39,429 research outputs found
A Rational and Efficient Algorithm for View Revision in Databases
The dynamics of belief and knowledge is one of the major components of any
autonomous system that should be able to incorporate new pieces of information.
In this paper, we argue that to apply rationality result of belief dynamics
theory to various practical problems, it should be generalized in two respects:
first of all, it should allow a certain part of belief to be declared as
immutable; and second, the belief state need not be deductively closed. Such a
generalization of belief dynamics, referred to as base dynamics, is presented,
along with the concept of a generalized revision algorithm for Horn knowledge
bases. We show that Horn knowledge base dynamics has interesting connection
with kernel change and abduction. Finally, we also show that both variants are
rational in the sense that they satisfy certain rationality postulates stemming
from philosophical works on belief dynamics
Believe It or Not: Adding Belief Annotations to Databases
We propose a database model that allows users to annotate data with belief
statements. Our motivation comes from scientific database applications where a
community of users is working together to assemble, revise, and curate a shared
data repository. As the community accumulates knowledge and the database
content evolves over time, it may contain conflicting information and members
can disagree on the information it should store. For example, Alice may believe
that a tuple should be in the database, whereas Bob disagrees. He may also
insert the reason why he thinks Alice believes the tuple should be in the
database, and explain what he thinks the correct tuple should be instead.
We propose a formal model for Belief Databases that interprets users'
annotations as belief statements. These annotations can refer both to the base
data and to other annotations. We give a formal semantics based on a fragment
of multi-agent epistemic logic and define a query language over belief
databases. We then prove a key technical result, stating that every belief
database can be encoded as a canonical Kripke structure. We use this structure
to describe a relational representation of belief databases, and give an
algorithm for translating queries over the belief database into standard
relational queries. Finally, we report early experimental results with our
prototype implementation on synthetic data.Comment: 17 pages, 10 figure
Structurally Tractable Uncertain Data
Many data management applications must deal with data which is uncertain,
incomplete, or noisy. However, on existing uncertain data representations, we
cannot tractably perform the important query evaluation tasks of determining
query possibility, certainty, or probability: these problems are hard on
arbitrary uncertain input instances. We thus ask whether we could restrict the
structure of uncertain data so as to guarantee the tractability of exact query
evaluation. We present our tractability results for tree and tree-like
uncertain data, and a vision for probabilistic rule reasoning. We also study
uncertainty about order, proposing a suitable representation, and study
uncertain data conditioned by additional observations.Comment: 11 pages, 1 figure, 1 table. To appear in SIGMOD/PODS PhD Symposium
201
Learning Tuple Probabilities
Learning the parameters of complex probabilistic-relational models from
labeled training data is a standard technique in machine learning, which has
been intensively studied in the subfield of Statistical Relational Learning
(SRL), but---so far---this is still an under-investigated topic in the context
of Probabilistic Databases (PDBs). In this paper, we focus on learning the
probability values of base tuples in a PDB from labeled lineage formulas. The
resulting learning problem can be viewed as the inverse problem to confidence
computations in PDBs: given a set of labeled query answers, learn the
probability values of the base tuples, such that the marginal probabilities of
the query answers again yield in the assigned probability labels. We analyze
the learning problem from a theoretical perspective, cast it into an
optimization problem, and provide an algorithm based on stochastic gradient
descent. Finally, we conclude by an experimental evaluation on three real-world
and one synthetic dataset, thus comparing our approach to various techniques
from SRL, reasoning in information extraction, and optimization
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