The Nuclear Science References (NSR) Database and Web Retrieval System

The Nuclear Science References (NSR) database together with its associated Web interface, is the world's only comprehensive source of easily accessible low- and intermediate-energy nuclear physics bibliographic information for more than 200,000 articles since the beginning of nuclear science. The weekly-updated NSR database provides essential support for nuclear data evaluation, compilation and research activities. The principles of the database and Web application development and maintenance are described. Examples of nuclear structure, reaction and decay applications are specifically included. The complete NSR database is freely available at the websites of the National Nuclear Data Center http://www.nndc.bnl.gov/nsr and the International Atomic Energy Agency http://www-nds.iaea.org/nsr.Comment: 16 pages, 5 figure

The NASA Astrophysics Data System: The Search Engine and its User Interface

The ADS Abstract and Article Services provide access to the astronomical literature through the World Wide Web (WWW). The forms based user interface provides access to sophisticated searching capabilities that allow our users to find references in the fields of Astronomy, Physics/Geophysics, and astronomical Instrumentation and Engineering. The returned information includes links to other on-line information sources, creating an extensive astronomical digital library. Other interfaces to the ADS databases provide direct access to the ADS data to allow developers of other data systems to integrate our data into their system. The search engine is a custom-built software system that is specifically tailored to search astronomical references. It includes an extensive synonym list that contains discipline specific knowledge about search term equivalences. Search request logs show the usage pattern of the various search system capabilities. Access logs show the world-wide distribution of ADS users. The ADS can be accessed at http://adswww.harvard.eduComment: 23 pages, 18 figures, 11 table

MapAffil: A bibliographic tool for mapping author affiliation strings to cities and their geocodes worldwide

Bibliographic records often contain author affiliations as free-form text strings. Ideally one would be able to automatically identify all affiliations referring to any particular country or city such as Saint Petersburg, Russia. That introduces several major linguistic challenges. For example, Saint Petersburg is ambiguous (it refers to multiple cities worldwide and can be part of a street address) and it has spelling variants (e.g., St. Petersburg, Sankt-Peterburg, and Leningrad, USSR). We have designed an algorithm that attempts to solve these types of problems. Key components of the algorithm include a set of 24k extracted city, state, and country names (and their variants plus geocodes) for candidate look-up, and a set of 1.1M extracted word n-grams, each pointing to a unique country (or a US state) for disambiguation. When applied to a collection of 12.7M affiliation strings listed in PubMed, ambiguity remained unresolved for only 0.1%. For the 4.2M mappings to the USA, 97.7% were complete (included a city), 1.8% included a state but not a city, and 0.4% did not include a state. A random sample of 300 manually inspected cases yielded six incompletes, none incorrect, and one unresolved ambiguity. The remaining 293 (97.7%) cases were unambiguously mapped to the correct cities, better than all of the existing tools tested: GoPubMed got 279 (93.0%) and GeoMaker got 274 (91.3%) while MediaMeter CLIFF and Google Maps did worse. In summary, we find that incorrect assignments and unresolved ambiguities are rare (< 1%). The incompleteness rate is about 2%, mostly due to a lack of information, e.g. the affiliation simply says “University of Illinois” which can refer to one of five different campuses. A search interface called MapAffil is available from http://abel.lis.illinois.edu/; the full PubMed affiliation dataset and batch processing is available upon request. The longitude and latitude of the geographical city-center is displayed when a city is identified. This not only helps improve geographic information retrieval but also enables global bibliometric studies of proximity, mobility, and other geo-linked data.NIH P01AG039347; NSF 1348742Ope

Special Libraries, Summer 1992

Volume 83, Issue 3https://scholarworks.sjsu.edu/sla_sl_1992/1002/thumbnail.jp

Entity Extraction from Unstructured Data on the Web

A large number of web pages contain information about entities in lists where the lists are represented in textual form. Textual lists contain implicit records of entities. However, the field values of such records cannot easily be separated or extracted by automatic processes. This, therefore, remains a challenging research problem in the literature. Previous studies in the literature relied mainly on probabilistic graph-based models to capture the attributes and the likely structures of implicit records in a list. However, one of the important limitations of existing methods is that the structures of the records in input lists were implicitly encoded via training data which was manually created. This thesis aims to investigate novel techniques to acquire automatically information about entities from implicit records embedded in textual lists on the web. This thesis introduces a self-supervised learning framework which exploits both existing data in a knowledge base and the structural similarity between sequences in lists to build an extraction model automatically. In the proposed framework, initial labels for candidate field values are created and assigned to generate label sequences. Then, the structure of implici

Effect of forename string on author name disambiguation

In author name disambiguation, author forenames are used to decide which name instances are disambiguated together and how much they are likely to refer to the same author. Despite such a crucial role of forenames, their effect on the performance of heuristic (string matching) and algorithmic disambiguation is not well understood. This study assesses the contributions of forenames in author name disambiguation using multiple labeled data sets under varying ratios and lengths of full forenames, reflecting real‐world scenarios in which an author is represented by forename variants (synonym) and some authors share the same forenames (homonym). The results show that increasing the ratios of full forenames substantially improves both heuristic and machine‐learning‐based disambiguation. Performance gains by algorithmic disambiguation are pronounced when many forenames are initialized or homonyms are prevalent. As the ratios of full forenames increase, however, they become marginal compared to those by string matching. Using a small portion of forename strings does not reduce much the performances of both heuristic and algorithmic disambiguation methods compared to using full‐length strings. These findings provide practical suggestions, such as restoring initialized forenames into a full‐string format via record linkage for improved disambiguation performances.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155924/1/asi24298.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155924/2/asi24298_am.pd