Knowledge Of Entities Research Articles

Territorial Knowledge Ontology as a Guide for the Identification of Resource of the Territory Toward Sustainability

AbstractRepresentation of territorial knowledge based on the ontology is an approach which explains the nature and reasoning of this knowledge for sustainability. This research proposes an ontology of domain according to the principles for modelling an ontology. This proposed ontology is named DOTK (descriptive ontology for territorial knowledge). DOTK ontology has specialized the entities of territorial knowledge for sustainability and its aim is enhancing the sustainable knowledge of actors within industries. This ontology is a guide for identifying an operational ontology of real example. This research show that ontology of domain can assist to the identifying of tangible and intangible resources of the local territory. So, these identified resources can help the industries and territory for sustainable objectives and improve the available territorial knowledge within local or regional industries.

Cosmopolitanizing Colonial Memories in Germany

Cosmopolitanizing Colonial Memories in Germany

Efficient heterogeneous proximity preserving network embedding model

We study the problem of representation learning in heterogeneous information networks. Its unique challenges come from the existence of multiple types of vertices and edges. Existing proximity-based network embedding techniques ignore the type information when evaluating the proximity and limits their usage in heterogeneous scenario. In this paper, we propose a heterogeneous proximity preserving network embedding model via meta path guided random walk, which is capable of capturing the high-order proximity between vertices specified by the given path. To improve the learning efficiency, we introduce a sampling based learning strategy which can incrementally learn representations. We conduct experiments on two real world heterogeneous information networks. Experimental results on several mining tasks prove the effectiveness of our approach over many competitive baselines. The model is very efficient and is able to learn embeddings for large networks both in offline and online scenarios. Besides, for expert system, our approach can be applied to improve the representation of knowledge entities by depicting the knowledge base as a heterogeneous information network.

A Key Management of Security to Design Enhanced Apache and Rhino Utilities in Big Data Using Hadoop

Hadoop is a dispersed information dispensation platform intended for investigate big data. Information is emergent at a massive value in the current charity. Entity of the best part popular knowledge existing intended for managing and dispensation to facilitate vast quantity of information is the Hadoop environment. Present be disparate conduct to accumulate and development huge quantity of information. Hadoop is broadly utilized, lonely of the majority popular strategy to accumulate enormous quantity of information and progression them in equivalent. at the same time as store insightful information, security performing an significant responsibility to stay it secure. Security is not that greatly measured while Hadoop be primarily projected. The early utilize of Hadoop was association huge quantity of shared network information so privacy of the accumulate information and. essentially user services in Hadoop be not authenticated; Hadoop is projected code on a disseminated compilation of technology so exclusive of correct authentication and any person might present and it would be implement. The outstanding Utilities encompass in progress to extend the protection of Hadoop. These utilities are using Enhanced Rhino Utility and Enhanced Sentry Utility. Enhanced Rhino develop splittable crypto codec to deliver encryption intended for the information to facilitate is accumulate in Hadoop dispersed conspirator organization. Moreover develop the essential authentication by execute Hadoop single sign on which prevents repeated authentication of the users accessing the same services with various times. While the authorization point of examination Enhanced Rhino utility deliver severance based authorization designed for Hbase. Enhanced Sentry utility give fine-grained entrance organizes by behind responsibility based authorization which different services can be bound to it to grant authorization for their users. It is probable to merge security enhancements which cover the Enhanced Rhino utility and Enhanced Sentry Utility to supporting get enhanced the presentation and offer enhanced mechanism to secure Hadoop. In this paper, the security of the organization in Hadoop is assess and different security enhancements to be proposed, enchanting into inspection security enhancement comprehensive by the two utilities, Enhanced Rhino utility and Enhanced Sentry utility, in provisions of encryption, authentication, and authorization. This paper proposes a number of sophisticated security improvements on the federal authentication and organization implementation made by enhanced Rhino Utility based on the HDFS data encryption scheme using the ARIA encryption algorithm on Hadoop.

Exploring High-Order User Preference on the Knowledge Graph for Recommender Systems

To address the sparsity and cold-start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve the performance of recommendation. In this article, we consider the knowledge graph (KG) as the source of side information. To address the limitations of existing embedding-based and path-based methods for KG-aware recommendation, we propose RippleNet , an end-to-end framework that naturally incorporates the KG into recommender systems. RippleNet has two versions: (1) The outward propagation version, which is analogous to the actual ripples on water, stimulates the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user’s potential interests along links in the KG. The multiple “ripples” activated by a user’s historically clicked items are thus superposed to form the preference distribution of the user with respect to a candidate item. (2) The inward aggregation version aggregates and incorporates the neighborhood information biasedly when computing the representation of a given entity. The neighborhood can be extended to multiple hops away to model high-order proximity and capture users’ long-distance interests. In addition, we intuitively demonstrate how a KG assists with recommender systems in RippleNet, and we also find that RippleNet provides a new perspective of explainability for the recommended results in terms of the KG. Through extensive experiments on real-world datasets, we demonstrate that both versions of RippleNet achieve substantial gains in a variety of scenarios, including movie, book, and news recommendations, over several state-of-the-art baselines.

Manufacturing Knowledge Graph: A Connectivism to Answer Production Problems Query With Knowledge Reuse

Manufacturing knowledge (MK) is enjoying a “new golden age” in the academic domain, marked by vast reuse to support product-related production problems (PPs) solving decision making for manufacturing enterprises in the industry sector. However, the practice of MK reuse and research is fragmented and insufficient, which cannot be mature to provide a systemic solution for that a decision-maker has to consider the involving issues: how MK can be used earlier and rightly; what kind of practical problems can be solved? In order to answer those interconnecting issues, this paper firstly proposes a connectivism framework to clarify the compressive relationship of problem-to-problem, knowledge-to-knowledge and problem-to-knowledge with knowledge integration, knowledge matching, and problem-solving layers. Then, based on the framework, an ontology-based MK graph (MKG) is constructed with a unified MK-filter to collect and integrate multifactor and multilevel MK, and a graph-oriented meta-knowledge model (MKM) is proposed to represent the details between the knowledge entities (i.e., concept and instance), which also shows the contribution to knowledge reasoning. After that, driven by a structure temporal query (i.e., 5W2H), a semantics-based knowledge computation is developed to compute the intrinsic term similarity (IS) and relational term similarity (RS) between two knowledge entities in the MKG. Finally, a case study is taken to demonstrate the effectiveness and performance of the proposed methods.

Mining knowledge entities in mHealth literature

ABSTRACTThis study introduces a work in progress project that demonstrates how research trends can be captured by mining domain knowledge entities in scholarly articles. From Web of Science, 5,600 mHealth related journal articles published between 2008 and 2018 were downloaded and analyzed. The preliminary results demonstrate how text mining techniques facilitate capturing knowledge transit in mHealth.

Retrieval system enhanced by fine‐grained knowledge entities

ABSTRACTAlong with the massive increase of academic full texts, users now have a growing need for fine‐grained academic literature retrieval. However, few studies have attempted to build domain knowledge retrieval system for academic literature in information science. Compared with other disciplines, information science pays more attention to the domain knowledge entities sequence including data resources, models and software. To fill the gap, this paper use academic full texts from JASIST. Using SVM, Text‐CNN, Bi‐LSTM and BERT to extract the knowledge entities sequence including data resource, model, software. The highest Precision, Recall and F1 are 92.45%, 93.86% and 93.14%, respectively. And the knowledge entity sequence indexing system is built based on the extracted knowledge entities sequences. It was found that the MRR score of the knowledge entities sequence retrieval system was higher by 13% as compared with the full text retrieval system.

Investigating drug–disease interactions in drug–symptom–disease triples via citation relations

With the growth in biomedical literature, the necessity of extracting useful information from the literature has increased. One approach to extracting biomedical knowledge involves using citation relations to discover entity relations. The assumption is that citation relations between any two articles connect knowledge entities across the articles, enabling the detection of implicit relationships among biomedical entities. The goal of this article is to examine the characteristics of biomedical entities connected via intermediate entities using citation relations aided by text mining. Based on the importance of symptoms as biomedical entities, we created triples connected via citation relations to identify drug–disease pairs with shared symptoms as intermediate entities. Drug–disease interactions built via citation relations were compared with co‐occurrence‐based interactions. Several types of analyses were adopted to examine the properties of the extracted entity pairs by comparing them with drug–disease interaction databases. We attempted to identify the characteristics of drug–disease pairs through citation relations in association with biomedical entities. The results showed that the citation relation‐based approach resulted in diverse types of biomedical entities and preserved topical consistency. In addition, drug–disease pairs identified only via citation relations are interesting for clinical trials when they are examined using BITOLA.

An ontology-oriented knowledge methodology for process planning in additive layer manufacturing

Abstract The goal of this research is the generation of a novel knowledge with process-oriented ontology and the informal model. With regard to the proposed ontology, it establishes an improvement to related ontologies because it involves the demands of fabrication engineering and, specifically, the layer-upon-layer manufacturing planning process with various AM systems. Generally, task of AM planning indicates to make repeated and essential decisions which are always on the basis of the engineers’ knowledge and experience in additive manufacturing. Hence, it is a suitable field towards the execution of a knowledge-based engineering system. To represent the knowledge at an upper tier, the IDEF0 diagrams is introduced for identifying the sequence of tasks contained in the AM planning. They are a vital resources for defining the sequence of tasks and the messages flow. Afterward, these messages are analyzed thoroughly by applying schematic graphs, and then they are categorized into knowledge segments. Eventually, each knowledge segment is further divided into knowledge entities. At the same time, the relationships among them are also defined. Meanwhile, knowledge modeling involved generating an ontology of design feature which is utilized as a general information model in both computer-aided design and process planning, expression of fabrication criteria that depict the basis and properties for picking fabrication parameters. In a first method, the ontology has been examined utilizing an essential activity in AM planning: the task related to the confirmation of parameters over component quality. In this task, decisions have to be made in the orientation, slicing and the other process parameters. In this research, the differences between geometric and dimensional tolerance fabrication is considered in the generated methodology. The knowledge needed to aid all decisions is expressed utilizing the proposed ontology.

AMBIENT INTELLIGENCE: PRINCIPLES,CURRENT TRENDS, FUTURE DIRECTIONS

In this paper we have presented the involved principles, trends, future directions of ambient intelligence. in the first section we have elucidated concept of ambient intelligence with the prevalent need of intelligent communication with the help of designing knowledgeable entities. We have presented the design process of I-blocks with its inherent merits. we have also discussed the various design concepts of ambient intelligence objects. Finally, we presented the current research directions to motivate societal needs of human beings.

Knowledge management of automobile system failures through development of failure knowledge ontology from maintenance experience

PurposeThe purpose of this paper is to develop an ontological model of failure knowledge of automobile systems that will enhance the knowledge management of automobile system failures, which will help for design and maintenance of automobiles. Failure knowledge of automobile systems and components gained through maintenance and repair can mitigate future failures, if integrated in the design. This is an outcome of this paper.Design/methodology/approachA failure coding scheme is developed for assimilating various entities of automobile failure knowledge and an ontological model is developed for its systematic structuring and representation. The developed failure code is a combination of alphanumeric and numeric code that incorporates ingredients of the failure knowledge, which will help database management, with reduced data entry time and storage space.FindingsThe maintenance of automobiles not only brings back the systems into operating conditions but also convey a lot of information regarding the failures. This is a useful input to the designers in development of reliable and maintainable automobile systems. A knowledge base can be created for automobile systems/components failures from their maintenance and service experience.Research limitations/implicationsDeveloped ontological model of automobile failure knowledge gained through maintenance experience can be shared across automobile manufacturers and service providers. This would help in design improvements, with ease and efficient undertaking of maintenance activities. This paper proposes the conceptual ontology structure, which is populated with three cases of automobile maintenance.Originality/valueThis research work is a first attempt to develop an ontological model for automobile failures from their maintenance and service experience. The novelty of the work is in its explicit consideration of all knowledge related to failures and maintenance of automobile systems, with their coding and structuring.

On the essay as a paroxismal form of the utopian thinking: present living pasts

This paper explores the role of the essay in the expression of knowledge and argues that this discursive style, in contrast with the academic paper as a form to disseminate knowledge, allows us to consider possible utopias, since it provides a link between past and present, between memory and oblivion, between the archaic and the prospective. This paper argues that the essay is a paroxismal narrative which rightly expresses the convergence of both past and present collective emotions in opposition to the scientificist rationality which is characteristic of the academic paper. This paper aims to reclaim the role of this discursive formula in the production of collective knowledge as well as to place it in its right context: that of the occurring pre-texts, particularly in the debate on the objectivity and subjectivity of knowledge-related procedures that takes place between the dynamics of the essay and the logic of the “scientific” paper. The link between the essay and the awareness of the collectively lived present, the connection of present utopian archaisms, will be shown by comparing the Medieval Universitates against the enlighted universities and the initiatory rites against the evidence of the sacred data. The goal of this comparison is to highlight knowledge-related writings that integrate emotions as knowledge entities.

Knowledge entity learning and representation for ontology matching based on deep neural networks

We study the task of ontology matching that is used mainly for solving the semantic heterogeneity problems, which concentrates on finding semantically related entities between different ontologies. Many previous works exploit the character-level or token-level information of the descriptions of an entity in ontology directly when applying the string-based matcher or token based matcher to find the corresponding entities. They ignored the higher level correlations between different descriptions of an entity. To address this problem, we propose a representation learning method based on deep neural networks which aim at learning the high level abstract representations of the input entity. Particularly, the representations of the entities are learned in an unsupervised way firstly, and then fine-tuned in a supervised manner with the training data. The experiment results show that our approaches can learn useful representations for entities from its descriptive information to better measure the similarity between entities.

Comparative evaluation of bibliometric content networks by tomographic content analysis: An application to Parkinson's disease

To understand the current state of a discipline and to discover new knowledge of a certain theme, one builds bibliometric content networks based on the present knowledge entities. However, such networks can vary according to the collection of data sets relevant to the theme by querying knowledge entities. In this study we classify three different bibliometric content networks. The primary bibliometric network is based on knowledge entities relevant to a keyword of the theme, the secondary network is based on entities associated with the lower concepts of the keyword, and the tertiary network is based on entities influenced by the theme. To explore the content and properties of these networks, we propose a tomographic content analysis that takes a slice‐and‐dice approach to analyzing the networks. Our findings indicate that the primary network is best suited to understanding the current knowledge on a certain topic, whereas the secondary network is good at discovering new knowledge across fields associated with the topic, and the tertiary network is appropriate for outlining the current knowledge of the topic and relevant studies.

Predicting the Future of Project Management Research

Although there has been much recent research on Project Management (PM), this research field is still relatively very young with many aspects still to be uncovered. Studies of the present literature have documented that, as no surprise, new research topics and emerging knowledge domains within this research field continue to appear at a rapid pace. This study aims to predict, based on a quantitative approach, the potential research areas that could appear in the foreseeable future of PM research. As carriers of PM knowledge entities, this study utilised different keywords that had been extracted from all publications of a reputed PM journal over a period of five years (i.e. 2009-2013). By applying the quantitative approach on this research dataset, this study made a projection about the future potential PM research topics. This projection will bring a competitive advantage to PM stakeholders by choosing a suitable field for investment, help aspiring PM researchers to choose a promising topic, and guide PM funding agencies as well as policy makers in distributing available research funds.

Time-aware link prediction to explore network effects on temporal knowledge evolution

Quantitative measurements of bibliometrics based on knowledge entities (i.e., keywords) improve competencies in tracking the structure and dynamic development of various scientific domains. Co-word...

Aspects of the Acceptance of Hanja and Its Identity in the Korean Society

This paper examines aspects of the acceptance and identity of Hanja (Chinese characters borrowed from Chinese and incorporated into the Korean language with Korean pronunciation) in the Korean society, and analyzes the literal and social mechanism employed for continued acceptance by the Korean public. The analysis illuminates insight into the social status and recognition of Hanja in the Korean society. In the ancient Korean society, Hanja was identified as a textual existence dominating the thoughts of the intellectuals and reproducing knowledge. Though its stature and status of the past are no longer present, Hanja still retains a remnant of their past stature in the contemporary Korean culture. In Korea’s popular culture, Hanja is used in various kinds of notices, signs, trademarks, and advertisements. In the consumer market, in particular, Hanja represents the Oriental sentiments and beauty and traditional authority, surpassing the realm of the text beyond borders of the elegant (􆘆) or the folksy (􁼘) to the level of an iconic entity of knowledge, whose symbolic images are used as marketing strategies in the consumer market.

SKO Types: an entity-based scientific knowledge objects metadata schema

Purpose – This paper aims to propose an entity-based scientific metadata schema, i.e. Scientific Knowledge Object (SKO) Types. During the past 50 years, many metadata schemas have been developed in a variety of disciplines. However, current scientific metadata schemas focus on describing data, but not entities. They are descriptive, but few of them are structural and administrative. Design/methodology/approach – To describe entities in scientific knowledge, the theory of SKO Types is proposed. SKO Types is an entity-based theory for representing and linking SKOs. It defines entities, relationships between entities and attributes of each entity in the scientific domain. Findings – In scientific knowledge management, SKO Types serves as the basis for relating entities, entity components, aggregated entities, relationships and attributes to various tasks, e.g. linked entity, rhetorical structuring, strategic reading, semantic annotating, etc., that users may perform when consulting ubiquitous SKOs. Originality/value – SKO Types can be widely applied in various digital libraries and scientific knowledge management systems, while for the existing legacy of scientific publications and their associated metadata schemas.

Autonomous real time route guidance in inter-vehicular communication urban networks

This paper presents an inter-vehicular communication (IVC)-based algorithm for real-time route guidance in urban traffic networks. The algorithm enables communication between searcher vehicle and candidate vehicles whose origin node matches the destination node of the searcher vehicle, and traveling in the opposite direction. The data entities of knowledge sharing among the vehicles and the algorithmic procedure as well as the conditions for information sharing are presented in detail. The mathematical formulation of the procedure is also presented. A microscopic simulation model is utilized to assess the effectiveness of the algorithm against the benchmark shortest path algorithm. Average travel time, and overall network productivity are presented to measure the effectiveness of the algorithm. Simulation runs are conducted under various network congestion levels, link speeds and link lengths in order to evaluate the network productivity, and the travel time measures of the presented IVC algorithm compared to the benchmark shortest path algorithm, decentralized route guidance systems as well as other IVC-based algorithms.