AllInOneNews

Liu, King-Lup; Meng, Weiyi; Qiu, Jing; Yu, Clement; Raghavan, Vijay V.; Wu, Zonghuan; Lu, Yiyao; He, Hai; Zhao, Hongkun

doi:10.1145/1247480.1247601

Cited by 22 publications

(3 citation statements)

References 16 publications

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“…It builds on the hypothesis that there is a unique data region that is large, centered, and contains many data records, which are very similar even if they come from different search engines, share a parent node, are clearly separated, and the shape of their left sides is very regular. ViNTs was used to generate wrappers for most of the search engines used in the AllInOneNews [101] metasearch engine The ViNTs algorithm works as follows:…”

Section: Vints: Visual Information and Tag Structurementioning

confidence: 99%

A Survey on Region Extractors from Web Documents

Sleiman

Corchuelo

2013

IEEE Trans. Knowl. Data Eng.

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Extracting information from web documents has become a research area in which new proposals sprout out year after year. This has motivated several researchers to work on surveys that attempt to provide an overall picture of the many existing proposals. Unfortunately, none of these surveys provide a complete picture, because they do not take region extractors into account. These tools are kind of preprocessors, because they help information extractors focus on the regions of a web document that contain relevant information. With the increasing complexity of web documents, region extractors are becoming a must to extract information from many websites. Beyond information extraction, region extractors have also found their way into information retrieval, focused web crawling, topic distillation, adaptive content delivery, mashups, and metasearch engines. In this paper, we survey the existing proposals regarding region extractors and compare them side by side.

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Section: Vints: Visual Information and Tag Structurementioning

confidence: 99%

A Survey on Region Extractors from Web Documents

Sleiman

Corchuelo

2013

IEEE Trans. Knowl. Data Eng.

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“…A conventional web search engine or a centralized information retrieval system cannot explore and index web documents at the same rate that they change [17,40]. Distributed Information Retrieval (DIR) [4], also known as Federated Search (FS) [10,33,34,46], has received much attention over the years as an effective solution to this problem. DIR aggregates distributed information sources (resources) under a single query interface.…”

Section: Introductionmentioning

confidence: 99%

Learning To Rank Resources with GNN

Ergashev

Dragut

Meng

2023

Proceedings of the ACM Web Conference 2023

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As the content on the Internet continues to grow, many new dynamically changing and heterogeneous sources of data constantly emerge. A conventional search engine cannot crawl and index at the same pace as the expansion of the Internet. Moreover, a large portion of the data on the Internet is not accessible to traditional search engines. Distributed Information Retrieval (DIR) is a viable solution to this as it integrates multiple shards (resources) and provides a unified access to them. Resource selection is a key component of DIR systems. There is a rich body of literature on resource selection approaches for DIR. A key limitation of the existing approaches is that they primarily use term-based statistical features and do not generally model resource-query and resource-resource relationships. In this paper, we propose a graph neural network (GNN) based approach to learning-to-rank that is capable of modeling resource-query and resource-resource relationships. Specifically, we utilize a pre-trained language model (PTLM) to obtain semantic information from queries and resources. Then, we explicitly build a heterogeneous graph to preserve structural information of query-resource relationships and employ GNN to extract structural information. In addition, the heterogeneous graph is enriched with resource-resource type of edges to further enhance the ranking accuracy. Extensive experiments on benchmark datasets show that our proposed approach is highly effective in resource selection. Our method outperforms the state-of-the-art by 6.4% to 42% on various performance metrics. CCS CONCEPTS• Information systems → Learning to rank; Combination, fusion and federated search.

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“…Combined, the meta-information goes a long way towards establishing the full semantics for (the meaning of and context around) a system's elements. IntegraL's approach is entirely different from federated search and metasearch [10]. The vast majority of IntegraL links are not found through searching.…”

Section: Introductionmentioning

confidence: 99%