152 research outputs found
Query Answering in Probabilistic Data and Knowledge Bases
Probabilistic data and knowledge bases are becoming increasingly important in academia and industry. They are continuously extended with new data, powered by modern information extraction tools that associate probabilities with knowledge base facts. The state of the art to store and process such data is founded on probabilistic database systems, which are widely and successfully employed. Beyond all the success stories, however, such systems still lack the fundamental machinery to convey some of the valuable knowledge hidden in them to the end user, which limits their potential applications in practice. In particular, in their classical form, such systems are typically based on strong, unrealistic limitations, such as the closed-world assumption, the closed-domain assumption, the tuple-independence assumption, and the lack of commonsense knowledge. These limitations do not only lead to unwanted consequences, but also put such systems on weak footing in important tasks, querying answering being a very central one. In this thesis, we enhance probabilistic data and knowledge bases with more realistic data models, thereby allowing for better means for querying them. Building on the long endeavor of unifying logic and probability, we develop different rigorous semantics for probabilistic data and knowledge bases, analyze their computational properties and identify sources of (in)tractability and design practical scalable query answering algorithms whenever possible. To achieve this, the current work brings together some recent paradigms from logics, probabilistic inference, and database theory
Most Probable Explanations for Probabilistic Database Queries: Extended Version
Forming the foundations of large-scale knowledge bases, probabilistic databases have been widely studied in the literature. In particular, probabilistic query evaluation has been investigated intensively as a central inference mechanism. However, despite its power, query evaluation alone cannot extract all the relevant information encompassed in large-scale knowledge bases. To exploit this potential, we study two inference tasks; namely finding the most probable database and the most probable hypothesis for a given query. As natural counterparts of most probable explanations (MPE) and maximum a posteriori hypotheses (MAP) in probabilistic graphical models, they can be used in a variety of applications that involve prediction or diagnosis tasks. We investigate these problems relative to a variety of query languages, ranging from conjunctive queries to ontology-mediated queries, and provide a detailed complexity analysis
Ontology-Mediated Query Answering over Log-Linear Probabilistic Data: Extended Version
Large-scale knowledge bases are at the heart of modern information systems. Their knowledge is inherently uncertain, and hence they are often materialized as probabilistic databases. However, probabilistic database management systems typically lack the capability to incorporate implicit background knowledge and, consequently, fail to capture some intuitive query answers. Ontology-mediated query answering is a popular paradigm for encoding commonsense knowledge, which can provide more complete answers to user queries. We propose a new data model that integrates the paradigm of ontology-mediated query answering with probabilistic databases, employing a log-linear probability model. We compare our approach to existing proposals, and provide supporting computational results
Splinting Rheumatoid Hand Deformities: A Case Report
Rheumatoid arthritis (RA) is a disease of unknown origin characterized by inflammatory changes in the synovial tissue of joints, cartilage, and bone, and less frequently in extra-articular sites. The rheumatoid hand term used to describe the characteristic deformities of the hands of patients with RA typically includes varying degrees of thumb deformity, finger deformities, and ulnar deviation. One of the treatment approaches in RA hand deformities is the splinting approach. In this case, the results of splinting applied to hand deformities in a patient with RA are presented
BoxE: A Box Embedding Model for Knowledge Base Completion
Knowledge base completion (KBC) aims to automatically infer missing facts by
exploiting information already present in a knowledge base (KB). A promising
approach for KBC is to embed knowledge into latent spaces and make predictions
from learned embeddings. However, existing embedding models are subject to at
least one of the following limitations: (1) theoretical inexpressivity, (2)
lack of support for prominent inference patterns (e.g., hierarchies), (3) lack
of support for KBC over higher-arity relations, and (4) lack of support for
incorporating logical rules. Here, we propose a spatio-translational embedding
model, called BoxE, that simultaneously addresses all these limitations. BoxE
embeds entities as points, and relations as a set of hyper-rectangles (or
boxes), which spatially characterize basic logical properties. This seemingly
simple abstraction yields a fully expressive model offering a natural encoding
for many desired logical properties. BoxE can both capture and inject rules
from rich classes of rule languages, going well beyond individual inference
patterns. By design, BoxE naturally applies to higher-arity KBs. We conduct a
detailed experimental analysis, and show that BoxE achieves state-of-the-art
performance, both on benchmark knowledge graphs and on more general KBs, and we
empirically show the power of integrating logical rules.Comment: Proceedings of the Thirty-Fourth Annual Conference on Advances in
Neural Information Processing Systems (NeurIPS 2020). Code and data available
at: http://www.github.com/ralphabb/Box
PlanE: Representation Learning over Planar Graphs
Graph neural networks are prominent models for representation learning over
graphs, where the idea is to iteratively compute representations of nodes of an
input graph through a series of transformations in such a way that the learned
graph function is isomorphism invariant on graphs, which makes the learned
representations graph invariants. On the other hand, it is well-known that
graph invariants learned by these class of models are incomplete: there are
pairs of non-isomorphic graphs which cannot be distinguished by standard graph
neural networks. This is unsurprising given the computational difficulty of
graph isomorphism testing on general graphs, but the situation begs to differ
for special graph classes, for which efficient graph isomorphism testing
algorithms are known, such as planar graphs. The goal of this work is to design
architectures for efficiently learning complete invariants of planar graphs.
Inspired by the classical planar graph isomorphism algorithm of Hopcroft and
Tarjan, we propose PlanE as a framework for planar representation learning.
PlanE includes architectures which can learn complete invariants over planar
graphs while remaining practically scalable. We empirically validate the strong
performance of the resulting model architectures on well-known planar graph
benchmarks, achieving multiple state-of-the-art results.Comment: Proceedings of the Thirty-Seventh Annual Conference on Advances in
Neural Information Processing Systems (NeurIPS 2023). Code and data available
at: https://github.com/ZZYSonny/Plan
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