Semantic similarity measurement based on knowledge mining: an artificial neural net approach (Record no. 160624)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01680nam a22002057a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | ZW-GwMSU |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20221128111707.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 221128b |||||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | MSU |
| Transcribing agency | MSU |
| Description conventions | rda |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Wenwen, Li |
| Relator term | author |
| 245 ## - TITLE STATEMENT | |
| Title | Semantic similarity measurement based on knowledge mining: an artificial neural net approach |
| Statement of responsibility, etc. | created by Wenwen enwen Li , Robert Raskin and Michael F. Goodchild |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Name of producer, publisher, distributor, manufacturer | Taylor & Francis |
| Date of production, publication, distribution, manufacture, or copyright notice | 2012 |
| 336 ## - CONTENT TYPE | |
| Source | rdacontent |
| Content type term | text |
| Content type code | txt |
| 337 ## - MEDIA TYPE | |
| Source | rdamedia |
| Media type term | unmediated |
| Media type code | n |
| 338 ## - CARRIER TYPE | |
| Source | rdacarrier |
| Carrier type term | volume |
| Carrier type code | nc |
| 440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE | |
| Volume/sequential designation | Volume , number , |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | etween spatial objects. It combines a description logic based knowledge base (an ontology) and a multi-layer neural network to simulate the human process of similarity perception. In the knowledge base, spatial concepts are organized hierarchically and are modelled by a set of features that best represent the spatial, temporal and descriptive attributes of the concepts, such as origin, shape and function. Water body ontology is used as a case study. The neural network was designed and human subjects' rankings on similarity of concept pairs were collected for data training, knowledge mining and result validation. The experiment shows that the proposed method achieves good performance in terms of both correlation and mean standard error analysis in measuring the similarity between neural network prediction and human subject ranking. The application of similarity measurement with respect to improving relevancy ranking of a semantic search engine is introduced at the end. |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1080/13658816.2011.635595 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Library of Congress Classification |
| Koha item type | Journal Article |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Serial Enumeration / chronology | Total Checkouts | Full call number | Date last seen | Copy number | Price effective from | Koha item type | Public note |
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| Library of Congress Classification | Main Library | Main Library | - Special Collections | 26/02/2013 | Vol 26 .No.7-8 pages 1415-1435 | G70.2 INT | 28/11/2022 | SP14366 | 28/11/2022 | Journal Article | For Inhouse use only |