selection is the task of selecting the most relevant verticals to a given query in order to improve the diversity and quality of web search results. This task requires not only predicting relevant verticals but also these verticals must be those the user expects to be relevant for his particular information need. Most existing works focused on using traditional machine learning techniques to combine multiple types of features for selecting several relevant verticals. Although these techniques are very efficient, handling vertical selection with high accuracy is still a challenging research task. In this paper, we propose an approach for improving vertical selection in order to satisfy the user vertical intent and reduce user’s browsing time and efforts. First, it generates query embeddings vectors using the doc2vec algorithm that preserves syntactic and semantic information within each query. Secondly, this vector will be used as input to a convolutional neural network model for increasing the representation of the query with multiple levels of abstraction including rich semantic information and then creating a global summarization of the query features. We demonstrate the effectiveness of our approach through comprehensive experimentation using various datasets. Our experimental findings show that our system achieves significant accuracy. Further, it realizes accurate predictions on new unseen data.

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“…The aggregated search issue has recently emerged within the IR community. Aggregated search attempt to achieve diversity by presenting search results from different information sources, e.g., images, blogs, or videos (Arguello, 2017;Kopliku, Pinel Sauvagnat, & Boughanem, 2014;Lalmas, 2011). The challenge is to identify the subset of heterogeneous documents that best fits the user's need.…”

Section: Future Workmentioning

confidence: 99%

TournaRank: When retrieval becomes document competition

Hubert

Pitarch

Pinel-Sauvagnat

et al. 2018

Information Processing & Management

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Numerous feature-based models have been recently proposed by the information retrieval community. The capability of features to express different relevance facets (query-or docu ment-dependent) can explain such a success story. Such models are most of the time supervised, thus requiring a leaming phase. To leverage the advantages of feature-based representations of documents, we propose TOURNARANK, an unsupervised approach inspired by real-life game and sport competition principles. Documents compete against each other in toumaments using features as evidences of relevanoe. Toumaments are modeled as a sequence of matches, which involve pairs of documents playing in tum their features. Once a toumament is ended, docu ments are ranked according to their number of won matches during the toumament. This principle is generic since it can be applied to any collection type. lt also provides great fl ex ibility since different alternatives can be considered by changing the toumament type, the match rules, the feature set, or the strategies adopted by documents during matches. TOURNAMNK was experimented on several collections to evaluate our mode! in different contexts and to compare it with related approaches such as Learning To Rank and fusion ones: the TREC Robust2004 collection for homogeneous documents, the TREC Web2014 (ClueWeb12) collec tion for heterogeneous web documents, and the LETOR3.0 collection for comparison with su pervised feature-based models. Information retrieval (IR) and ranking are inherently linked. Document ranking implies evaluating the relevance of documents according to a user need, expressed as a query. Pioneering models are based on manually tunable weighting sc hemes and matching functions such as BM25, Tf.Idf, or Cosine measure (Manning, Raghavan, & Schütze, 2008). More recent works intend to automatically determine these two facets of IR models. They mainly rely on a feature based representation of documents. Due to their variety, features used as relevance predictors enable a rich representatio n of potentially heterogeneous documents. The feature based works can fit into two main categories: supervised and unsupervised techniques. On the first hand, supervised approaches, lmown as Learning To Rank (LTR) methods (Tax, Bockting, & Hiemstra, 2015), have been thoroughly investigated in the literature and often offer very competitive performances provided that enough labelled training data are available (Macdonald, Santos, & Ounis, 2013). On the other hand and to the best of our lmowledge, very few works have investigated the use of features in an unsupervised scenario for adhoc retrieval. They have been proposed to fit very particular scenarios using most of the time linear combination of features.

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Aggregated search

Cited by 55 publications

References 102 publications

Search, access, and explore life science nanopublications on the Web

Search, access, and explore life science nanopublications on the Web

Vertical intent prediction approach based on Doc2vec and convolutional neural networks for improving vertical selection in aggregated search

TournaRank: When retrieval becomes document competition

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