INSTANCE MIGRATION BETWEEN ONTOLOGIES HAVING STRUCTURAL DIFFERENCES

2011 ◽  
Vol 20 (06) ◽  
pp. 1127-1156 ◽  
Author(s):  
MAXIM DAVIDOVSKY ◽  
VADIM ERMOLAYEV ◽  
VYACHESLAV TOLOK
Keyword(s):  
Knowledge Management ◽  
Life Cycle ◽  
Structural Changes ◽  
Software Framework ◽  
Ontology Alignment ◽  
Transformation Rules ◽  
Structural Differences ◽  
Prototype Software ◽  
Descriptive Theories ◽  
Future Work

Ontology instance migration is one of the complex and not fully solved problems in knowledge management. A solution is required when the ontology schema evolves in the life cycle and the assertions have to be transferred to the newer version. The problem may become more complex in distributed settings when, for example, several autonomous software entities use and exchange partial assertional knowledge in a domain that is formalized by different though semantically overlapping descriptive theories. Such an exchange is essentially the migration of the assertional part of an ontology to other ontologies belonging to or used by different entities. The paper presents our method and tool for migrating instances between the ontologies that have structurally different but semantically overlapping schemas. The approach is based on the use of the manually coded transformation rules describing the changes between the input and the output ontologies. The tool is implemented as a plug-in for the ProjectNavigator prototype software framework. The article also reports the results of our three evaluation experiments. In these experiments we evaluated the degree of complexity in the structural changes to which our approach remains valid. We also chose the ontology sets in one of the experiments to make the results comparable with the ontology alignment software. Finally we checked how well our approach scales with the increase of the quantity of the migrated ontology instances to the numbers that are characteristic to industrial ontologies. In our opinion the evaluation results are satisfactory and suggest some directions for the future work.

Neuronal mRNA Translation in Addiction

2018 ◽  
Author(s):  
Emma Puighermanal ◽  
Emmanuel Valjent
Keyword(s):  
Structural Changes ◽  
Drugs Of Abuse ◽  
Mrna Translation ◽  
Cell Types ◽  
Behavioral Changes ◽  
Translational Machinery ◽  
Addictive Drugs ◽  
Specific Mrnas ◽  
Cell Type Specific ◽  
Future Work

Addictive drugs trigger persistent synaptic and structural changes in the neuronal reward circuits that are thought to underlie the development of drug-adaptive behavior. While transcriptional and epigenetic modifications are known to contribute to these circuit changes, accumulating evidence indicates that altered mRNA translation is also a key molecular mechanism. This chapter reviews recent studies demonstrating how addictive drugs alter protein synthesis and/or the translational machinery and how this leads to neuronal circuit remodeling and behavioral changes. Future work will determine precisely which neuronal circuits and cell types participate in these changes. The chapter summarizes current methodologies for identifying cell type-specific mRNAs whose translation is affected by drugs of abuse and gives recent examples of the mechanistic insights into addiction they provide.

scholarly journals Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jiao Li ◽  
Jakob Seidlitz ◽  
John Suckling ◽  
Feiyang Fan ◽  
Gong-Jun Ji ◽  
...  
Keyword(s):  
Gene Expression ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Structural Changes ◽  
Brain Regions ◽  
Cell Type ◽  
Major Depressive ◽  
Structural Differences ◽  
Neuroimaging Data ◽  
Cell Type Specific

AbstractMajor depressive disorder (MDD) has been shown to be associated with structural abnormalities in a variety of spatially diverse brain regions. However, the correlation between brain structural changes in MDD and gene expression is unclear. Here, we examine the link between brain-wide gene expression and morphometric changes in individuals with MDD, using neuroimaging data from two independent cohorts and a publicly available transcriptomic dataset. Morphometric similarity network (MSN) analysis shows replicable cortical structural differences in individuals with MDD compared to control subjects. Using human brain gene expression data, we observe that the expression of MDD-associated genes spatially correlates with MSN differences. Analysis of cell type-specific signature genes suggests that microglia and neuronal specific transcriptional changes account for most of the observed correlation with MDD-specific MSN differences. Collectively, our findings link molecular and structural changes relevant for MDD.

Digital Platforms

2018 ◽  
pp. 1-43 ◽  
Keyword(s):  
Knowledge Management ◽  
Life Cycle ◽  
Business Model ◽  
Dynamic Capabilities ◽  
Business Models ◽  
Building Blocks ◽  
Management Tool ◽  
Digital Platforms ◽  
Research Platform ◽  
Key Aspects

This chapter reviews the following key aspects of platform research: platform strategy, dynamic capabilities, and business models. The main platform typologies and basic definitions are described first. It provides a brief summary of the literature relating to arguing platform strategy, platform life cycle, platform building blocks, and business models. A platform strategy categorization taxonomy and platform practical strategies of preventing platform disintermediation are developed. The main types of platform business models are introduced. The multi-sided platform business model pattern (MSP BMP) is designed. MSP BMP is used as a basic conceptual framework and knowledge management tool for describing, analyzing, and interpreting non-price instruments used by digital platforms, especially platform intermediaries.

Digital Technology Advancements in Knowledge Management in Domestic Tour Products in the Russian Federation

2021 ◽  
pp. 135-148
Author(s):  
Vardan Mkrttchian ◽  
Viacheslav Voronin
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Supply Chain ◽  
Life Cycle ◽  
Russian Federation ◽  
Practical Implementation ◽  
Digital Twins ◽  
Software Modules ◽  
Multi Agent ◽  
The Russian Federation

This chapter discusses the capabilities with problem-oriented digital twin avatars, supply chain, volumetric hybrid, and federated-consistent blockchain use to the nature of knowledge. The goal of this chapter is a theoretical study and practical implementation in the form of basic models and software modules and artificial intelligence algorithms in managing the life cycle of an internal Russian tour product. A laboratory for digitization and management, using multi-agent models of intelligent digital twins-avatars, is created. The purpose of these studies is to solve a scientific problem.

Knowledge-Based Strategies and Systems

2011 ◽  
pp. 1-62 ◽  
Author(s):  
Meir Russ ◽  
J. Greg Jones ◽  
Jeannette K Jones
Keyword(s):  
Knowledge Management ◽  
Life Cycle ◽  
Early Stage ◽  
Management Strategies ◽  
Academic Research ◽  
Business Strategies ◽  
Knowledge Based Systems ◽  
Academic Literature ◽  
Knowledge Based ◽  
New Research

Knowledge management strategies and implementation of knowledge-based systems have gained importance over the last decade. However, many organizations are not able to develop “winning” knowledge-based strategies and others waste signifi- cant monies when the knowledge-based systems they invest in fail to produce the desired results. To address the challenges faced by these organizations, a recently developed framework for strategic dilemmas was proposed by Russ, Jones, and Fineman (2006) to aid in the development of knowledge-based (KB) strategies. The framework (C3EEP) identifies six dilemmas that organizations should balance when considering their knowledge management and business strategies. Examples of such dilemmas include the balance between concealment (secrecy) vs. transparency, complementary vs. destroying, and the balance between exploitation and exploration. The framework compliments the six stages in the life cycle of KB systems (KBS) as identified by the academic literature that discusses the development and implementation of KBS from the information systems (IS) perspective (e.g., Lytras, Pouloudi, & Poulymenakou, 2002; Nissen, Kamel, & Sengupta, 2000). This interaction/ linkage between KB strategies and systems is crucial for the success of both. Academic research supports the complex relationship between the two. However, there is no conclusive formula for managing this relationship to achieve success. The purpose of this study will be to identify crossovers between the two streams (strategy and systems) of research by using a systematic literature review. For example, is the academic literature focusing mostly on the learning aspect (late stage in the life cycle) of the exploration strategy while largely ignoring the discussion about attracting the appropriate knowledge (early stage in the life cycle) for this kind of strategy? Or does the academic literature focus on populating a KBS with appropriate complementary knowledge while largely ignoring the dynamics of the transfer of destroying knowledge (learning aspect)? The authors hope to accomplish three goals in this study: (1) to continue the validation of the two (C3EEP and KBS life cycle) frameworks; (2) to identify new research opportunities; and (3) to focus managerial attention on areas of importance in KB strategies and systems that lack depth of academic discussion.

Model Based on Ontological Engineering as Support for Stakeholder Management

2020 ◽  
pp. 305-338
Author(s):  
Beitmantt Geovanni Cárdenas Quintero ◽  
Flor Nancy Díaz-Piraquive ◽  
Hilma Ximena Fonseca Ruiz
Keyword(s):  
Knowledge Management ◽  
Life Cycle ◽  
Project Management ◽  
Information Structure ◽  
Stakeholder Management ◽  
Management Model ◽  
Ontological Engineering ◽  
Model Based ◽  
Complex Knowledge

The objective of this article is a way of working that is used as an instrument for managing the parts with semantic and conceptual elements, such as mastery of management projects, and the effects of ontological engineering techniques, which solves frequently asked questions with respect to information related to this resource. This is done in an agile, precise, and effective way, facilitating its management in the phases that make up the life cycle of the project. The research will contribute to the integration of ontological engineering and project management, enabling the construction of a stakeholder information structure with semantic components that enable it to be the input of a more complex knowledge management model.

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