In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[EX Q]: Nowadays ontologies present a growing interest in Data Fusion applications. As a matter of fact, the ontologies are seen as a semantic tool for describing and reasoning about sensor data, objects, relations and general domain theories. In addition, uncertainty is perhaps one of the most important characteristics of the data and information handled by Data Fusion. However, the fundamental nature of ontologies implies that ontologies describe only asserted and veracious facts of the world. Different probabilistic, fuzzy and evidential approaches already exist to fill this gap; this paper recaps the most popular tools. However none of the tools meets exactly our purposes. Therefore, we constructed a Dempster-Shafer ontology that can be imported into any specific domain ontology and that enables us to instantiate it in an uncertain manner. We also developed a Java application that enables reasoning about these uncertain ontological instances.
[EX A]: Uncertainty in Ontologies: Dempster-Shafer Theory for Data Fusion Applications

[EX Q]: Determining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow information, capturing accurate semantics is difficult. By contrast, logical semantic representations capture deeper levels of sentence semantics, but their symbolic nature does not offer graded notions of textual similarity. We propose a method for determining semantic textual similarity by combining shallow features with features extracted from natural deduction proofs of bidirectional entailment relations between sentence pairs. For the natural deduction proofs, we use ccg2lambda, a higherorder automatic inference system, which converts Combinatory Categorial Grammar (CCG) derivation trees into semantic representations and conducts natural deduction proofs. Experiments show that our system was able to outperform other logicbased systems and that features derived from the proofs are effective for learning textual similarity.
[EX A]: Determining Semantic Textual Similarity using Natural Deduction Proofs

[EX Q]: We introduce a framework for representing a variety of interesting problems as inference over the execution of probabilistic model programs. We represent a “solution” to such a problem as a guide program which runs alongside the model program and influences the model program's random choices, leading the model program to sample from a different distribution than from its priors. Ideally the guide program influences the model program to sample from the posteriors given the evidence. We show how the KLdivergence between the true posterior distribution and the distribution induced by the guided model program can be efficiently estimated (up to an additive constant) by sampling multiple executions of the guided model program. In addition, we show how to use the guide program as a proposal distribution in importance sampling to statistically prove lower bounds on the probability of the evidence and on the probability of a hypothesis and the evidence. We can use the quotient of these two bounds as an estimate of the conditional probability of the hypothesis given the evidence. We thus turn the inference problem into a heuristic search for better guide programs. Problem Specification Given partial observations of a complicated system governed by known or unknown probabilistic rules, we would like to automatically reason about the likely state of hidden parts of the system. Systems with Known Rules We model our system as a program in a general purpose programming language. We call this the model program. We will be agnostic to what programming language we are using. We will only insist that it be deterministic except for a choose function which takes a probability distribution as an argument and returns a random choice from it. The model program thus defines a probability distribution P(x) over execution paths x. If over the course of an execution path x of the model program, the choose function is called n times with distributions (P1, P2 ... Pn) and the randomly chosen values are (c1, c2 .. cn) respectively, then the probability of that execution path is P(x) = ∏i Pi(ci). We are interested in the conditional expected value E(h(x)|e), where h is some function of the execution path, and e is some evidence such that we can easily compute P(e|x) for any x. Our programming language needs to include constructs for specifying P(e|x) and h(x). The model program reports P(e|x) as the product of all calls to a function: evidence. This is a particularly convenient in that the evidence function can be passed boolean values which are interpreted as 0 or 1. For example, we could represent an observation that the grass is wet with the call evidence(grass_wet). If grass_wet is false, P(e|x) is multiplied by 0, and if grass_wet is false, P(e|x) is left unchanged. The value of the hypothesis h(x) is defined as the final value of a global variable *h*. We may not have a hypothesis, and may only be interested in sampling runs of the program given the evidence. In this case, *h* might not be set or used. Example 1: Three fair dice are rolled and it is observed that their sum is 7. What is the probability that the first die rolled was a 5? The following model program (shown in pseudocode) might encode that problem. die1 := choose(uniform(1..6)) die2 := choose(uniform(1..6)) die3 := choose(uniform(1..6))
[EX A]:
Variational Program Inference