Reacher in Users Recommended of Social Data

Hu Yin; Yun Fei Lv; Wei Wei Wang

doi:10.4028/www.scientific.net/AMM.303-306.2416

Paper Titles

Touch Screen Technology and its Driver Design in uCLinux
p.2397

CPS Design about Container Freight Management System
p.2403

A New Security Baseline Reinforcement Method for the Power Information System
p.2407

Implementation of Parallel Lanczos Method for Intrusion Detection with Cloud Technologies
p.2412

Reacher in Users Recommended of Social Data
p.2416

Study on Security Problems of the Internet of Things
p.2425

Task Scheduling Algorithm Based on Improved Min-Min Algorithm in Cloud Computing Environment
p.2429

The Analysis of the Government Information Sharing Based on the Game Theory
p.2433

The Ranking of Deep Web Sources Based on Data Quality
p.2437

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 303-306Reacher in Users Recommended of Social Data

Reacher in Users Recommended of Social Data

Abstract:

We discuss some key techniques associated with integrating user social data recommendation into entity search engine, which can provide entity search engine more accurate information and make up for automatically fetching information on Web. The goal of social data recommendation is to make search engine become a content provider, and solve some challenges that traditional architecture of search engine has faced with, such as limited resources, accurate search, etc. To this end, we describe the storage format of the user social recommended data and submission methods for them. For the purpose of fusing this structural information into entity search engine, we present formal definitions related to Web entity fusion, and give several important fusion operators, and discuss their properties. Finally, we propose a Web entity fusion algorithm, which exploits some techniques related to natural language processing such as sentence similarity computation and sentence fusion. Our experimental results show that the proposed algorithms are effective.

You might also be interested in these eBooks

Sensors, Measurement and Intelligent Materials

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 303-306)

Pages:

2416-2424

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.303-306.2416

Citation:

Cite this paper

Online since:

February 2013

Authors:

Hu Yin, Yun Fei Lv, Wei Wei Wang

Keywords:

Entity Fusion, Entity Modeling, User Recommendation, Web Information Extraction, Web Page Segmentation

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] M. K. Bergman. The deep web: Surfacing hidden value. Technical report, Bright Planet LLC, Dec. (2000).

Google Scholar

[2] Jayant Madhavan, David Ko, Lucja Kot, Vignesh Ganapathy, Alex Rasmussen, Alon Y. Halevy: Google's Deep Web crawl. PVLDB 1(2): 1241-1252 (2008).

DOI: 10.14778/1454159.1454163

Google Scholar

[3] About Google Base, http: /base. google. com/support/bin/answer. py?answer= 59260 &hl=en.

Google Scholar

[4] Jayant Madhavan, Shirley Cohen, Xin Luna Dong, Alon Y. Halevy, Shawn R. Jeffery, David Ko, Cong Yu: Web-Scale Data Integration: You can afford to Pay as You Go. CIDR 2007: 342-350.

Google Scholar

[5] Shawn R. Jeffery, Michael J. Franklin, Alon Y. Halevy: Pay-as-you-go user feedback for dataspace systems. SIGMOD 2008: 847-860.

DOI: 10.1145/1376616.1376701

Google Scholar

[6] Hector Gonzalez, Alon Y. Halevy, Christian S. Jensen, Anno Langen, Jayant Madhavan, Rebecca Shapley, Warren Shen, Jonathan Goldberg-Kidon: Google fusion tables: web-ce.

DOI: 10.1145/1807167.1807286

Google Scholar