4,666 research outputs found
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
A SAT-based System for Consistent Query Answering
An inconsistent database is a database that violates one or more integrity
constraints, such as functional dependencies. Consistent Query Answering is a
rigorous and principled approach to the semantics of queries posed against
inconsistent databases. The consistent answers to a query on an inconsistent
database is the intersection of the answers to the query on every repair, i.e.,
on every consistent database that differs from the given inconsistent one in a
minimal way. Computing the consistent answers of a fixed conjunctive query on a
given inconsistent database can be a coNP-hard problem, even though every fixed
conjunctive query is efficiently computable on a given consistent database.
We designed, implemented, and evaluated CAvSAT, a SAT-based system for
consistent query answering. CAvSAT leverages a set of natural reductions from
the complement of consistent query answering to SAT and to Weighted MaxSAT. The
system is capable of handling unions of conjunctive queries and arbitrary
denial constraints, which include functional dependencies as a special case. We
report results from experiments evaluating CAvSAT on both synthetic and
real-world databases. These results provide evidence that a SAT-based approach
can give rise to a comprehensive and scalable system for consistent query
answering.Comment: 25 pages including appendix, to appear in the 22nd International
Conference on Theory and Applications of Satisfiability Testin
Time-Aware Probabilistic Knowledge Graphs
The emergence of open information extraction as a tool for constructing and expanding knowledge graphs has aided the growth of temporal data, for instance, YAGO, NELL and Wikidata. While YAGO and Wikidata maintain the valid time of facts, NELL records the time point at which a fact is retrieved from some Web corpora. Collectively, these knowledge graphs (KG) store facts extracted from Wikipedia and other sources. Due to the imprecise nature of the extraction tools that are used to build and expand KG, such as NELL, the facts in the KG are weighted (a confidence value representing the correctness of a fact). Additionally, NELL can be considered as a transaction time KG because every fact is associated with extraction date. On the other hand, YAGO and Wikidata use the valid time model because they maintain facts together with their validity time (temporal scope). In this paper, we propose a bitemporal model (that combines transaction and valid time models) for maintaining and querying bitemporal probabilistic knowledge graphs. We study coalescing and scalability of marginal and MAP inference. Moreover, we show that complexity of reasoning tasks in atemporal probabilistic KG carry over to the bitemporal setting. Finally, we report our evaluation results of the proposed model
A Dichotomy on the Complexity of Consistent Query Answering for Atoms with Simple Keys
We study the problem of consistent query answering under primary key
violations. In this setting, the relations in a database violate the key
constraints and we are interested in maximal subsets of the database that
satisfy the constraints, which we call repairs. For a boolean query Q, the
problem CERTAINTY(Q) asks whether every such repair satisfies the query or not;
the problem is known to be always in coNP for conjunctive queries. However,
there are queries for which it can be solved in polynomial time. It has been
conjectured that there exists a dichotomy on the complexity of CERTAINTY(Q) for
conjunctive queries: it is either in PTIME or coNP-complete. In this paper, we
prove that the conjecture is indeed true for the case of conjunctive queries
without self-joins, where each atom has as a key either a single attribute
(simple key) or all attributes of the atom
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