The opinion expressed in various Web sites and social-media is an essential
contributor to the decision making process of several organizations. Existing
sentiment analysis tools aim to extract the polarity (i.e., positive, negative,
neutral) from these opinionated contents. Despite the advance of the research
in the field, sentiment analysis tools give \textit{inconsistent} polarities,
which is harmful to business decisions. In this paper, we propose SentiQ, an
unsupervised Markov logic Network-based approach that injects the semantic
dimension in the tools through rules. It allows to detect and solve
inconsistencies and then improves the overall accuracy of the tools.
Preliminary experimental results demonstrate the usefulness of SentiQ.

本研究提出了 SentiQ，一种基于无监督 Markov 逻辑网络的方法来注入语义维度以检测并解决情感分析工具中的不一致性，从而提高准确性。初步实验结果证明了 SentiQ 的实用性。

SentiQ: 基于概率逻辑的情感分析工具质量增强方法

SentiQ: A Probabilistic Logic Approach to Enhance Sentiment Analysis  Tool Quality

Knowledge graph reasoning, which aims at predicting the missing facts through
reasoning with the observed facts, is critical to many applications. Such a
problem has been widely explored by traditional logic rule-based approaches and
recent knowledge graph embedding methods. A principled logic rule-based
approach is the Markov Logic Network (MLN), which is able to leverage domain
knowledge with first-order logic and meanwhile handle their uncertainty.
However, the inference of MLNs is usually very difficult due to the complicated
graph structures. Different from MLNs, knowledge graph embedding methods (e.g.
TransE, DistMult) learn effective entity and relation embeddings for reasoning,
which are much more effective and efficient. However, they are unable to
leverage domain knowledge. In this paper, we propose the probabilistic Logic
Neural Network (pLogicNet), which combines the advantages of both methods. A
pLogicNet defines the joint distribution of all possible triplets by using a
Markov logic network with first-order logic, which can be efficiently optimized
with the variational EM algorithm. In the E-step, a knowledge graph embedding
model is used for inferring the missing triplets, while in the M-step, the
weights of logic rules are updated based on both the observed and predicted
triplets. Experiments on multiple knowledge graphs prove the effectiveness of
pLogicNet over many competitive baselines.

该论文提出了一种基于 Markov Logic Network 和知识图谱嵌入方法的概率逻辑神经网络 (pLogicNet) 来推理缺失的三元组，该方法结合了两种技术的优点，并通过多重知识图实验证明了其优越性。

概率逻辑神经网络推理

Probabilistic Logic Neural Networks for Reasoning

Automated extraction of semantic information from a network of sensors for
cognitive analysis and human-like reasoning is a desired capability in future
ground surveillance systems. We tackle the problem of complex decision making
under uncertainty in network information environment, where lack of effective
visual processing tools, incomplete domain knowledge frequently cause
uncertainty in the visual primitives, leading to sub-optimal decisions. While
state-of-the-art vision techniques exist in detecting visual entities (humans,
vehicles and scene elements) in an image, a missing functionality is the
ability to merge the information to reveal meaningful information for high
level inference. In this work, we develop a probabilistic first order predicate
logic(FOPL) based reasoning system for recognizing complex events in
synchronized stream of videos, acquired from sensors with non-overlapping
fields of view. We adopt Markov Logic Network(MLN) as a tool to model
uncertainty in observations, and fuse information extracted from heterogeneous
data in a probabilistically consistent way. MLN overcomes strong dependence on
pure empirical learning by incorporating domain knowledge, in the form of
user-defined rules and confidences associated with them. This work demonstrates
that the MLN based decision control system can be made scalable to model
statistical relations between a variety of entities and over long video
sequences. Experiments with real-world data, under a variety of settings,
illustrate the mathematical soundness and wide-ranging applicability of our
approach.

本文介绍了一种使用 Markov 逻辑网络的概率一阶谓词逻辑（FOPL）推理系统，从传感器网络中提取信息进行复杂决策，以及证明这种方法在处理各种实际数据时的数学准确性和广泛适用性。

传感器网络中的不确定情况下的复杂事件识别

Complex Events Recognition under Uncertainty in a Sensor Network

RockIt is a maximum a-posteriori (MAP) query engine for statistical
relational models. MAP inference in graphical models is an optimization problem
which can be compiled to integer linear programs (ILPs). We describe several
advances in translating MAP queries to ILP instances and present the novel
meta-algorithm cutting plane aggregation (CPA). CPA exploits local
context-specific symmetries and bundles up sets of linear constraints. The
resulting counting constraints lead to more compact ILPs and make the symmetry
of the ground model more explicit to state-of-the-art ILP solvers. Moreover,
RockIt parallelizes most parts of the MAP inference pipeline taking advantage
of ubiquitous shared-memory multi-core architectures.
We report on extensive experiments with Markov logic network (MLN) benchmarks
showing that RockIt outperforms the state-of-the-art systems Alchemy, Markov
TheBeast, and Tuffy both in terms of efficiency and quality of results.

RockIt 是一种基于最大后验查询引擎的查询引擎，通过整数线性规划 (ILPs) 的编译实现 MAP 推理，并使用一种名为 cutting plane aggregation (CPA) 的新型元算法。它还利用共享内存多核架构并行化 MAP 推理管道的大部分部分，通过与 Alchemy、Markov TheBeast 和 Tuffy 等状态 - of-the-art 系统的大量实验表明，RockIt 在效率和结果质量方面都优于这些系统。