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.

Q: So-called combined approaches answer a conjunctive query over a description logic ontology in three steps: first, they materialise certain consequences of the ontology and the data; second, they evaluate the query over the data; and third, they filter the result of the second phase to eliminate unsound answers. Such approaches were developed for various members of the DL-Lite and the EL families of languages, but none of them can handle ontologies containing nominals. In our work, we bridge this gap and present a combined query answering approach for ELHO ⊥—a logic that contains all features of the OWL 2 EL standard apart from transitive roles and complex role inclusions. This extension is nontrivial because nominals require equality reasoning, which introduces complexity into the first and the third step. Our empirical evaluation suggests that our technique is suitable for practical application, and so it provides a practical basis for conjunctive query answering in a large fragment of OWL 2 EL.

A: Introducing Nominals to the Combined Query Answering Approaches for EL
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Q: We present the Bayesian Case Model (BCM), a general framework for Bayesian case-based reasoning (CBR) and prototype classification and clustering. BCM brings the intuitive power of CBR to a Bayesian generative framework. The BCM learns prototypes, the “quintessential” observations that best represent clusters in a dataset, by performing joint inference on cluster labels, prototypes and important features. Simultaneously, BCM pursues sparsity by learning subspaces, the sets of features that play important roles in the characterization of the prototypes. The prototype and subspace representation provides quantitative benefits in interpretability while preserving classification accuracy. Human subject experiments verify statistically significant improvements to participants’ understanding when using explanations produced by BCM, compared to those given by prior art.

A: The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
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Q: Automation and computer intelligence to support<lb>complex human decisions becomes essential to manage large and<lb>distributed systems in the Cloud and IoT era. Understanding<lb>the root cause of an observed symptom in a complex system<lb>has been a major problem for decades. As industry dives into<lb>the IoT world and the amount of data generated per year<lb>grows at an amazing speed, an important question is how to<lb>find appropriate mechanisms to determine root causes that can<lb>handle huge amounts of data or may provide valuable feedback<lb>in real-time. While many survey papers aim at summarizing<lb>the landscape of techniques for modelling system behavior and<lb>infering the root cause of a problem based in the resulting<lb>models, none of those focuses on analyzing how the different<lb>techniques in the literature fit growing requirements in terms<lb>of performance and scalability. In this survey, we provide a<lb>review of root-cause analysis, focusing on these particular aspects.<lb>We also provide guidance to choose the best root-cause analysis<lb>strategy depending on the requirements of a particular system<lb>and application.

A:
Survey on Models and Techniques for Root-Cause Analysis
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