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]: This paper develops the idea of membership function assignment for OWL (Web Ontology Language) ontology elements in order to subsequently generate fuzzy rules from this ontology. The task of membership function assignment for OWL ontology elements had already been partially described, but this concerned the case , when several OWL ontologies of the same domain were available, and they were merged into a single ontology. The purpose of this paper is to present the way of membership function assignment for OWL ontology elements in the case, when there is the only one available ontology. Fuzzy rules, generated from the OWL ontology, are necessary for supplement of the SWES (Semantic Web Expert System) knowledge base. SWES is an expert system, which will be able to extract knowledge from OWL ontologies , found in the Web, and will serve as a universal expert for the user.
[EX A]: Membership Function Assignment for Elements of Single OWL Ontology

[EX Q]: An extended, revised form of Tim Buckwalter's Arabic lexical and morphological resource AraMorph, eXtended Revised AraMorph (henceforth XRAM), is presented which addresses a number of weaknesses and inconsistencies of the original model by allowing a wider coverage of real-world Classical and contemporary (both formal and informal) Arabic texts. Building upon previous research, XRAM enhancements include (i) flag-selectable usage markers, (ii) probabilistic mildly context-sensitive POS tagging, filtering, disambiguation and ranking of alternative morphological analyses, (iii) semi-automatic increment of lexical coverage through extraction of lexical and morphological information from existing lexical resources. Testing of XRAM through a front-end Python module showed a remarkable success level.
[EX A]: Semi-Automatic Data Annotation, POS Tagging and Mildly Context- Sensitive Disambiguation: the eXtended Revised AraMorph (XRAM)

[EX Q]: Large-scale multi-relational embedding refers to the task of learning the latent representations for entities and relations in large knowledge graphs. An effective and scalable solution for this problem is crucial for the true success of knowledgebased inference in a broad range of applications. This paper proposes a novel framework for optimizing the latent representations with respect to the analogical properties of the embedded entities and relations. By formulating the learning objective in a differentiable fashion, our model enjoys both theoretical power and computational scalability, and significantly outperformed a large number of representative baseline methods on benchmark datasets. Furthermore, the model offers an elegant unification of several well-known methods in multi-relational embedding, which can be proven to be special instantiations of our framework.
[EX A]:
Analogical Inference for Multi-relational Embeddings