Fakultät für Informatik und Mathematik
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During the last few years, the technological progress in collecting, storing and processing a large quantity of data for a reasonable cost has raised serious privacy issues. Privacy concerns many areas, but is especially important in frequently used services like search engines (e.g., Google, Bing, Yahoo!). These services allow users to retrieve relevant content on the Internet by exploiting their personal data. In this context, developing solutions to enable users to use these services in a privacy-preserving way is becoming increasingly important.
In this thesis, we introduce SimAttack an attack against existing protection mechanism to query search engines in a privacy-preserving way. This attack aims at retrieving the original user query. We show with this attack that three representative state-of-the-art solutions do not protect the user privacy in a satisfactory manner.
We therefore develop PEAS a new protection mechanism that better protects the user privacy. This solution leverages two types of protection: hiding the user identity (with a succession of two nodes) and masking users' queries (by combining them with several fake queries). To generate realistic fake queries, PEAS exploits previous queries sent by the users in the system.
Finally, we present mechanisms to identify sensitive queries. Our goal is to adapt existing protection mechanisms to protect sensitive queries only, and thus save user resources (e.g., CPU, RAM). We design two modules to identify sensitive queries. By deploying these modules on real protection mechanisms, we establish empirically that they dramatically improve the performance of the protection mechanisms.
In our knowledge-driven society, the acquisition and the transfer of knowledge play a principal role. Web search engines are somehow tools for knowledge acquisition and transfer from the web to the user. The search engine results page (SERP) consists mainly of a list of links and snippets (excerpts from the results). The snippets are used to express, as efficiently as possible, the way a web page may be relevant to the query.
As an extension of the existing web, the semantic web or “web 3.0” is designed to convert the presently available web of unstructured documents into a web of data consumable by both human and machines. The resulting web of data and the current web of documents coexist and interconnect via multiple mechanisms, such as the embedded structured data, or the automatic annotation.
In this thesis, we introduce a new interactive artifact for the SERP: the “Semantic Snippet”. Semantic Snippets rely on the coexistence of the two webs to facilitate the transfer of knowledge to the user thanks to a semantic contextualization of the user’s information need. It makes apparent the relationships between the information need and the most relevant entities present in the web page.
The generation of semantic snippets is mainly based on the automatic annotation of the LOD1’s entities in web pages. The annotated entities have different level of impor- tance, usefulness and relevance. Even with state of the art solutions for the automatic annotations of LOD entities within web pages, there is still a lot of noise in the form of erroneous or off-topic annotations. Therefore, we propose a query-biased algorithm (LDRANK) for the ranking of these entities. LDRANK adopts a strategy based on the linear consensual combination of several sources of prior knowledge (any form of con- textual knowledge, like the textual descriptions for the nodes of the graph) to modify a PageRank-like algorithm.
For generating semantic snippets, we use LDRANK to find the more relevant entities in the web page. Then, we use a supervised learning algorithm to link each selected entity to excerpts from the web page that highlight the relationship between the entity and the original information need.
In order to evaluate our semantic snippets, we integrate them in ENsEN (Enhanced Search Engine), a software system that enhances the SERP with semantic snippets.
Finally, we use crowdsourcing to evaluate the usefulness and the efficiency of ENsEN.
This thesis addresses a problem related to usage analysis in information retrieval
systems. Indeed, we exploit the history of search queries as support of analysis to
extract a profile model. The objective is to characterize the user and the data source
that interact in a system to allow different types of comparison (user-to-user, sourceto-
source, user-to-source). According to the study we conducted on the work done on
profile model, we concluded that the large majority of the contributions are strongly
related to the applications within they are proposed. As a result, the proposed
profile models are not reusable and suffer from several weaknesses. For instance,
these models do not consider the data source, they lack of semantic mechanisms and
they do not deal with scalability (in terms of complexity). Therefore, we propose
a generic model of user and data source profiles. The characteristics of this model
are the following. First, it is generic, being able to represent both the user and the
data source. Second, it enables to construct the profiles in an implicit way based on histories of search queries. Third, it defines the profile as a set of topics of interest,
each topic corresponding to a semantic cluster of keywords extracted by a specific
clustering algorithm. Finally, the profile is represented according to the vector space
model. The model is composed of several components organized in the form of a
framework, in which we assessed the complexity of each component.
The main components of the framework are:
• a method for keyword queries disambiguation
• a method for semantically representing search query logs in the form of a
taxonomy;
• a clustering algorithm that allows fast and efficient identification of topics of
interest as semantic clusters of keywords;
• a method to identify user and data source profiles according to the generic
model.
This framework enables in particular to perform various tasks related to usage-based
structuration of a distributed environment. As an example of application, the framework
is used to the discovery of user communities, and the categorization of data
sources. To validate the proposed framework, we conduct a series of experiments
on real logs from the search engine AOL search, which demonstrate the efficiency
of the disambiguation method in short queries, and show the relation between the
quality based clustering and the structure based clustering.