=Paper=
{{Paper
|id=Vol-500/paper-13
|storemode=property
|title=Unifying Search and Reasoning: From the Viewpoint of Granularity
|pdfUrl=https://ceur-ws.org/Vol-500/paper13.pdf
|volume=Vol-500
}}
==Unifying Search and Reasoning: From the Viewpoint of Granularity==
<pdf width="1500px">https://ceur-ws.org/Vol-500/paper13.pdf</pdf>
<pre>
                  Unifying Web-scale Search and Reasoning
                       from the Viewpoint of Granularity

                               Yi Zeng, Yan Wang
          (International WIC Institute, Beijing University of Technology)

Abstract
Considering the time constraints, it is impossible to achieve absolutely complete
reasoning results based on Web-scale data. Plus, for the same query, the same
results may not meet the diversity of user needs since their expectations may
differ a lot. One of the major solutions for this problem is to unify search and
reasoning. From the perspective of granularity, this talk provides various
strategies of unifying search and reasoning for effective problem solving on the
Web. We bring the strategies of multilevel, multiperspective, starting point
(inspired by the basic level advantage in Cognitive Science) from human problem
solving to Web scale reasoning to satisfy a wide variety of user needs and to
remove the scalability barriers. Concrete methods such as network statistics
based data selection and ontology supervised hierarchical reasoning are applied
to these strategies.

For the diversity issue: The strategy of starting point focuses on user specific
background and the unification process is familiarity driven or novelty driven, and
is obviously user oriented. Multilevel completeness strategy is with anytime
behavior, and provides predictions of completeness for user judges when the
user interacts with the system. Multilevel specificity strategy emphasizes on
reasoning with multiple levels of specificity and users can choose whether to go
into more specific or more general levels. Multiperspective strategy attempts to
meet various user needs from multiple perspectives. For the scalability issue: In
the multilevel completeness strategy, although the partial results may have low
completeness, more important results have been searched out and ranked to the
top ones for reasoning. In other words, more important results are provided as a
possible way to solve the scalability problems. The starting point strategy also
provides two methods to select important nodes for reasoning. The multilevel
specificity strategy concentrates on the appropriate levels of specificity controlled
by the knowledge hierarchy and does not get into unnecessary levels of data.
Hence, under limited time, the reasoning task and time is reduced. The
experimental results based on the SwetoDBLP dataset shows that the proposed
strategies are potentially effective.

Acknowledgement

The study related to this talk is supported by the LarKC WP8 Exchange Program
and Vrije University Amsterdam, the Netherlands. Most part of this talk was
prepared when Yi Zeng was visiting Vrije University Amsterdam. I would like to
thank Zhisheng Huang on his guidance during my visit, and Stefan Schlobach,
Christophe Gueret on their constructive comments.

</pre>