Issue Information
Samer NarouzeJ. Brian FowlkesStamatia DestounisMaryam EsmaeilzadehMichelle L. RobbinRichard HoppmannFaium ElastographyGiovanna FerraioliAdam AshJason WagnerCarol B. BensonArchie AlexanderRick FeldJon A. JacobsonAnthony M. VintzileosHarriet J. PaltielFaium SonographyMartin de JongWilliam MiddletonJohn R. EisenbreyLaurence NeedlemanJacques S. AbramowiczAlfred AbuhamadReem Abu-RustumSrikar AdhikariRonald B. AdlerEdward Araujo JúniorJean AyoubGeorge BegaTimothy CanavanAnnabel Chen‐TournouxCharles C. ChurchDouglas ClemHarris L. CohenLeeber CohenJoshua A. CopelJodi S. DasheBenjamin DennisRussell L. DeterGregory DevorePeter M. DoubiletJimmy EspinozaArthur C. FleischerFerdinand FrauscherDeborah FriedmanDiana GaitiniSteven GoldsteinLuís GonçalvesGowthaman GunabushanamDaniel HaunHoward J. HellerEdgar Hernández‐AndradeAbid IrshadKenneth LeeStefanie LeeJianwen LuoGiancarlo MariJohn P. McGahanIsrael MeiznerMilija MijajlovićDouglas L. MillerAllan NadelWilliam D. O’BrienTülin ÖzcanLawrence PlattJoseph PolakDolores PretoriusMatthew D. RifkinMark RondeauHenrietta Kotlus RosenbergNelson RoyallRodrigo RuanoTatjana RundekConstantine SaadehGregory SaboeiroCeleste SheppardDavid M. ShererJay SmithRoya SohaeyIlan E. Timor‐TritschJames W. TsungHaifeng TuMitchell TublinThomas C. WinterXimena WortsmanSimcha YagelMichael YingJames A. ZagzebskiCharlotte HenningsenGlynis HarveyPeter MagnusonCynthia CaeBruce OwensTherese CooperErin HoffmanKelly‐Anne PhillipsHaylea WeissRdms Editor-In-ChiefR. Graham BarrMichael BlaivasFlemming ForsbergWesley LeeMark E. LockhartAndrej LyshchikFaium ThomasMarco AmbrosioDiego RaimondoL. SavelliPaolo SalucciAlessandro ArenaGiulia BorgheseGiulia MattioliIlaria GiaquintoM. ScifoMaria MeriggiolaPaolo CasadioRenato SeracchioliOsman ÇiloğluFeride Fatma GörgülüR. daSilva ChakrJoão CláudioOliveira SantosLaura DaSilva AlvesNicole Pamplona Bueno de AndradeAline RanzolinViegas Brenol
1
Citation
2
Reference
10
Related Paper
Citation Trend
Researchers usually present their publication records (we call citation records in this paper) on publication lists on the Web, which could be an important data source for many applications to collect more publication records than from some digital libraries, such as DBLP. However, it is still not easy to design an algorithm to extract citation records from publication lists because of the diversity of page layouts and citation formats. In this paper, we propose an automatic approach to extract citation records from publication list pages by utilizing two properties. First, citation records are usually represented as nodes at the same level in the DOM tree. Second, citation records in the same page are presented by similar HTML tags. Extensive experiments are conducted to measure the effects of all parameters and system performance. Experiment results show that our approach performs stable and well (with 86.2% of F-measure on average).
Tree (set theory)
Cite
Citations (5)
Citation Recommendation is very interesting research area. Many algorithms and methods are proposed for better citation recommendation. Recently, the growth of information technology is high. So the digital libraries are there such as IEEE Xplore and ACM Digital library. The online publications of research papers and conferences are increasing day by day. This makes citation recommendation is a very challenging one. In this paper, propose a citation recommendation method that uses citation relations and similarity between many other papers. The basic method consists of recommend citations by cross references. If one paper is co-occurred in two or more citing papers, then they are similar to some extent. After that, these citing papers are pairwise compared with their contents to get similarities between them. Here, evaluate the proposed method in real word datasets such as IEEE journals.
Co-citation
Similarity (geometry)
Citation analysis
Cite
Citations (7)
With the tremendous amount of citations available in digital library, how to suggest citations automatically, to meet the information needs of researchers has become an important problem. In this paper, we propose a model which treats citation recommendation as a special retrieval task to address this challenge. First, users provide a target paper with some metadata to our system. Second, the system retrieves a relevant candidate citation set. Then the candidate citations are reranked by well-chosen citation evidence, such as publication time preference, self-citation preference, co-citation preference and publication reputation preference. Especially, various measures are introduced to integrate the evidence. We experimented with the proposed model on an established bibliographic corpus-ACL Anthology Network, the results show that the model is valuable in practice, and citation recommendation can be significantly improved using proposed evidences.
Cite
Citations (14)
OpenURL links provide access to full-text articles from citation databases; however, end users who have found citations outside of library databases must find the full text of their journal articles another way. Many OpenURL link resolvers offer a citation finder service which allows the end user to search for specific known items at the article level. This paper studies the usability of the “citation linker” search from Ex Libris' SFX®. Twenty-one volunteers tested finding known journal articles using both a standard A-to-Z list and SFX' citation linker search. The researchers compare results from the two search interfaces, document the problems found, recommend which interface makes the best default search interface, and suggest improvements to the citation linker interface.
Interface (matter)
Cite
Citations (0)
OpenURL links provide access to full-text articles from citation databases; however, end users who have found citations outside of library databases must find the full text of their journal articles another way. Many OpenURL link resolvers offer a citation finder service which allows the end user to search for specific known items at the article level. This paper studies the usability of the “citation linker” search from Ex Libris' SFX®. Twenty-one volunteers tested finding known journal articles using both a standard A-to-Z list and SFX' citation linker search. The researchers compare results from the two search interfaces, document the problems found, recommend which interface makes the best default search interface, and suggest improvements to the citation linker interface.
Interface (matter)
Cite
Citations (3)
Cite
Citations (46)
This paper highlights the information explosion, the need for bibliographic control, the need for information retrieval tools. Explains the emergence of Citation Index, concept of citation indexing, reasons for citing, its structure (print and electronic versions of Science citation Index and Social Science Citation Index ), and application of citation index. It also discusses the search effectiveness, factors taken into consideration for coverage of journals in citation indexes, Journal Citation Reports, various latest citation products including the Web of Science and limitations of citation indexes.
Citation index
Citation analysis
Cite
Citations (6)
Get PDF Email Share Share with Facebook Tweet This Post on reddit Share with LinkedIn Add to CiteULike Add to Mendeley Add to BibSonomy Get Citation Copy Citation Text , "National Science Foundation, National Academy of Sciences—National Research Council, and American Documentation Institute Jointly Announce Plans to Establish an International Conference on Scientific Information," J. Opt. Soc. Am. 47, 258_3-259 (1957) Export Citation BibTex Endnote (RIS) HTML Plain Text Citation alert Save article
Foundation (evidence)
Research council
Cite
Citations (0)
Relevance
Cite
Citations (5)
As the volume of publications has increased dramatically, an urgent need has developed to assist researchers in locating high-quality, candidate-cited papers from a research repository. Traditional scholarly-recommendation approaches ignore the chronological nature of citation recommendations. In this study, we propose a novel method called "Chronological Citation Recommendation" which assumes initial user information needs could shift while users are searching for papers in different time slices. We model the information-need shifts with two-level modeling: dynamic time-related ranking feature construction and dynamic evolving feature weight training. In more detail, we employed a supervised document influence model to characterize the content "time-varying" dynamics and constructed a novel heterogeneous graph that encapsulates dynamic topic-based information, time-decay paper/topic citation information, and word-based information. We applied multiple meta-paths for different ranking hypotheses which carried different types of information for citation recommendation in various time slices, along with information-need shifting. We also used multiple learning-to-rank models to optimize the feature weights for different time slices to generate the final "Chronological Citation Recommendation" rankings. The use of Chronological Citation Recommendation suggests time-series ranking lists based on initial user textual information need and characterizes the information-need shifting. Experiments on the ACM corpus show that Chronological Citation Recommendation can significantly enhance citation recommendation performance.
Learning to Rank
Feature (linguistics)
Rank (graph theory)
Citation analysis
Cite
Citations (32)