| Abstract |
In recent years we have witnessed an explosion in publishing data on the Web, mostly in
the formof Linked Data. An important question is how typical users, who mainly use keyword
search queries, can access and exploit this constantly increasing body of knowledge.
Although existing interaction paradigms in Semantic Search hide their complexity behind
easy-to-use interfaces, they have not managed to cover common search needs. At the
same time, according to several studies, a large number of search tasks are of exploratory
nature. However, in such tasks the traditional “ranked list” approach for interacting with
the retrieved results is often inadequate.
The objective of this thesis is to enable effective exploratory search services which can
bridge the gap between the classic responses of non-semantic search systems (e.g., Professional
Search Systems, Web Search Engines) and semantic information expressed in the
form of Linked Open Data (LOD). Towards this direction, we introduce an approach in
which named entities (like names of persons, locations, chemical substances, etc.) are exploited
as the glue for automatically connecting documents (search results) with data and
knowledge. We study an approach where this entity-based integration is performed at
real-time, without any human intervention and without the need of prebuilt indexes. This
allows the provision of “fresh” information, the easy configuration of this functionality according
to the needs of the underlying search application, as well as its easy exploitation
by existing search systems.
The provision of the aforementioned functionality is challenging. At first, the LOD that
are available on the Web are big, are distributed in many knowledge bases, are increased
and updated continuously, and also cover many domains. Consequently, there is the need
of an interoperability model that will allow the specification of the entities of interest as
well as of the related and useful semantic data. In addition, the number of extractable
entities from the search results can be very high and the same is true for the amount of semantic
information that can be retrieved from the LOD for these entities (i.e., the number
of their attributes and of their associations with other entities). Thus, there is also the need
of methods that can estimate the important (for the search context) entities, attributes and
associations.
To cope with above challenges, this thesis proposes a semantic analysis process in
which the search results are connected with data and knowledge at real-time without any
human intervention. For describing the entities of interest, as well as the related (and
useful for the application context) semantic information, we propose a generic model for configuring aNamed Entity Extraction (NEE) system, while for specifying the semantics of
this model, we introduce an RDF/S vocabulary, called “Open NEE Configuration Model”,
which allows a NEE system to describe (and publish as LOD) its entity-mining capabilities.
To enable associating the result of a NEE process with an applied configuration, we
propose an extension of the Open Annotation Data Model which also allows publishing
the annotation results as LOD. To examine the feasibility of this model, we developed the
system X-Link which, contrary to existing NEE systems, allows its easy configuration by
exploiting one or more semantic Knowledge Bases. To identify the important semantic
information related to the search results, we introduce and study a ranking method that is
based on the RandomWalk model and which exploits the extracted entities and their connectivity.
The exploitation of the selected semantic information is achieved either through
the visualization of the related semantic graph and/or in the context of a faceted interaction
model that allows the user to gradually restrict the search space. Besides, this thesis
studied the exploitation of such graphs for re-ranking the list of retrieved results aiming to
promote relevant but low-ranked hits.
The dissertation reports extensive evaluation results of the proposed functionalities
and methods. As regards the system X-Link, a task-based evaluation with users showed its
ease of configuration, while a case study illustrated the efficiency of the supported operations.
The comparative evaluation of the proposed probabilistic scheme for ranking entities
and semantic data showed that the proposed approach is more effective compared
to other ranking approaches (producing more than 20% better ranking). As regards the
presentation of the important entities (and of their associations), the conducted survey
in a marine-related search context demonstrated that the majority of participants (more
than 70%) prefer to see a graph representation of entities related to the retrieved results
regardless the type of the submitted query. The evaluation of the proposed probabilistic
algorithm for re-ranking the retrieved search results (using TREC datasets related to the
medicine domain) showed that this approach can notably improve the list of results by
promoting relevant hits in higher positions. Finally, the implementation and the experimental
results of the proposed search process demonstrated its feasibility and efficiency,
and also enabled us to reveal its limitations.
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