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Abstract

\chapterAbstract}

To extend the traditional knowledge base with temporal dimension, this thesis
offers methods and tools for harvesting temporal facts from both
semi-structured and textual sources.
Our contributions are briefly summarized as follows.

\begin{enumerate}
\item{\bf Timely YAGO:} A temporal knowledge base called Timely YAGO
(T-YAGO) which extends YAGO with temporal attributes is built. We define a
simple RDF-style data model to support temporal knowledge.

\item{\bf PRAVDA:} To be able to harvest as many temporal facts from
free-text as possible, we develop a system PRAVDA.
It utilizes a graph-based semi-supervised learning algorithm to extract fact
observations, which are further cleaned up by an Integer Linear Program based
constraint solver.
We also attempt to harvest spatio-temporal facts to track a person's
trajectory.

\item{\bf PRAVDA-live:} A user-centric interactive knowledge harvesting
system, called PRAVDA-live, is developed for extracting facts from natural
language free-text. It is built on the framework of PRAVDA.
It supports fact extraction of user-defined relations from ad-hoc selected text
documents
and ready-to-use RDF exports.

\item{\bf T-URDF:} We present a simple and efficient representation
model for time-dependent uncertainty in combination with first-order
inference rules and recursive queries over RDF-like knowledge bases.
We adopt the common possible-worlds semantics known from probabilistic
databases and extend it towards histogram-like confidence distributions that
capture the validity of facts across time.


\end{enumerate

All of these components are fully implemented systems, which together form an
integrative architecture.
PRAVDA and PRAVDA-live aim at gathering new facts (particularly temporal facts),
and then T-URDF reconciles them.
Finally these facts are stored in a (temporal) knowledge base, called T-YAGO.
A SPARQL-like time-aware querying language, together with a visualization tool,
are designed for T-YAGO.
Temporal knowledge can also be applied for document summarization.