Presence Of Background Knowledge Research Articles

Методы формирования коммуникативных навыков в процессе обучения русско-персидских переводчиков

In the process of training future translators, with the help of special translation exercises, skills and abilities are developed to perform various types of translation. Special importance in translation classes is given to exercises aimed at developing the skills of deverbalization, transformation and switching necessary for the translator, and the development of a bilingual mechanism among students.
 The purpose of this article is to present some useful tasks in the process of learning to translate from Russian into Persian and vice versa, which develop communication skills and bilingualism skills of future translators, as well as improve the quality of translation. The exercises presented in the article are focused not only on working out the lexical and grammatical knowledge of students, but also include tasks implying the presence of background knowledge about culture, awareness of students about precedent texts, understanding of the realities of the two countries.

Objectives and content of teaching foreign languages at the present stage of development of school education

Learning a foreign language, due to the social nature of the language as a repository and reflection of national culture, is inconceivable without simultaneously studying the culture of the country of the language being studied, which necessitates the inclusion of regional geographic materials in the educational process as an essential component of the content of teaching a foreign language. The ability to understand a foreign language culture is ensured by the presence of background knowledge in the linguistic consciousness of the speaker. In the absence of a language environment, the accumulation of background knowledge occurs mainly through reading. In the process of reading, both explicit and implicit information is updated, therefore, in the complex activity of reading in a foreign language, we can distinguish the regional geographic component of the lexical reading skill, the formation of which ensures the accumulation of background knowledge

Objectives and content of teaching foreign languages at the present stage of development of school education

Learning a foreign language, due to the social nature of the language as a repository and reflection of national culture, is inconceivable without simultaneously studying the culture of the country of the language being studied, which necessitates the inclusion of regional geographic materials in the educational process as an essential component of the content of teaching a foreign language. The ability to understand a foreign language culture is ensured by the presence of background knowledge in the linguistic consciousness of the speaker. In the absence of a language environment, the accumulation of background knowledge occurs mainly through reading. In the process of reading, both explicit and implicit information is updated, therefore, in the complex activity of reading in a foreign language, we can distinguish the regional geographic component of the lexical reading skill, the formation of which ensures the accumulation of background knowledge.

Semantic DMN: Formalizing and Reasoning About Decisions in the Presence of Background Knowledge

AbstractThe Decision Model and Notation (DMN) is a recent Object Management Group standard for the elicitation and representation of decision models and for managing their interconnection with business processes. DMN builds on the notion of decision tables and their combination into more complex decision requirements graphs (DRGs), which bridge between business process models and decision logic models. DRGs may rely on additional, external business knowledge models, whose functioning is not part of the standard. In this work, we consider one of the most important types of business knowledge, namely, background knowledge that conceptually accounts for the structural aspects of the domain of interest, and propose decision knowledge bases (DKBs), which semantically combine DRGs modeled in DMN, and domain knowledge captured by means of first-order logic with datatypes. We provide a logic-based semantics for such an integration, and formalize different DMN reasoning tasks for DKBs. We then consider background knowledge formulated as a description logic (DL) ontology with datatypes, and show how the main verification tasks for DMN in this enriched setting can be formalized as standard DL reasoning services and actually carried out in ExpTime. We discuss the effectiveness of our framework on a case study in maritime security.

Event labeling combining ensemble detectors and background knowledge

Event labeling is the process of marking events in unlabeled data. Traditionally, this is done by involving one or more human experts through an expensive and time- consuming task. In this article we propose an event label- ing system relying on an ensemble of detectors and back- ground knowledge. The target data are the usage log of a real bike sharing system. We first label events in the data and then evaluate the performance of the ensemble and indi- vidual detectors on the labeled data set using ROC analysis and static evaluation metrics in the absence and presence of background knowledge. Our results show that when there is no access to human experts, the proposed approach can be an effective alternative for labeling events. In addition to the main proposal, we conduct a comparative study regarding the various predictive models performance, semi-supervised and unsupervised approaches, train data scale, time series filtering methods, online and offline predictive models, and distance functions in measuring time series similarity.

A LOGICAL FRAMEWORK FOR CONCEPT LEARNING WITH BACKGROUND KNOWLEDGE

A central process in many kinds of learning is the process of generalization or concept learning from a set of training instances (a set of examples and counterexamples) in the presence of some Background Knowledge. Given a set of examples and counterexamples of a concept, the learner induces a general concept that describes all of the positive examples and none of the counterexamples, and is consistent with the Background Knowledge. We present here a logical framework to induce concept descriptions from a given set of examples and counterexamples in the presence of Background Knowledge described by a set of Horn clauses. In particular, we provide: 1) a definition of what is meant by “a learnable concept” from examples and counterexamples in the presence of Background Knowledge described by a set H of Horn clauses”. Broadly speaking, in our framework, a learnable concept C is an atom which is true (valid) in the least Herbrand model of H but false in some other models of H and, 2) a methodology to induce general concept descriptions from concept examples and counterexamples in Horn theories. We give an automatic method, based on a well-founded ordering on the elements of H, which is the basis for checking validity in the least Herbrand model of H. This work generalizes, unifies, and provides a logical framework of previous studies on the concept-learning-from-examples paradigm.