Egocentric Search based on RSS
Kosuke Numa1,2, Ikki Ohmukai1,2, Masahiro Hamasaki1,2, Hideaki Takeda2,1
1
The Graduate University for Advanced Studies,
2-1-2 Hitotsubashi, Chiyoda, Tokyo, Japan{numa, i2k, hamasaki}@grad.nii.ac.jp
2National Institute of Informatics,
2-1-2 Hitotsubashi, Chiyoda, Tokyo, Japan
takeda@nii.ac.jp
Abstract
We propose an egocentric search method which
evaluates the importance of information by thedistance between the user and the information.
Personal networks around the user can be extracted by using Weblog and RSS. We performed an experiment and found this distance isrelated to similarity between documents. We
applied the method to a Weblog editor to support Weblog authoring.

1Introduction

In Semblog project, we propose a personal knowledge
publishing environment for gathering, authoring andpublishing information [Ohmukai et al., 2004]. As a partof the project, we propose a new way of search called
egocentric search, which can support the authoringprocess by collecting related documents.
When writing a document, we often refer to the related documents written by others. With current searchengines (e.g., Google), it is hard to find such subjective
and contextual information. Personalized search may
find better results, but some sort of user profile is required. Designing profiles is difficult and also maintaining profiles requires much effort to users. In order tosolve this problem, we propose egocentric searchmethod. This method uses relationship between the user
and the target information on the personal network
around the user [Ohmukai et al., 2003]. Our method
provides more subjective results than conventional
search methods.

22.1

Egocentric Searchuser’s documents. The search target documents are ranked
by the distance from the user on the network graph. Thesearch results are shown ordered by the distance.

2.2

Site as Person

To realize the egocentric search method, we regard aWeblog site as a representation of a person on the Web.
Generally, it is difficult to distinguish a personal Web
site for each individual user from many Web pages, but
it is much easier with RDF Site Summary (RSS) [RSS,2000]. RSS is becoming popular since Weblog tools
create RSS files automatically.
An RSS summary contains a channel element andsome item elements. In typical RSS files made by Weblog tools, a channel indicates the Weblog site itself,and each item indicates an individual document which iscalled as an entry. Accordingly we can find a parent site
for each entry. RSS files often include the author information with Dublin Core vocabulary [DCMI, 1999].
Thus, we can find a correspondence between the author
and the document.
We extract a personal network from a document
network connected by hyperlinks and TrackBacks [Trottet al., 2002]. Based on Weblog and RSS, it is easy toextract a personal network.

(a) Document Network

Concept of Egocentric Search

In our egocentric search method, we consider the Web as a
document network, and assume a set of documents which
represent a user on the Web. The importance of the target
information is measured by the distance between the user
and the information on the network.
We defined an egocentric network as a local network
around a user extracted by tracing hyperlinks from the(b) Personal Network

Figure 1: Example of Obtained Egocentric Network

3Experiment and Analysis

We performed an experiment to verify a hypothesis thatthe importance of the information for a user is related tothe distance between the user and the information. We
extract 4 egocentric networks by crawling around from 4
Weblogs. Figure 1 shows the example of the obtained
network. Figure 1(a) stands for a document-based network, and (b) shows a person-based network. The network structures are simplified by binding the documents
to the people. The colors of the nodes indicate the distance from the starting Weblog circled in red. Table 1
indicates the numbers of nodes and links in the networks.
Weblog A

Weblog B

Weblog C

links to them if she/he likes. This action will change
her/his egocentric network. Therefore, new search results will be shown. It is expected that the user can brush
up the document and the network incrementally by repeating this process.

Figure 3: System Architecture

Weblog DDocument Person Document Person Document Person Document Person
Nodes

2966

930

106

15

105

163159734

Links23053 2260

1120

27

320

27

79174

2097

Table 1: Numbers of Nodes and Links
in the Obtained Egocentric Networks
We calculated the document similarity between theuser’s entry and each document in the distance n, andaverage the values for each distance. This similarity isdefined by the similarity of appearance of words. It iscalculated by SMART algorithm [Salton et al., 1983].
Figure 2 indicates the correlation between the distanceand the average of document similarity. Closer documents to the starting Weblog tend to be scored higher
similarity. This fact shows that the proposed method canfind suitable information for authoring.

Figure 4: Snapshot of the Weblog Editor5Conclusion

This paper presented the new way of search for Weblogauthoring. We proposed the egocentric search method,which evaluates the importance of the information bythe distance between the user and the information. We
assume a Weblog site as a representation of its author
described with RSS metadata. Personal relationship canbe extracted by tracing hyperlinks and TrackBacks. The
experimental results showed that our method can findsuitable information. We applied the method to theWeblog editor.
References

Figure 2: Relation between Distanceand Averages of Similarity

4Implementation on Weblog Editor

We implement our egocentric search method on a Weblog editor, which finds and shows related contents tothe currently writing document by the method. Figure 3
shows the system architecture. The system consists of 3
parts: (1) crawler which extracts the egocentric network,(2) cache database which stores the surrounding documents, and (3) editor which recommends related contents and posts the edited document to the user’s Weblog
site.
Figure 4 shows the snapshot of the system. The editing field is placed on the left-hand side, and the recommendation field on the right-hand side. The user
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