Feedback particle filter for collective inference

<p style='text-indent:20px;'>The purpose of this paper is to describe the feedback particle filter algorithm for problems where there are a large number (<inline-formula><tex-math id="M1">\begin{document}$ M $\end{document}</tex-math></inline-formula>) of non-interacting agents (targets) with a large number (<inline-formula><tex-math id="M2">\begin{document}$ M $\end{document}</tex-math></inline-formula>) of non-agent specific observations (measurements) that originate from these agents. In its basic form, the problem is characterized by data association uncertainty whereby the association between the observations and agents must be deduced in addition to the agent state. In this paper, the large-<inline-formula><tex-math id="M3">\begin{document}$ M $\end{document}</tex-math></inline-formula> limit is interpreted as a problem of collective inference. This viewpoint is used to derive the equation for the empirical distribution of the hidden agent states. A feedback particle filter (FPF) algorithm for this problem is presented and illustrated via numerical simulations. Results are presented for the Euclidean and the finite state-space cases, both in continuous-time settings. The classical FPF algorithm is shown to be the special case (with <inline-formula><tex-math id="M4">\begin{document}$ M = 1 $\end{document}</tex-math></inline-formula>) of these more general results. The simulations help show that the algorithm well approximates the empirical distribution of the hidden states for large <inline-formula><tex-math id="M5">\begin{document}$ M $\end{document}</tex-math></inline-formula>.</p>

Cautious Rule-Based Collective Inference

Collective inference is a popular approach for solving tasks as knowledge graph completion within the statistical relational learning field. There are many existing solutions for this task, however, each of them is subjected to some limitation, either by restriction to only some learning settings, lacking interpretability of the model or theoretical test error bounds. We propose an approach based on cautious inference process which uses first-order rules and provides PAC-style bounds.

Extracting relations from traditional Chinese medicine literature via heterogeneous entity networks

Objective Traditional Chinese medicine (TCM) is a unique and complex medical system that has developed over thousands of years. This article studies the problem of automatically extracting meaningful relations of entities from TCM literature, for the purposes of assisting clinical treatment or poly-pharmacology research and promoting the understanding of TCM in Western countries. Methods Instead of separately extracting each relation from a single sentence or document, we propose to collectively and globally extract multiple types of relations (eg, herb-syndrome, herb-disease, formula-syndrome, formula-disease, and syndrome-disease relations) from the entire corpus of TCM literature, from the perspective of network mining. In our analysis, we first constructed heterogeneous entity networks from the TCM literature, in which each edge is a candidate relation, then used a heterogeneous factor graph model (HFGM) to simultaneously infer the existence of all the edges. We also employed a semi-supervised learning algorithm estimate the model’s parameters. Results We performed our method to extract relations from a large dataset consisting of more than 100 000 TCM article abstracts. Our results show that the performance of the HFGM at extracting all types of relations from TCM literature was significantly better than a traditional support vector machine (SVM) classifier (increasing the average precision by 11.09%, the recall by 13.83%, and the F1-measure by 12.47% for different types of relations, compared with a traditional SVM classifier). Conclusion This study exploits the power of collective inference and proposes an HFGM based on heterogeneous entity networks, which significantly improved our ability to extract relations from TCM literature.