45 research outputs found
Fast and Simple Relational Processing of Uncertain Data
This paper introduces U-relations, a succinct and purely relational
representation system for uncertain databases. U-relations support
attribute-level uncertainty using vertical partitioning. If we consider
positive relational algebra extended by an operation for computing possible
answers, a query on the logical level can be translated into, and evaluated as,
a single relational algebra query on the U-relation representation. The
translation scheme essentially preserves the size of the query in terms of
number of operations and, in particular, number of joins. Standard techniques
employed in off-the-shelf relational database management systems are effective
for optimizing and processing queries on U-relations. In our experiments we
show that query evaluation on U-relations scales to large amounts of data with
high degrees of uncertainty.Comment: 12 pages, 14 figure
Named Entity Extraction and Disambiguation: The Reinforcement Effect.
Named entity extraction and disambiguation have received much attention in recent years. Typical fields addressing these topics are information retrieval, natural language processing, and semantic web. Although these topics are highly dependent, almost no existing works examine this dependency. It is the aim of this paper to examine the dependency and show how one affects the other, and vice versa. We conducted experiments with a set of descriptions of holiday homes with the aim to extract and disambiguate toponyms as a representative example of named entities. We experimented with three approaches for disambiguation with the purpose to infer the country of the holiday home. We examined how the effectiveness of extraction influences the effectiveness of disambiguation, and reciprocally, how filtering out ambiguous names (an activity that depends on the disambiguation process) improves the effectiveness of extraction. Since this, in turn, may improve the effectiveness of disambiguation again, it shows that extraction and disambiguation may reinforce each other.\u
Toward an OSGi-Based Infra- structure for Smart Home Applications
[[abstract]]In this paper we show the steps to implement the prototype of a smart home environment, with emphasis on the OSGi framework and first-order logic based inference engine. Due to the inherent extensibility of the underlying system, either the interfaces of sensors/home appliances or the inference rules can be adjusted and reloaded during runtime. Moreover, we illustrate several sensor-driven services that may help develop intriguing smart home applications in the near future. Related works and future directions are also addressed to complete our current implementation afterwards.[[conferencetype]]國際[[conferencedate]]20090707~20090709[[iscallforpapers]]Y[[conferencelocation]]Brisbane, Australia
Scalable Probabilistic Similarity Ranking in Uncertain Databases (Technical Report)
This paper introduces a scalable approach for probabilistic top-k similarity
ranking on uncertain vector data. Each uncertain object is represented by a set
of vector instances that are assumed to be mutually-exclusive. The objective is
to rank the uncertain data according to their distance to a reference object.
We propose a framework that incrementally computes for each object instance and
ranking position, the probability of the object falling at that ranking
position. The resulting rank probability distribution can serve as input for
several state-of-the-art probabilistic ranking models. Existing approaches
compute this probability distribution by applying a dynamic programming
approach of quadratic complexity. In this paper we theoretically as well as
experimentally show that our framework reduces this to a linear-time complexity
while having the same memory requirements, facilitated by incremental accessing
of the uncertain vector instances in increasing order of their distance to the
reference object. Furthermore, we show how the output of our method can be used
to apply probabilistic top-k ranking for the objects, according to different
state-of-the-art definitions. We conduct an experimental evaluation on
synthetic and real data, which demonstrates the efficiency of our approach
Integrating and Ranking Uncertain Scientific Data
Mediator-based data integration systems resolve exploratory queries by joining data elements across sources. In the presence of uncertainties, such multiple expansions can quickly lead to spurious connections and incorrect results. The BioRank project investigates formalisms for modeling uncertainty during scientific data integration and for ranking uncertain query results. Our motivating application is protein function prediction. In this paper we show that: (i) explicit modeling of uncertainties as probabilities increases our ability to predict less-known or previously unknown functions (though it does not improve predicting the well-known). This suggests that probabilistic uncertainty models offer utility for scientific knowledge discovery; (ii) small perturbations in the input probabilities tend to produce only minor changes in the quality of our result rankings. This suggests that our methods are robust against slight variations in the way uncertainties are transformed into probabilities; and (iii) several techniques allow us to evaluate our probabilistic rankings efficiently. This suggests that probabilistic query evaluation is not as hard for real-world problems as theory indicates
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