Bee Hive at Work: Story Tracking Case Study

Návrat, Pavol; Jastrzembska, Lucia; Jelínek, Tomáš

doi:10.1109/wi-iat.2009.244

Cited by 4 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Huang et al gave a novel approach for building an intelligent focused crawler for retrieving E‐commerce information. For the users who are interested in keeping track of a developing story, a beehive model is suggested in Ref . The story can even be reconstructed backward in time.…”

Section: Current Status Of Web Crawlermentioning

confidence: 99%

A survey of Web crawlers for information retrieval

Kumar

Bhatia

Rattan

2017

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Performance of any search engine relies heavily on its Web crawler. Web crawlers are the programs that get webpages from the Web by following hyperlinks. These webpages are indexed by a search engine and can be retrieved by a user query. In the area of Web crawling, we still lack an exhaustive study that covers all crawling techniques. This study follows the guidelines of systematic literature review and applies it to the field of Web crawling. We used the standard procedure of carrying out a systematic literature review on 248 studies from a total of 1488 articles published in 12 leading journals and other premier conferences and workshops. Existing literature about the Web crawler is classified into different key subareas. Each subarea is further divided according to the techniques being used. We analyzed the distribution of various articles using multiple criteria and depicted conclusions. Various studies that use open source Web crawlers are also reported. We have highlighted future areas of research. We call for an increased awareness in various fields of the Web crawler and identify how techniques from other domains can be used for crawling the Web. Limitations and recommendations for future are also discussed. WIREs Data Mining Knowl Discov 2017, 7:e1218. doi: 10.1002/widm.1218 This article is categorized under: Algorithmic Development > Web Mining Fundamental Concepts of Data and Knowledge > Information Repositories Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

show abstract

Section: Current Status Of Web Crawlermentioning

confidence: 99%

A survey of Web crawlers for information retrieval

Kumar

Bhatia

Rattan

2017

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…An event is registered after a cluster reaches a certain size, whereas the cluster will be removed when it becomes older than 5 days. Tracking stories: (Navrat et al, 2009) propose a system with a focused crawler which works on the user side and tracks a story by smartly selecting the links on the article page. The performance on the experimental set was noisy and the system purely relies on the existing links on the page.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 2nd Workshop on Computing News Storylines (CNS 2016)

Caselli

Miller²,

Erp³

et al. 2016

View full text Add to dashboard Cite

at the heart of information sharing. Ever since people began to share their experiences, they have connected them to form narratives. The study of storytelling and the field of literary theory called narratology have developed complex frameworks and models related to various aspects of narrative such as plots structures, narrative embeddings, characters' perspectives, reader response, point of view, narrative voice, narrative goals, and many others. These notions from narratology have been applied mainly in Artificial Intelligence and to model formal semantic approaches to narratives (e.g. Plot Units developed by Lehnert (1981)). In recent years, computational narratology has qualified as an autonomous field of study and research. Narrative has been the focus of a number of workshops and conferences (AAAI Symposia, Interactive Storytelling Conference (ICIDS), Computational Models of Narrative). Furthermore, reference annotation schemes for narratives have been proposed (NarrativeML by Mani (2013)).The workshop aims to bring together researchers from different communities working on representing and extracting narrative structures in news, a text genre popular in NLP research but which has received little attention in research into narrative structure, representation and analysis.Current advances in NLP technology have made it possible to look beyond scenario-driven, atomic extraction of events from single documents and work towards extracting story structures from multiple documents, while these documents are published over time as news streams. Policy makers and information specialists are increasingly in need of tools that support them in finding salient stories in large amounts of information to more effectively implement policies, monitor actions of "big players" in society and check facts. Their tasks often revolve around reconstructing cases either with respect to specific entities (e.g. person or organisations) or events (e.g. the 2016 presidential elections). Storylines represent explanatory schemas that enable us to make better selections of relevant information but also projections for the future. They constitute a huge potential for exploiting news data in an innovative way.Of the 14 submissions we received, 8 were accepted that touch upon different aspects of narrative research in news. Three contributions describe approaches to detect storylines either from news (Brüggermann et al.), from Tweets (Krishnan and Eisenstein), or from news but with metadata added via Twitter (Poghosyan and Ifrim). Besides detecting storylines, different aspects of storylines such as diegesis and point of view are also addressed (Eisenberg and Finlayson). The second topic that is addressed is annotation and representation of storylines (Caselli and Vossen and O'Gorman et al.). Related to this is the analysis of the distribution of narrative schemas in a corpus, which may help further the discussion on corpus creation (Simonson and Davis). Finally, ideas on how to put storylines to use in a newsroom are discussed in Caswel...

show abstract

“…We attempted to approach this problem using the bee hive model [19]. Inspired by the work of [13] we used the concept of energy from agent InfoSpider.…”

Section: Web Storymentioning

confidence: 99%

Bee Hive at Work: Following a Developing Story on the Web

Návrat

Ezzeddine

2010

Artificial Intelligence in Theory and Practice III

Self Cite

View full text Add to dashboard Cite

Problem solving methods, inspired biologically or by nature, are gaining in importance. Their potential for devising new, but also for increasing efficiency of some well-known methods for solving different kinds of problems, is becoming more and more obvious. The behaviour of social insects provides a lot of inspiration. We took inspiration from honey bees, in particular the way they behave in a hive. We proposed a modified model devised for searching parts of the web online. In a series of experiments, we attempted to follow a developing story with the aim to automatically find documents (web pages) bringing news on the story. This service, working as a beehive, delivered documents as expected.

show abstract

Bee Hive at Work: Story Tracking Case Study

Cited by 4 publications

References 17 publications

A survey of Web crawlers for information retrieval

A survey of Web crawlers for information retrieval

Proceedings of the 2nd Workshop on Computing News Storylines (CNS 2016)

Bee Hive at Work: Following a Developing Story on the Web

Contact Info

Product

Resources

About