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]: The holy Quran is the holy book of the Muslims. It contains information about many domains. Often people search for particular concepts of holy Quran based on the relations among concepts. An ontological modeling of holy Quran can be useful in such a scenario. In this paper, we have modeled nature related concepts of holy Quran using OWL (Web Ontology Language) / RDF (Resource Description Framework). Our methodology involves identifying nature related concepts mentioned in holy Quran and identifying relations among those concepts. These concepts and relations are represented as classes/instances and properties of an OWL ontology. Later, in the result section it is shown that, using the Ontological model, SPARQL queries can retrieve verses and concepts of interest. Thus, this modeling helps semantic search and query on the holy Quran. In this work, we have used English translation of the holy Quran by Sahih International, Protege OWL Editor and for querying we have used SPARQL. Keywords— Quranic Ontology; Semantic Quran; Quranic Knowledge Representation.
[A]: Applying Ontological Modeling on Quranic “Nature” Domain


[Q]: In this paper, we implicitly incorporate morpheme information into word embedding. Based on the strategy we utilize the morpheme information, three models are proposed. To test the performances of our models, we conduct the word similarity and syntactic analogy. The results demonstrate the effectiveness of our methods. Our models beat the comparative baselines on both tasks to a great extent. On the golden standard Wordsim-353 and RG-65, our models approximately outperform CBOW for 5 and 7 percent, respectively. In addition, 7 percent advantage is also achieved by our models on syntactic analysis. According to parameter analysis, our models can increase the semantic information in the corpus and our performances on the smallest corpus are similar to the performance of CBOW on the corpus which is five times ours. This property of our methods may have some positive effects on NLP researches about the corpus-limited languages.
[A]: Implicitly Incorporating Morphological Information into Word Embedding


[Q]: Recent progress in randomized motion planners has led to the development of a new class of samplingbased algorithms that provide asymptotic optimality guarantees, notably the RRT∗ and the PRM∗ algorithms. Careful analysis reveals that the so-called “rewiring” step in these algorithms can be interpreted as a local policy iteration (PI) step (i.e., a local policy evaluation step followed by a local policy improvement step) so that asymptotically, as the number of samples tend to infinity, both algorithms converge to the optimal path almost surely (with probability 1). Policy iteration, along with value iteration (VI) are common methods for solving dynamic programming (DP) problems. Based on this observation, recently, the RRT algorithm has been proposed, which performs, during each iteration, Bellman updates (aka“backups”) on those vertices of the graph that have the potential of being part of the optimal path (i.e., the “promising” vertices). The RRT algorithm thus utilizes dynamic programming ideas and implements them incrementally on randomly generated graphs to obtain high quality solutions. In this work, and based on this key insight, we explore a different class of dynamic programming algorithms for solving shortest-path problems on random graphs generated by iterative sampling methods. These class of algorithms utilize policy iteration instead of value iteration, and thus are better suited for massive parallelization. Contrary to the RRT∗ algorithm, the policy improvement during the rewiring step is not performed only locally but rather on a set of vertices that are classified as “promising” during the current iteration. This tends to speed-up the whole process. The resulting algorithm, aptly named Policy Iteration-RRT (PI-RRT) is the first of a new class of DP-inspired algorithms for randomized motion planning that utilize PI methods.
[A]:
Incremental Sampling-based Motion Planners Using Policy Iteration Methods