18,603 research outputs found
Embedding Uncertain Knowledge Graphs
Embedding models for deterministic Knowledge Graphs (KG) have been
extensively studied, with the purpose of capturing latent semantic relations
between entities and incorporating the structured knowledge into machine
learning. However, there are many KGs that model uncertain knowledge, which
typically model the inherent uncertainty of relations facts with a confidence
score, and embedding such uncertain knowledge represents an unresolved
challenge. The capturing of uncertain knowledge will benefit many
knowledge-driven applications such as question answering and semantic search by
providing more natural characterization of the knowledge. In this paper, we
propose a novel uncertain KG embedding model UKGE, which aims to preserve both
structural and uncertainty information of relation facts in the embedding
space. Unlike previous models that characterize relation facts with binary
classification techniques, UKGE learns embeddings according to the confidence
scores of uncertain relation facts. To further enhance the precision of UKGE,
we also introduce probabilistic soft logic to infer confidence scores for
unseen relation facts during training. We propose and evaluate two variants of
UKGE based on different learning objectives. Experiments are conducted on three
real-world uncertain KGs via three tasks, i.e. confidence prediction, relation
fact ranking, and relation fact classification. UKGE shows effectiveness in
capturing uncertain knowledge by achieving promising results on these tasks,
and consistently outperforms baselines on these tasks
Efficient Subgraph Similarity Search on Large Probabilistic Graph Databases
Many studies have been conducted on seeking the efficient solution for
subgraph similarity search over certain (deterministic) graphs due to its wide
application in many fields, including bioinformatics, social network analysis,
and Resource Description Framework (RDF) data management. All these works
assume that the underlying data are certain. However, in reality, graphs are
often noisy and uncertain due to various factors, such as errors in data
extraction, inconsistencies in data integration, and privacy preserving
purposes. Therefore, in this paper, we study subgraph similarity search on
large probabilistic graph databases. Different from previous works assuming
that edges in an uncertain graph are independent of each other, we study the
uncertain graphs where edges' occurrences are correlated. We formally prove
that subgraph similarity search over probabilistic graphs is #P-complete, thus,
we employ a filter-and-verify framework to speed up the search. In the
filtering phase,we develop tight lower and upper bounds of subgraph similarity
probability based on a probabilistic matrix index, PMI. PMI is composed of
discriminative subgraph features associated with tight lower and upper bounds
of subgraph isomorphism probability. Based on PMI, we can sort out a large
number of probabilistic graphs and maximize the pruning capability. During the
verification phase, we develop an efficient sampling algorithm to validate the
remaining candidates. The efficiency of our proposed solutions has been
verified through extensive experiments.Comment: VLDB201
Distributed Robustness Analysis of Interconnected Uncertain Systems Using Chordal Decomposition
Large-scale interconnected uncertain systems commonly have large state and
uncertainty dimensions. Aside from the heavy computational cost of solving
centralized robust stability analysis techniques, privacy requirements in the
network can also introduce further issues. In this paper, we utilize IQC
analysis for analyzing large-scale interconnected uncertain systems and we
evade these issues by describing a decomposition scheme that is based on the
interconnection structure of the system. This scheme is based on the so-called
chordal decomposition and does not add any conservativeness to the analysis
approach. The decomposed problem can be solved using distributed computational
algorithms without the need for a centralized computational unit. We further
discuss the merits of the proposed analysis approach using a numerical
experiment.Comment: 3 figures. Submitted to the 19th IFAC world congres
Synchronously-pumped OPO coherent Ising machine: benchmarking and prospects
The coherent Ising machine (CIM) is a network of optical parametric oscillators (OPOs) that solves for the ground state of Ising problems through OPO bifurcation dynamics. Here, we present experimental results comparing the performance of the CIM to quantum annealers (QAs) on two classes of NP-hard optimization problems: ground state calculation of the Sherrington-Kirkpatrick (SK) model and MAX-CUT. While the two machines perform comparably on sparsely-connected problems such as cubic MAX-CUT, on problems with dense connectivity, the QA shows an exponential performance penalty relative to CIMs. We attribute this to the embedding overhead required to map dense problems onto the sparse hardware architecture of the QA, a problem that can be overcome in photonic architectures such as the CIM
Euclidean Greedy Drawings of Trees
Greedy embedding (or drawing) is a simple and efficient strategy to route
messages in wireless sensor networks. For each source-destination pair of nodes
s, t in a greedy embedding there is always a neighbor u of s that is closer to
t according to some distance metric. The existence of greedy embeddings in the
Euclidean plane R^2 is known for certain graph classes such as 3-connected
planar graphs. We completely characterize the trees that admit a greedy
embedding in R^2. This answers a question by Angelini et al. (Graph Drawing
2009) and is a further step in characterizing the graphs that admit Euclidean
greedy embeddings.Comment: Expanded version of a paper to appear in the 21st European Symposium
on Algorithms (ESA 2013). 24 pages, 20 figure
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