=Paper=
{{Paper
|id=None
|storemode=property
|title=MI-Search: a Smart Approach for Urban Information Clouding
|pdfUrl=https://ceur-ws.org/Vol-815/paper3.pdf
|volume=Vol-815
|dblpUrl=https://dblp.org/rec/conf/invit/MontanelliC11
}}
==MI-Search: a Smart Approach for Urban Information Clouding==
https://ceur-ws.org/Vol-815/paper3.pdf
MI-Search: a Smart Approach for Urban Information
Clouding1
Stefano Montanelli and Silvana Castano
Università degli Studi di Milano
DICo - Via Comelico, 39 - 20135 Milano
{stefano.montanelli,silvana.castano}@unimi.it
Abstract. In this paper, we present an approach for urban-centered and
calendar-oriented surfing of web contents according to the personal preferences
of a user profile and interests.
Real examples related to the city of Milan are discussed in the paper to illustrate
the technical peculiarities of the proposed approach.
Keywords: Urban Information Clouding, Web Content Classification
1 Introduction
The recent innovations in the field of Web 2.0 and Semantic Web have radically
changed the way that web contents are surfed and explored. On one side, the growing
availability of user-generated contents, like microblogging posts and RSS news,
typical of Web 2.0 and Social Web platforms, has posed the question of how to
effectively handle and index this huge amount of short and rapidly-obsolescent data
[5]. On the other side, the success of the Linked Data paradigm is enforcing the
upcoming data-oriented vision of the Semantic Web, in spite of the conventional
resource-oriented model [1]. The result is that the existing techniques for web content
classification, search, and presentation are actually inadequate to satisfy the user
needs in such a pervasive and highly-dynamic scenario.
In this paper, we present an approach for urban-centered and calendar-oriented
surfing of web contents according to the personal preferences of a user profile and
interests. Such an approach has been developed in the framework of the MI-Search
project co-funded by Regione Lombardia and Fastweb S.p.A..
A distinguishing feature of MI-Search is the capability to go beyond the actual
interoperability problems concerned with the capability to exploit traditional web sites
and spontaneous user comments/posts in an integrated way. This allows to enable a
cloud-based web exploration, where all the available information about a topic/event
of interest are delivered to the user in a comprehensive, intuitive picture. By urban-
centered, we mean the MI-Search is tailored to work on the specific scenario of a
1
This work is funded by Regione Lombardia and Fastweb S.p.A. in the framework of the Dote
in Ricerca project.
18
selected city, such as the city of Milan in our case, with the goal to focus the web
contents to consider on a selected target. By calendar-oriented, we mean that the
events and meetings noted in the personal agenda can be exploited to automatically
select the web contents that can be suggested as potentially interesting for the final
user. Real examples related to the city of Milan are discussed in the paper to illustrate
the technical peculiarities of MI-Search.
2 Overview of MI-Search
The MI-Search approach is shown in Figure 1 and it is characterized by the following
distinguishing features.
Fig. 1. The MI-Search approach
Capability to consider different kinds of web contents in a seamless way. MI-Search is
conceived to deal with contents extracted from different kinds of web resources. In
particular, in MI-Search, we distinguish three different kinds of web resources, that
are tagged resources, microdata resources, and semantic web resources (see the
bottom part of Figure 1). Tagged resources are traditional web resources (i.e., web
pages) and they are characterized by a raw structure with few metadata. Microdata
resources are posts/comments coming from news feeds and microblogging systems
(e.g., Facebook, Twitter posts). A microdata resource is characterized by a short
textual content and a set of metadata/properties, like title, author, and creation date,
that are commonly employed to describe publishing items. Semantic web resources
are instances/individuals coming from RDF(S) knowledge repositories and OWL
ontologies and they are characterized by a structured description composed of a set of
assertions denoting its specification in the web document of origin. MI-Search
successfully supports content interoperability by providing a support repository where
all the considered contents are stored according to a reference data model developed
in the project.
19
Capability to perform a similarity-based aggregation of web contents. The web
contents stored in the support repository are submitted to a matching and
classification process where contents referring to the same argument are first detected
and then aggregated in similarity clusters (see the middle part of Figure 1). A
similarity cluster is defined to collect web resources that can have a different nature,
but are similar in content. In other words, a similarity cluster represents a specific
argument and it contains all the web resources, either tagged, microdata, and semantic
web resource, that refer to that argument.
Capability to tailor the contents to deliver according to the user profile and interests.
The similarity clusters are exploited by the final users during their content surfing
activities. By relying on the user profile/interests, the similarity clusters that are
interesting for the user are selected. This way, MI-Search succeeds in tailoring the
most appropriate information and/or the suggestions to deliver to the user according
to the specific scenario that is currently enforced (see the top part of Figure 1).
The MI-Search project distinguishes two different kinds of user categories: the
personal users and the business users. Personal users are users interested in receiving
information and they exploit the MI-Search technology for obtaining contents and
suggestions about their personal interests and events in the agenda. Business users are
users interested in public events and other possible situations that are suitable for
promoting their business activities. In this respect, the following three main scenarios
have been envisaged in MI-Search:
─ Search-2-me scenario. This is the typical scenario of personal users and it is
triggered when new personal events are planned by the user in the agenda. By
exploiting the user agenda, the MI-Search technology discovers the user
interests and it can provide a complete set of information about a planned event.
In particular, MI-Search retrieves spontaneous information and user-generated
contents related to the considered event, like comments from other similar users
and special user offers joint with the participation to the event. As an example,
we consider an art-exhibition event about the singer Fabrizio de André located in
Milan at Rotonda della Besana. The user plans to visit this exhibition and a
personal event is inserted in the agenda for a certain date. Through specialized
websites (e.g., http://www.fabriziodeandrelamostra.com), MI-Search automati-
cally provides to the user all the available information about the exhibition and
about the singer. Moreover, other information are extracted by the MI-Search
technology from social networks (e.g., Facebook2, Twitter3) to provide
comments of other users that previously visited the exhibition.
─ Me-2-search scenario. This is the typical scenario of business users and it is
triggered by the user when she/he start browsing the available suggestions that
the system provides as potentially interesting opportunities for promoting the
2
http://www.facebook.com/.
3 http://www.twitter.com/.
20
user business. The user can browse a suggestion list of public events that can be
interesting from the business point of view and she/he can decide to insert in the
system a new business offer joint with a suggested event. For example, we
consider a business user that has a sushi restaurant located in Milan, Viale
Montenero (near to Rotonda della Besana). When the user starts browsing the
possible suggestions, the art exhibition about Fabrizio de André at Rotonda della
Besana is retrieved (due to a geo-locality proximity). The business user can
decide to insert in the system a special menu price for the exhibition visitors.
Such an offer will be linked to the art exhibition event and it will be visualized
by personal users that plan to visit the exhibition.
─ Recommend-2-me scenario. This is a basic scenario of personal users and it is
permanently active without requiring any triggering event. The recommend-2-
me scenario is based on the user interests expressed in the personal profile to
suggest events and/or (promotional) initiatives that can be potentially
interesting. In this scenario, the user periodically receives a report with a list of
upcoming events, either public and business events, that match her/his
preferences for possible selection (and subsequent insertion in the personal
agenda). As an example, we consider a personal user who specified an interest
for sushi restaurants in her/his profile. Receiving the periodic report of
interesting upcoming events, the user becomes aware of the special menu price
of the sushi restaurant in Viale Montenero joint with the art exhibition about
Fabrizio de André. The user can decide to visit the exhibition with the goal to
subsequently take advantage of the special sushi offer. A personal event is
inserted in the user agenda to plan the visit and to receive further information
about the event (see the search-2-me scenario).
3 The MI-Search techniques
In the following, we discuss some technical details of MI-Search with special
reference to those aspects of the project that are concerned with interoperability
issues. In particular, web content acquisition and web resource matching and
classification of MI-Search will be presented.
3.1 Web content acquisition
MI-Search is based on a support repository called MI-Search-DB capable of storing all
the different kinds of web contents considered in the project through a uniform
representation. The representation of specific features for event localization, such as
spatial/temporal coordinates, is also enforced in MI-Search-DB. The repository is
implemented as a PostgreSQL relational database, whose ER schema is shown in
Figure 2. In the schema, we note that any kind of considered web content is
represented through the entity Web Content. Web contents are distinguished in events
(entity Event) and resources (entity Resource).
21
Fig. 2. The schema of the MI-Search-DB repository for web content acquisition
Event. Events are classified in public events (entity Public Event), that represent
official initiatives like art exhibitions or concerts, and business events (entity
Business Event) that represent commercial initiatives inserted by business users. An
event is characterized by attributes that describe its temporal frame (i.e., from-date, to-
date, time, and frequency) and other features, like description and price (where
needed). The entity Event is associated with the entities Contact and Location to
represent the different contact-points for the event (e.g., Phone, Facebook page,
Twitter channel) and the geo-coordinates where the event takes place, respectively.
Resource. Resources are web contents acquired from outside the system and they
distinguished in Tagged Resource, Microdata Resource, and Semantic Web Resource
as discussed in Section 2.
Tag. Each web content, either event or resource, is associated with a set of tags (entity
Tag) denoting the keywords that mostly characterize the event/resource. For an event,
the set of tags can be automatically extracted from one or more reference website.
This usually happens with public events. Otherwise, tags can be manually inserted by
the user that inserts the event. This usually happens with business events. For a
resource, the set of tags is automatically extracted from the resource content itself. In
a tagged resource, tags are extracted from bookmarking and social annotation systems
(e.g., Delicious, Flickr). In a microdata resources, tags are extracted from the textual
resource content and from other available metadata/properties, like the title. In a
semantic web resource, tags are extracted from literals, property names, and property
values contained in the RDF/OWL assertions of the resource specification. We note
that, before insertion in the entity Tag, a tag is submitted to a normalization procedure
for word-lemma extraction and for compound-term tokenization [4,7].
Example. In Figure 3, we consider two examples of acquired web contents.
22
Fig. 3. Examples of web resource acquisition
Figure 3(a) shows a RSS post published on a well-known electronic wall about
events planned in the city of Milan (http://blog.milano-italia.it/). This is an example of
public event related to the art-exhibition about Fabrizio de Andrè located at at
Rotonda della Besana. Besides the featuring attributes expected in MI-Search-DB for
a public event, contact and location information are also provided. Figure 3(b) shows
a comment posted on the Facebook social network published by a user that visited the
art-exhibition about Fabrizio de André. This is an example of microdata resource
featured by its URL on the web as expected in MI-Search-DB. Moreover, either the
public event and the microdata resource, are associated with a set of tags
automatically extracted from the two posts as a sort of synthetic characterization of
each web contents.
3.2 Web content matching and classification
The goal of matching and classification in MI-Search is to detect and build the
similarity clusters to use for content delivery to the final users.
Content matching. This step has the goal to evaluate the degree of similarity between
each pair of web contents stored in the MI-Search-DB. Given two web contents wci
and wcj, the similarity coefficient σ(wci,wcj) ∈ [0,1] denotes the level of similarity of
wci and wcj based on their commons tags. We define Tagwc = {tag1, …, tagm} as the
set of tags associated with the web content wc in MI-Search-DB. The similarity
coefficient σ(wci,wcj) is calculated as follows:
2 ∗ | ~ |
,
| | | |
23
where tagx ~ tagy denotes that tagx ∈ Tagwci and tagy ∈ Tagwcj are matching tags
according to a string matching metric that considers the structure of tagx and tagy. For
σ calculation, we employ our matching system HMatch 2.0, where state-of-the-art
metrics for string matching (e.g., I-Sub, Q-Gram, Edit-Distance, and Jaro-Winkler) are
implemented [2].
Content classification. Similarity clusters are built by relying on a clique percolation
method (CPM) [6]. This method receives in input a graph G where nodes are the web
contents stored in the MI-Search-DB repository and edges are established between any
pair (wci, wcj) of similar contents for which σ(wci,wcj) ≥ th (th ∈ (0,1] is a matching
threshold denoting the minimum level of similarity required to consider two web
contents as matching contents). The CPM returns a set of similarity clusters where
each cluster collects a region of nodes in G that are more densely connected to each
other than to the nodes outside the region. The CPM is based on the notion of k-clique
which corresponds to a complete (fully-connected) sub-graph of k nodes within the
graph Gs+. Two k-cliques are defined as adjacent k-cliques if they share k - 1 nodes.
The CPM determines clusters from k-cliques. In particular, a cluster, or more
precisely, a k-clique-cluster, is defined as the union of all k-cliques that can be
reached from each other through a series of adjacent k-cliques. More technical details
about the CPM and the construction of similarity clusters can be found in [3].
Example. We consider the example shown in Figure 3. We call wc1 the public event
of Figure 3(a) and wc2 the microdata resource of Figure 3(b). The similarity
coefficient of wc1 and wc2 is σ(wc1,wc2) = 0.35 due to the matching tags in Tagwc1 and
Tagwc2. With a matching threshold th = 0.3, the web contents wc1 and wc2 are
considered as matching contents and an edge (wc1, wc2) is set in the graph G that is
passed to the CPM method for calculation of the similarity clusters. An example of
similarity cluster is shown in Figure 4. Besides the web contents wc1 and wc2, the
cluster of Figure 4 contains a Flickr image taken from the exhibition (tagged
resource), another Facebook user comment (microdata resource), the Freebase page
about Fabrizio de André (semantic web resource), and the contact information of the
Aoyama restaurant, a sushi restaurant that published in MI-Search a discounted dinner
offer associated with the art-exhibition at Rotonda della Besana (i.e., business event).
Such a cluster will be exploited by the delivery services of MI-Search when a request
about the Fabrizio de André art-exhibition is submitted by a user.
4 Concluding remarks
In this paper, we presented the main features of the MI-Search project for urban-
centered and calendar-oriented surfing of web contents. Technical issues about web
content acquisition, matching, and classification as well as real examples applied to
the city of Milan are also discussed.
24
Fig. 4. An example of similarity cluster
Ongoing research work is devoted to study the problem of periodically refreshing
the contents of the MI-Search-DB repository and to complete the acquisition of a
dataset about the city of Milan to be employed for experimentation. Moreover,
matching techniques combining both string-based techniques and position-based
techniques are currently under development as well as techniques for content delivery
based on similarity cluster exploitation. Finally, next-future activities will be focused
on the development of a mobile prototype based on the presented ideas.
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