Source Of Background Knowledge Research Articles

Современная филологическая мозаика: Рецензия на монографию "Мировые языки и культуры как объект филологического исследования" : коллективная монография / под общ. ред. О. Д. Вишняковой, А. А. Липгарта. М. : Наука, 2023

The collective monograph “World Languages and Cultures as Object of Philological Research” is evaluated by the authors of the review as a noticeable humanitarian event in Russian science. The advantages of the monograph are a wide range of philological problems under consideration, a high level of philological analysis, involvement in the study of the representatives of Moscow State University named after M.V. Lomonosov and other universities of the Russian Federation engaged in the study of topical issues, including the integration of scientific knowledge in the field of linguistics, cultural studies, literary criticism, linguodidactics. The review considers and positively evaluates the translation and psychological and pedagogical aspects of the monograph: the role of articles in the translation of newspaper headlines; interpretation of metaphor as a direction of cognitive-semantic research; features of the formation and functioning of PR discourse neologisms; the language of advertising in the cultural aspect; translation in the field of business communication; extralinguistic aspects of translation as a source of background knowledge of the translator, the pedagogical role of hypertext in the formation of Internet culture; Russian cultural code in terms of the anthropocentric approach with the example of the cross-cultural analysis of the American film “Onegin”. The monograph has a high theoretical and practical value due to the presence in it of thoughtful and carefully described practical aspects of modern philology, reflecting the numerous aspects of its development. The theoretical value of the monograph could have become more pronounced with a detailed interpretation of the unifying principles for the selection of philological studies.

Изучение неологизмов студентами неязыковых профилей подготовки: основные направления

The article focuses on the importance of learning the process of neologization of a foreign language for the formation of the linguistic competence of university students. The lexical composition of any language is subject to the influence of various linguistic and extralinguistic factors, since it is a fairly open level of the language system. As a result, new words appear in the language - neologisms. At present, the lexical composition of the German language is expanding quite intensively. The author tries to answer the question: Should students of non-linguistic profiles of training in foreign language classes focus on the problem of language neologization, and if so, how can the study of neologisms be used to form students’ foreign language competence? The purpose of the article is to analyze and clarify the main areas of study of neologisms by students of non-linguistic training profiles in the process of foreign language education. The article highlights the main areas of study by students of neologisms (cognitive, general developmental, educational, educational) and the basic tasks that the teacher solves when studying the processes of neologization of the German language: to raise students’ awareness of the history and culture of the people, to acquaint them with the current changes taking place in the language space countries of the language being studied; use new words as a source of background knowledge; explain to students what are the actual ways of word formation; expand their knowledge of a new phenomenon in the German language - Denglisch. Students will also learn about the activities of the society of fighters for the purity of their native language (Gesellschaft für deutsche Sprache). In conclusion, it is noted that the involvement of students in practical classes in the German language to consider such an interesting linguistic phenomenon as neologization motivates them to learn the language, in general, has a positive effect on the success of the formation of foreign language communicative competence.

Background knowledge in ontology matching: A survey

Ontology matching is an integral part for establishing semantic interoperability. One of the main challenges within the ontology matching operation is semantic heterogeneity, i.e. modeling differences between the two ontologies that are to be integrated. The semantics within most ontologies or schemas are, however, typically incomplete because they are designed within a certain context which is not explicitly modeled. Therefore, external background knowledge plays a major role in the task of (semi-) automated ontology and schema matching. In this survey, we introduce the reader to the general ontology matching problem. We review the background knowledge sources as well as the approaches applied to make use of external knowledge. Our survey covers all ontology matching systems that have been presented within the years 2004–2021 at a well-known ontology matching competition together with systematically selected publications in the research field. We present a classification system for external background knowledge, concept linking strategies, as well as for background knowledge exploitation approaches. We provide extensive examples and classify all ontology matching systems under review in a resource/strategy matrix obtained by coalescing the two classification systems. Lastly, we outline interesting and yet underexplored research directions of applying external knowledge within the ontology matching process.

Pervasive conditional selection of driver mutations and modular epistasis networks in cancer.

Cancer driver mutations often display mutual exclusion or co-occurrence, underscoring the key role of epistasis in carcinogenesis. However, estimating the magnitude of epistasis and quantifying its effect on tumor evolution remains a challenge. We develop a method (Coselens) to quantify conditional selection on the excess of nonsynonymous substitutions in cancer genes. Coselens infers the number of drivers per gene in different partitions of a cancer genomics dataset using covariance-based mutation models and determines whether coding mutations in a gene affect selection for drivers in any other gene. Using Coselens, we identify 296 conditionally selected gene pairs across 16 cancer types in the TCGA dataset. Conditional selection affects 25%-50% of driver substitutions in tumors with >2 drivers. Conditionally co-selected genes form modular networks, whose structures challenge the traditional interpretation of within-pathway mutual exclusivity and across-pathway synergy, suggesting a more complex scenario where gene-specific across-pathway epistasis shapes differentiated cancer subtypes.

Report on the Workshop on Personal Knowledge Graphs (PKG 2021) at AKBC 2021

The term personal knowledge graph (PKG) has been broadly used to refer to structured representation of information about a given user, primarily in the form of entities that are personally related to the user. The potential of personal knowledge graphs as a means of managing and organizing personal data, as well as a source of background knowledge for personalizing downstream services, has recently gained increasing attention from researchers in multiple fields, including that of Information Retrieval, Natural Language Processing, and the Semantic Web. The goal of the PKG'21 workshop was to create a forum for researchers and practitioners from diverse areas to present and discuss methods, tools, techniques, and experiences related to the construction and use of personal knowledge graphs, identify open questions, and create a shared research agenda. It successfully brought about a diverse workshop program, comprising an invited keynote, paper presentations, and breakout discussions, as a half-day event at the 3rd Automated Knowledge Base Construction (AKBC'21) conference. The workshop demonstrated that while the concept and research field of personal knowledge graphs is still in its early stages, there are many promising avenues of future development and research that already, and independently, have attracted the interest of several different communities. Date: October 8, 2021. Website: https://pkgs.ws.

REWOrD: Semantic Relatedness in the Web of Data

This paper presents REWOrD, an approach to compute semantic relatedness between entities in the Web of Data representing real word concepts. REWOrD exploits the graph nature of RDF data and the SPARQL query language to access this data. Through simple queries, REWOrD constructs weighted vectors keeping the informativeness of RDF predicates used to make statements about the entities being compared. The most informative path is also considered to further refine informativeness. Relatedness is then computed by the cosine of the weighted vectors. Differently from previous approaches based on Wikipedia, REWOrD does not require any prepro- cessing or custom data transformation. Indeed, it can lever- age whatever RDF knowledge base as a source of background knowledge. We evaluated REWOrD in different settings by using a new dataset of real word entities and investigate its flexibility. As compared to related work on classical datasets, REWOrD obtains comparable results while, on one side, it avoids the burden of preprocessing and data transformation and, on the other side, it provides more flexibility and applicability in a broad range of domains.

The use of proverbs and anti-proverbs in teaching english to secondary school students

Language and culture co-study issues have a permanent tendency to be interpreted from the view point of socio-cultural competence. In this regard, a foreign language teacher faces the problem of creating pedagogical conditions for the effective development of linguistic and socio-cultural competencies of students in order to acquire the ability to use a foreign language in the process of intercultural communication. As one of the ways to solve this problem, the authors of the article suggest using authentic proverbial material in the process of teaching a foreign language. The authors believe that the national culture and modern language trends are most fully and vividly manifested in the paremiological units of the language, which include Proverbs and anti-Proverbs. Their use at different stages of learning a foreign language will become an additional source of background knowledge about the country of a target language and contribute to the evaluation of students’ language and speaking skills. With the help of experimental and diagnostic training, the authors are trying to prove and justify the effectiveness of using authentic proverbial material in the process of teaching a foreign language to secondary school students of the 9th grade. The analysis of empirical data has shown that indicators of socio-cultural and linguistic competence development are the abilities of students to interpret adequately background information and use a set of language tools for intercultural communication. Participants of experimental diagnostic testing objectively improved the indicators of the level of training and demonstrated the abitity to use background knowledge fluently.

Model-based Persian calligraphy synthesis via learning to transfer templates to personal styles

Current software tools for computer generation of Persian calligraphy can be mostly described as conventional fonts and typesetting software, which basically neglect the ‘variations’ of real calligraphy performed by hand, in terms of personalization to different calligraphers’ styles, as well as their statistical characteristics. In this paper, we address the problem of natural-looking Persian calligraphy synthesis via a machine learning based approach, at the level of subwords. Given images of samples written by a calligrapher, we train a parametric model to imitate the style. The core idea is to make use of templates (fonts) as a source of background knowledge, and learn a probabilistic mapping from them to personal styles of calligraphers, which is posed as transformation of attributed graphs using neural networks with sliding windows. This can be understood as adding ‘naturalness’ to a Persian calligraphy font, in essence. We report both objective and subjective evaluations, including the model performance in writer (calligrapher) identification task and Visual Turing Test. The results of the latter suggest that humans are unable to distinguish the calligraphy synthesized by our approach from real calligraphy in many cases.

The Effectiveness of Peer-to-Peer Lecture Films in Inverted Classroom Teaching Scenarios

At HTW Berlin, Germany first year mechanical engineering students are taught material science as one of the fundamental courses with high work load in a blended learning environment with flipped classroom elements. Therefore peer-to-peer lecture films were established as source of theoretical background knowledge provided for self-study periods. Because the teaching method “inverted classroom” and class results directly relate to the quality of the video material one of the columns of lecture video production is the involvement of students in the lecture film production. First year students directly benefit from their fellow student learning experience, needs and perspective on teaching material. From the lecturers perspective students were generally more active and better prepared during class resulting in better grades. Practice examples introduce and evaluate both, the teaching method and videos.

Semantic association computation: a comprehensive survey

Semantic association computation is the process of quantifying the strength of a semantic connection between two textual units, based on different types of semantic relations. Semantic association computation is a key component of various applications belonging to a multitude of fields, such as computational linguistics, cognitive psychology, information retrieval and artificial intelligence. The field of semantic association computation has been studied for decades. The aim of this paper is to present a comprehensive survey of various approaches for computing semantic associations, categorized according to their underlying sources of background knowledge. Existing surveys on semantic computation have focused on a specific aspect of semantic associations, such as utilizing distributional semantics in association computation or types of spatial models of semantic associations. However, this paper has put a multitude of computational aspects and factors in one picture. This makes the article worth reading for those researchers who want to start off in the field of semantic associations computation. This paper introduces the fundamental elements of the association computation process, evaluation methodologies and pervasiveness of semantic measures in a variety of fields, relying on natural language semantics. Along the way, there is a detailed discussion on the main categories of background knowledge sources, classified as formal and informal knowledge sources, and the underlying design models, such as spatial, combinatorial and network models, that are used in the association computation process. The paper classifies existing approaches of semantic association computation into two broad categories, based on their utilization of background knowledge sources: knowledge-rich approaches; and knowledge-lean approaches. Each category is divided further into sub-categories, according to the type of underlying knowledge sources and design models of semantic association. A comparative analysis of strengths and limitations of various approaches belonging to each research stream is also presented. The paper concludes the survey by analyzing the pivotal factors that affect the performance of semantic association measures.

CBSSD: community-based semantic subgroup discovery

Modern data mining algorithms frequently need to address the task of learning from heterogeneous data, including various sources of background knowledge. A data mining task where ontologies are used as background knowledge in data analysis is referred to as semantic data mining. A specific semantic data mining task is semantic subgroup discovery: a rule learning approach enabling ontology terms to be used in subgroup descriptions learned from class labeled data. This paper presents Community-Based Semantic Subgroup Discovery (CBSSD), a novel approach that advances ontology-based subgroup identification by exploiting the structural properties of induced complex networks related to the studied phenomenon. Following the idea of multi-view learning, using different sources of information to obtain better models, the CBSSD approach can leverage different types of nodes of the induced complex network, simultaneously using information from multiple levels of a biological system. The approach was tested on ten data sets consisting of genes related to complex diseases, as well as core metabolic processes. The experimental results demonstrate that the CBSSD approach is scalable, applicable to large complex networks, and that it can be used to identify significant combinations of terms, which can not be uncovered by contemporary term enrichment analysis approaches.

Засоби та методи аналізу неструктурованих даних

Analysis of the current trends in the unstructured text data wide usage and the development of software tools for their processing causes the high urgency of this research direction and the necessity of intelligent information systems in such processing. A signigicant part of Big Data consists of unstructured texts that require the further development of specific Text Mining and algorythms of machine learning. Unstructured data consisting of natural language text in the general case, do not have a predetermined data model. Their ambiguity, heterogeneity and context dependence considerably complicate the classification of documents, the identification of their components and the automated obtaining of user-oriented knowledge from their content, while the large volumes and dynamism of such data do not involve efficient manual processing. The means and methods of data structuring, their various software implementations are considered. The prospects of using background knowledge for such structuring are analyzed. The feasibility of application such W3C standards as RDF and OWL is substantiated. The use of semantic Wiki-technologies for development of distributed information resources simplifies the process of natural text structuring by users and also generates the source of background knowledge for the analysis of arbitrary texts of the corresponding domains. The models and methods proposed in the work allow to improve this process. Problems in programming 2019; 1: 57-77

Data synthesis based on generative adversarial networks

Privacy is an important concern for our society where sharing data with partners or releasing data to the public is a frequent occurrence. Some of the techniques that are being used to achieve privacy are to remove identifiers, alter quasi-identifiers, and perturb values. Unfortunately, these approaches suffer from two limitations. First, it has been shown that private information can still be leaked if attackers possess some background knowledge or other information sources. Second, they do not take into account the adverse impact these methods will have on the utility of the released data. In this paper, we propose a method that meets both requirements. Our method, called table-GAN , uses generative adversarial networks (GANs) to synthesize fake tables that are statistically similar to the original table yet do not incur information leakage. We show that the machine learning models trained using our synthetic tables exhibit performance that is similar to that of models trained using the original table for unknown testing cases. We call this property model compatibility . We believe that anonymization/perturbation/synthesis methods without model compatibility are of little value. We used four real-world datasets from four different domains for our experiments and conducted indepth comparisons with state-of-the-art anonymization, perturbation, and generation techniques. Throughout our experiments, only our method consistently shows balance between privacy level and model compatibility.

VB-MK-LMF: fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization

BackgroundComputational fusion approaches to drug-target interaction (DTI) prediction, capable of utilizing multiple sources of background knowledge, were reported to achieve superior predictive performance in multiple studies. Other studies showed that specificities of the DTI task, such as weighting the observations and focusing the side information are also vital for reaching top performance.MethodWe present Variational Bayesian Multiple Kernel Logistic Matrix Factorization (VB-MK-LMF), which unifies the advantages of (1) multiple kernel learning, (2) weighted observations, (3) graph Laplacian regularization, and (4) explicit modeling of probabilities of binary drug-target interactions.ResultsVB-MK-LMF achieves significantly better predictive performance in standard benchmarks compared to state-of-the-art methods, which can be traced back to multiple factors. The systematic evaluation of the effect of multiple kernels confirm their benefits, but also highlights the limitations of linear kernel combinations, already recognized in other fields. The analysis of the effect of prior kernels using varying sample sizes sheds light on the balance of data and knowledge in DTI tasks and on the rate at which the effect of priors vanishes. This also shows the existence of “small sample size” regions where using side information offers significant gains. Alongside favorable predictive performance, a notable property of MF methods is that they provide a unified space for drugs and targets using latent representations. Compared to earlier studies, the dimensionality of this space proved to be surprisingly low, which makes the latent representations constructed by VB-ML-LMF especially well-suited for visual analytics. The probabilistic nature of the predictions allows the calculation of the expected values of hits in functionally relevant sets, which we demonstrate by predicting drug promiscuity. The variational Bayesian approximation is also implemented for general purpose graphics processing units yielding significantly improved computational time.ConclusionIn standard benchmarks, VB-MK-LMF shows significantly improved predictive performance in a wide range of settings. Beyond these benchmarks, another contribution of our work is highlighting and providing estimates for further pharmaceutically relevant quantities, such as promiscuity, druggability and total number of interactions.

Curious Cat--Mobile, Context-Aware Conversational Crowdsourcing Knowledge Acquisition

Scaled acquisition of high-quality structured knowledge has been a longstanding goal of Artificial Intelligence research. Recent advances in crowdsourcing, the sheer number of Internet and mobile users, and the commercial availability of supporting platforms offer new tools for knowledge acquisition. This article applies context-aware knowledge acquisition that simultaneously satisfies users’ immediate information needs while extending its own knowledge using crowdsourcing. The focus is on knowledge acquisition on a mobile device, which makes the approach practical and scalable; in this context, we propose and implement a new KA approach that exploits an existing knowledge base to drive the KA process, communicate with the right people, and check for consistency of the user-provided answers. We tested the viability of the approach in experiments using our platform with real users around the world, and an existing large source of common-sense background knowledge. These experiments show that the approach is promising: the knowledge is estimated to be true and useful for users 95% of the time. Using context to proactively drive knowledge acquisition increased engagement and effectiveness (the number of new assertions/day/user increased for 175%). Using pre-existing and newly acquired knowledge also proved beneficial.

Taxo-Semantics: Assessing similarity between multi-word expressions for extending e-catalogs

Taxonomies, also named directories, are utilized in e-catalogs to classify goods in a hierarchical manner with the help of concepts. If there is a need to create new concepts when modifying the taxonomy, the semantic similarity between the provided concepts has to be assessed properly. Existing semantic similarity assessment techniques lack in a comprehensive support for e-commerce, as those are not supporting multi-word expressions, multilingualism, the import/export to relational databases, and supervised user-involvement. This paper proposes Taxo-Semantics, a decision support system that is based on the progress in taxonomy matching to match each expression against various sources of background knowledge. The similarity assessment is based on providing three different matching strategies: a lexical-based strategy named Taxo-Semantics-Label, the strategy Taxo-Semantics-Bk, which is using different sources of background knowledge, and the strategy Taxo-Semantics-User that is providing user-involvement. The proposed system includes a translating service to analyze non-English concepts with the help of the WordNet lexicon, can parse taxonomies of relational databases, supports user-involvement to match single sequences with WordNet, and is capable to analyze each sequence as (sub)-taxonomy. The three proposed matching strategies significantly outperformed existing techniques. Taxo-Semantics-Label could improve the accuracy result by more than 7% as compared to state-of-the-art lexical techniques. Taxo-Semantics-Bk could improve the accuracy compared to structure-based techniques by more than 8%. And, Taxo-Semantics-User could additionally increase the accuracy by on average 23%.

Improving Event Causality Recognition with Multiple Background Knowledge Sources Using Multi-Column Convolutional Neural Networks

We propose a method for recognizing such event causalities as "smoke cigarettes" → "die of lung cancer" using background knowledge taken from web texts as well as original sentences from which candidates for the causalities were extracted. We retrieve texts related to our event causality candidates from four billion web pages by three distinct methods, including a why-question answering system, and feed them to our multi-column convolutional neural networks. This allows us to identify the useful background knowledge scattered in web texts and effectively exploit the identified knowledge to recognize event causalities. We empirically show that the combination of our neural network architecture and background knowledge significantly improves average precision, while the previous state-of-the-art method gains just a small benefit from such background knowledge.

Automatic background knowledge selection for matching biomedical ontologies.

Ontology matching is a growing field of research that is of critical importance for the semantic web initiative. The use of background knowledge for ontology matching is often a key factor for success, particularly in complex and lexically rich domains such as the life sciences. However, in most ontology matching systems, the background knowledge sources are either predefined by the system or have to be provided by the user. In this paper, we present a novel methodology for automatically selecting background knowledge sources for any given ontologies to match. This methodology measures the usefulness of each background knowledge source by assessing the fraction of classes mapped through it over those mapped directly, which we call the mapping gain. We implemented this methodology in the AgreementMakerLight ontology matching framework, and evaluate it using the benchmark biomedical ontology matching tasks from the Ontology Alignment Evaluation Initiative (OAEI) 2013. In each matching problem, our methodology consistently identified the sources of background knowledge that led to the highest improvements over the baseline alignment (i.e., without background knowledge). Furthermore, our proposed mapping gain parameter is strongly correlated with the F-measure of the produced alignments, thus making it a good estimator for ontology matching techniques based on background knowledge.

Enriching semantic knowledge bases for opinion mining in big data applications

This paper presents a novel method for contextualizing and enriching large semantic knowledge bases for opinion mining with a focus on Web intelligence platforms and other high-throughput big data applications. The method is not only applicable to traditional sentiment lexicons, but also to more comprehensive, multi-dimensional affective resources such as SenticNet. It comprises the following steps: (i) identify ambiguous sentiment terms, (ii) provide context information extracted from a domain-specific training corpus, and (iii) ground this contextual information to structured background knowledge sources such as ConceptNet and WordNet. A quantitative evaluation shows a significant improvement when using an enriched version of SenticNet for polarity classification. Crowdsourced gold standard data in conjunction with a qualitative evaluation sheds light on the strengths and weaknesses of the concept grounding, and on the quality of the enrichment process.

DIVERSIFYING SEMANTIC ENTITY SEARCH: INDEPENDENT COMPONENT ANALYSIS APPROACH

Structured Web data are increasingly accessed using information retrieval methods and information retrieval increasingly relies on structured background knowledge. As users' searches are often directed towards finding information about entities rather than text documents, a key affordance of semantic search is the ability to retrieve relevant information about entities more precisely by utilizing the rich structured descriptions and background knowledge. Entity search also poses challenges for information retrieval methods. Entity descriptions are often short and conventional search term matching alone can be insufficient. As a consequence, the search engine should be able to increase the recall of the returned results and select a representative set of entities for a user; to diversify search results. This paper presents an approach to diversify entity search by using semantics present and inferred from the initial entity search results. Our approach utilizes ontologies as a source of background knowledge to improve recall of entity retrieval and independent component analysis to detect independent latent components shared by the entities. The search results are then diversified by selecting a representative set of entities based on their membership in the independent components. We demonstrate the performance of our approach through retrieval experiments conducted by using a real-world dataset composed from four entity databases. The results suggest that our approach can significantly improve effectiveness and diversity of entity search.