scholarly journals Propagators and Solvers for the Algebra of Modular Systems

10.29007/t7r9 ◽  
2018 ◽  
Author(s):  
Bart Bogaerts ◽  
Eugenia Ternovska ◽  
David Mitchell
Keyword(s):  
Knowledge Representation ◽  
Learning Algorithm ◽  
Knowledge Bases ◽  
Black Box ◽  
Modular System ◽  
General Notion ◽  
Combined System ◽  
Modular Systems ◽  
Problem Solvers ◽  
Model Expansion

Solving complex problems can involve non-trivial combinations of distinct knowledge bases and problem solvers. The Algebra of Modular Systems is a knowledge representation framework that provides a method for formally specifying such systems in purely semantic terms. Many practical systems based on expressive formalisms solve the model expansion task. In this paper, we con- struct a solver for the model expansion task for a complex modular system from an expression in the algebra and black-box propagators or solvers for the primitive modules. To this end, we define a general notion of propagators equipped with an explanation mechanism, an extension of the algebra to propagators, and a lazy conflict-driven learning algorithm. The result is a framework for seamlessly combining solving technology from different domains to produce a solver for a combined system.

scholarly journals A Modular System Oriented to the Design of Versatile Knowledge Bases for Chatbots

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Giovanni Pilato ◽  
Agnese Augello ◽  
Salvatore Gaglio
Keyword(s):  
Corpus Callosum ◽  
Knowledge Representation ◽  
User Behavior ◽  
Knowledge Bases ◽  
Modular System ◽  
Conversational Agent ◽  
Proof Of Concept ◽  
Conversational Agents ◽  
Specific Domain ◽  
Representation Techniques

The paper illustrates a system that implements a framework, which is oriented to the development of a modular knowledge base for a conversational agent. This solution improves the flexibility of intelligent conversational agents in managing conversations. The modularity of the system grants a concurrent and synergic use of different knowledge representation techniques. According to this choice, it is possible to use the most adequate methodology for managing a conversation for a specific domain, taking into account particular features of the dialogue or the user behavior. We illustrate the implementation of a proof-of-concept prototype: a set of modules exploiting different knowledge representation methodologies and capable of managing different conversation features has been developed. Each module is automatically triggered through a component, named corpus callosum, that selects in real time the most adequate chatbot knowledge module to activate.

scholarly journals An Optimized Network Representation Learning Algorithm Using Multi-Relational Data

Mathematics ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 460
Author(s):  
Zhonglin Ye ◽  
Haixing Zhao ◽  
Ke Zhang ◽  
Yu Zhu ◽  
Zhaoyang Wang
Keyword(s):  
Knowledge Representation ◽  
Learning Algorithm ◽  
Representation Learning ◽  
Knowledge Bases ◽  
Relational Data ◽  
New Approach ◽  
Network Representation ◽  
Structural Relationships ◽  
Low Dimensional

Representation learning aims to encode the relationships of research objects into low-dimensional, compressible, and distributed representation vectors. The purpose of network representation learning is to learn the structural relationships between network vertices. Knowledge representation learning is oriented to model the entities and relationships in knowledge bases. In this paper, we first introduce the idea of knowledge representation learning into network representation learning, namely, we propose a new approach to model the vertex triplet relationships based on DeepWalk without TransE. Consequently, we propose an optimized network representation learning algorithm using multi-relational data, MRNR, which introduces the multi-relational data between vertices into the procedures of network representation learning. Importantly, we adopted a kind of higher order transformation strategy to optimize the learnt network representation vectors. The purpose of MRNR is that multi-relational data (triplets) can effectively guide and constrain the procedures of network representation learning. The experimental results demonstrate that the proposed MRNR can learn the discriminative network representations, which show better performance on network classification, visualization, and case study tasks compared to the proposed baseline algorithms in this paper.

scholarly journals SUPPORT FOR MANAGING PARTLY CONFIGURABLE MODULAR SYSTEMS

2021 ◽  
Vol 1 ◽  
pp. 2791-2800
Author(s):  
Jarkko Pakkanen ◽  
Teuvo Heikkinen ◽  
Nillo Adlin ◽  
Timo Lehtonen ◽  
Janne Mämmelä ◽  
...  
Keyword(s):  
Portfolio Management ◽  
Product Variety ◽  
Added Value ◽  
Modular System ◽  
Product Management ◽  
Design Decision ◽  
Product Portfolio ◽  
Modular Systems ◽  
Engineer To Order ◽  
The Impact

AbstractThe paper studies what kind of support could be applied to the management of partly configurable modular systems. The main tasks of product management, product portfolio management and product variety management are defined. In addition, a partly configurable product structure and modular system are defined. Because the limited support in the literature for managing partly configurable modular systems, the article reviews previous product development cases in which authors have been involved on lessons learnt basis, i.e., if the methods and tools used in the cases could provide support for the research objective. As a result, the existing definition of the modular system should be extended by the concepts of non-module and design decision sequence description when dealing with partly configurable modular systems. This is because engineer-to-order should be made possible in cases where it brings clear added value to the customer compared to completely pre-defined solutions that may limit the customer's interest in the offering. Tools to assess the impact of changes to the product offering are required. These should be taken into account in frameworks that are used in method and tool development.

Analysis of the Waste Volume and CO2-Equivalent Caused by the Use of Selected Patch Pumps

2021 ◽  
pp. 193229682199152
Author(s):  
Jana Winkelkötter ◽  
Thore Reitz
Keyword(s):  
Modular Design ◽  
Insulin Pump ◽  
Raw Materials ◽  
Modular System ◽  
Mass Determination ◽  
Modular Systems ◽  
Use Of Resources ◽  
Manual Sorting ◽  
Free Insulin ◽  
Incineration Residues

Background: The use of tube-free insulin pumps is increasing. To protect the environment, the use of resources and the amount of emissions into the environment should be kept as low as possible when designing these systems. In addition to basic waste avoidance, the composition of the waste produced must be considered. Methods: To compare current tube-free pumps from an ecological standpoint, a tube-free insulin pump with a modular design and two non-modular tube-free pumps were subjected to manual separation, manual sorting, characterization, and mass determination. The annual waste volume of a user was measured, and the recyclability was assessed. The global warming potential (GWP) resulting from extraction of raw materials, energetic utilization of waste, and landfill of the incineration residues was balanced. Results: For the modular tube-free pump, a total waste volume of 5.5 kg/a (recycling percentage 44.3%) was determined. The non-modular systems generated 4.9 kg/a (recycling percentage 14.6%) and 5.1 kg/a (recycling percentage 16.0%) waste. The product-specific GWP of the modular system was approximately 50% lower than that of the non-modular systems; the packaging-specific GWP was 2.5 times higher. In total, a GWP of 13.6 kg CO2-equivalent per year could be determined for the modular system and a GWP of 15.5 kg CO2-equivalent per year for the non-modular systems. Conclusions: Although the modular micropump has a higher total waste volume, a greater ecological potential can be attributed to it. This is based on the recyclability of the system due to its modularity and the possible reduction of packaging waste.

scholarly journals Effectiveness of lumbar orthoses in low back pain: Review of the literature and our results

2018 ◽  
Vol 10 (4) ◽  
Author(s):  
Cordelia Schott ◽  
Sonja Zirke ◽  
Jillian Marie Schmelzle ◽  
Christel Kaiser ◽  
Lluis Aguilar I Fernández
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Randomized Study ◽  
Cost Of Care ◽  
Modular System ◽  
Low Back ◽  
Modular Systems ◽  
Frequency Of Use ◽  
Modular Groups ◽  
The Cost

Back pain and diseases of the spine are today a health disorder of outstanding epidemiological, medical, and health economic importance. The cost of care for patients with lumbosciatic complaints are steadily increasing. Accordingly, the guidelines and treatments are constantly renewed. One concept is the orthotic care. In the following we want to give an overview of the literature and the effectiveness of lumbar orthoses in low back pain supplemented by our own data. A prospective randomized study with 230 patients, divided into three groups, each with two subgroups. Three Orthoses by the TIGGES-Zours GmbH were prescribed; a demountable two-step lumbar orthosis, three-step bridging orthosis and a four-step flexion orthosis modular system. Each were compared to the nonmodular equivalent. All six groups showed improvement in pain intensity and functional capacity at 6 and 12 weeks. The modular groups were found to have improvement in the frequency of use. The subjective effectiveness and sensitivity for the modular and non-modular groups was assessed as being good. In the literature, there are no clear guidelines for an orthotic supply. The studies do not seem to be meaningful and universal due to the difficult ascertainability of pain. There is a need for further research here. Nevertheless, the authors of this review are of the opinion that the implementation of trunk orthoses is void of side effects and beneficial to patients. The modular systems seem to have an advantage as well as higher patient satisfaction.

A Formal Investigation of Semantic Interoperability of HCLS Systems

2015 ◽  
pp. 177-208
Author(s):  
Ratnesh Sahay ◽  
Antoine Zimmermann ◽  
Ronan Fox ◽  
Axel Polleres ◽  
Manfred Hauswirth
Keyword(s):  
Life Sciences ◽  
Knowledge Bases ◽  
Semantic Interoperability ◽  
Research Community ◽  
Local Information ◽  
General Notion ◽  
Global Knowledge ◽  
Research Issues ◽  
Key Features ◽  
Overall Effectiveness

Semantic interoperability facilitates Health Care and Life Sciences (HCLS) systems in connecting stakeholders at various levels as well as ensuring seamless use of healthcare resources. Their scope ranges from local to regional, national and cross-border. The use of semantics in delivering interoperable solution for HCLS systems is weakened by fact that an Ontology Based Information System (OBIS) has restrictions in modeling, aggregating, and interpreting global knowledge in conjunction with local information (e.g., policy, profiles). This chapter presents an example-scenario that shows such limitations and recognizes that enabling two key features, namely the type and scope of knowledge, within a knowledge base could enhance the overall effectiveness of an OBIS. This chapter provides the idea of separating knowledge bases in types with scope (e.g., global or local) of applicability. Then, it proposes two concrete solutions on this general notion. Finally, the chapter describes open research issues that may be of interest to knowledge system developers and broader research community.

Web knowledge

2006 ◽  
Author(s):  
Christopher Walton
Keyword(s):  
Semantic Web ◽  
Knowledge Representation ◽  
Knowledge Base ◽  
Web Applications ◽  
Semantic Network ◽  
Knowledge Bases ◽  
Web Standards ◽  
Description Framework ◽  
The Web

In the introductory chapter of this book, we discussed the means by which knowledge can be made available on the Web. That is, the representation of the knowledge in a form by which it can be automatically processed by a computer. To recap, we identified two essential steps that were deemed necessary to achieve this task: 1. We discussed the need to agree on a suitable structure for the knowledge that we wish to represent. This is achieved through the construction of a semantic network, which defines the main concepts of the knowledge, and the relationships between these concepts. We presented an example network that contained the main concepts to differentiate between kinds of cameras. Our network is a conceptualization, or an abstract view of a small part of the world. A conceptualization is defined formally in an ontology, which is in essence a vocabulary for knowledge representation. 2. We discussed the construction of a knowledge base, which is a store of knowledge about a domain in machine-processable form; essentially a database of knowledge. A knowledge base is constructed through the classification of a body of information according to an ontology. The result will be a store of facts and rules that describe the domain. Our example described the classification of different camera features to form a knowledge base. The knowledge base is expressed formally in the language of the ontology over which it is defined. In this chapter we elaborate on these two steps to show how we can define ontologies and knowledge bases specifically for the Web. This will enable us to construct Semantic Web applications that make use of this knowledge. The chapter is devoted to a detailed explanation of the syntax and pragmatics of the RDF, RDFS, and OWL Semantic Web standards. The resource description framework (RDF) is an established standard for knowledge representation on the Web. Taken together with the associated RDF Schema (RDFS) standard, we have a language for representing simple ontologies and knowledge bases on the Web.

scholarly journals Efficient Results in Semantic Interoperability for Health Care

2016 ◽  
Vol 25 (01) ◽  
pp. 184-187
Author(s):  
J. Charlet ◽  
L. F. Soualmia ◽  
Keyword(s):  
Health Care ◽  
Semantic Web ◽  
Knowledge Representation ◽  
Large Scale ◽  
Recommendation System ◽  
Clinical Care ◽  
Knowledge Bases ◽  
Semantic Interoperability ◽  
The Third ◽  
Research Activities

Summary Objectives: To summarize excellent current research in the field of Knowledge Representation and Management (KRM) within the health and medical care domain. Method: We provide a synopsis of the 2016 IMIA selected articles as well as a related synthetic overview of the current and future field activities. A first step of the selection was performed through MEDLINE querying with a list of MeSH descriptors completed by a list of terms adapted to the KRM section. The second step of the selection was completed by the two section editors who separately evaluated the set of 1,432 articles. The third step of the selection consisted of a collective work that merged the evaluation results to retain 15 articles for peer-review. Results: The selection and evaluation process of this Yearbook’s section on Knowledge Representation and Management has yielded four excellent and interesting articles regarding semantic interoperability for health care by gathering heterogeneous sources (knowledge and data) and auditing ontologies. In the first article, the authors present a solution based on standards and Semantic Web technologies to access distributed and heterogeneous datasets in the domain of breast cancer clinical trials. The second article describes a knowledge-based recommendation system that relies on ontologies and Semantic Web rules in the context of chronic diseases dietary. The third article is related to concept-recognition and text-mining to derive common human diseases model and a phenotypic network of common diseases. In the fourth article, the authors highlight the need for auditing the SNOMED CT. They propose to use a crowd-based method for ontology engineering. Conclusions: The current research activities further illustrate the continuous convergence of Knowledge Representation and Medical Informatics, with a focus this year on dedicated tools and methods to advance clinical care by proposing solutions to cope with the problem of semantic interoperability. Indeed, there is a need for powerful tools able to manage and interpret complex, large-scale and distributed datasets and knowledge bases, but also a need for user-friendly tools developed for the clinicians in their daily practice.

COMBINING DEFAULT LOGIC DATABASES

1994 ◽  
Vol 03 (03) ◽  
pp. 319-348 ◽  
Author(s):  
CHITTA BARAL ◽  
SARIT KRAUS ◽  
JACK MINKER ◽  
V. S. SUBRAHMANIAN
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Large Scale ◽  
Future Generation ◽  
Knowledge Bases ◽  
Default Logic ◽  
Integrity Constraints ◽  
Specialized Knowledge ◽  
The Past ◽  
The Future

During the past decade, it has become increasingly clear that the future generation of large-scale knowledge bases will consist, not of one single isolated knowledge base, but a multiplicity of specialized knowledge bases that contain knowledge about different domains of expertise. These knowledge bases will work cooperatively, pooling together their varied bodies of knowledge, so as to be able to solve complex problems that no single knowledge base, by itself, would have been able to address successfully. In any such situation, inconsistencies are bound to arise. In this paper, we address the question: "Suppose we have a set of knowledge bases, KB1, …, KBn, each of which uses default logic as the formalism for knowledge representation, and a set of integrity constraints IC. What knowledge base constitutes an acceptable combination of KB1, …, KBn?"

Modeling The Tower Of Hanoi Using Neural Network

1993 ◽  
pp. 47-56
Author(s):  
Mohamed Othman ◽  
Mohd. Hassan Selamat ◽  
Zaiton Muda ◽  
Lili Norliya Abdullah
Keyword(s):  
Neural Network ◽  
Knowledge Representation ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Learning Rule ◽  
Neural System ◽  
Neural Systems ◽  
Tower Of Hanoi ◽  
Artificial Neural ◽  
The Given

This paper discusses the modeling of Tower of Hanoi using the concepts of neural network. The basis idea of backpropagation learning algorithm in Artificial Neural Systems is then described. While similar in some ways, Artificial Neural System learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connection in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable qf reproducing the desired function within the given network. Key words: Tower of Hanoi; Backpropagation Algorithm; Knowledge Representation;

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