ACM Home Page
Please provide us with feedback. Feedback
Browsing fatigue in handhelds: semantic bookmarking spells relief
Full text PdfPdf (644 KB)
Source International World Wide Web Conference archive
Proceedings of the 14th international conference on World Wide Web table of contents
Chiba, Japan
SESSION: Improving the browsing experience table of contents
Pages: 593 - 602  
Year of Publication: 2005
ISBN:1-59593-046-9
Authors
Saikat Mukherjee  Stony Brook University, Stony Brook, NY
I. V. Ramakrishnan  Stony Brook University, Stony Brook, NY
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 8,   Downloads (12 Months): 39,   Citation Count: 2
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1060745.1060832
What is a DOI?

ABSTRACT

Focused Web browsing activities such as periodically looking up headline news, weather reports, etc., which require only selective fragments of particular Web pages, can be made more efficient for users of limited-display-size handheld mobile devices by delivering only the target fragments. Semantic bookmarks provide a robust conceptual framework for recording and retrieving such targeted content not only from the specific pages used in creating the bookmarks but also from any user-specified page with similar content semantics. This paper describes a technique for realizing semantic bookmarks by coupling machine learning with Web page segmentation to create a statistical model of the bookmarked content. These models are used to identify and retrieve the bookmarked content from Web pages that share a common content domain. In contrast to ontology-based approaches where semantic bookmarks are limited to available concepts in the ontology, the learning-based approach allows users to bookmark ad-hoc personalized semantic concepts to effectively target content that fits the limited display of handhelds. User evaluation measuring the effectiveness of a prototype implementation of learning-based semantic bookmarking at reducing browsing fatigue in handhelds is provided.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
2
3
 
4
5
 
6
7
8
9
10
11
12
 
13
14
 
15
16
 
17
M. Dzbor, J. Domingue, and E. Motta. Magpie - towards a semantic web browser. In Intl. Semantic Web Conf. (ISWC), 2003.
18
 
19
J. Heflin, J. A. Hendler, and S. Luke. SHOE: A blueprint for the semantic web. In D. Fensel, J. A. Hendler, H. Lieberman, and W. Wahlster, editors, Spinning the Semantic Web, pages 29--63. MIT Press, 2003.
 
20
 
21
22
23
24
 
25
 
26
N. Milic-Frayling and R. Sommerer. Smartview: Flexible viewing of web page contents. In Intl. World Wide Web Conf. (WWW), 2002.
27
 
28
 
29
S. Mukherjee, G. Yang, and I. Ramakrishnan. Automatic annotation of content-rich html documents: Structural and semantic analysis. In Intl. Semantic Web Conf. (ISWC), 2003.
 
30
B. Popov, A. Kiryakov, A. Kirilov, D. Manov, D. Ognyanoff, and M. Goranov. KIM - semantic annotation platform. In Intl. Semantic Web Conf. (ISWC), 2003.
31
32
33
 
34
T.-L. Wong and W. Lam. Text mining from site invariant and dependent features for information extraction knowledge adaptation. In SIAM Intl. Conf. on Data Mining (SDM), 2004.
35
36
 
37
L. Yi and B. Liu. Web page cleaning for web mining through feature weighting. In Intl. Joint Conf. on Artificial Intelligence (IJCAI), 2003.
38
39
 
40


Collaborative Colleagues:
Saikat Mukherjee: colleagues
I. V. Ramakrishnan: colleagues