scholarly journals 3cixty: Building Comprehensive Knowledge Bases for City Exploration

2017 ◽  
Author(s):  
Raphael Troncy ◽  
Giuseppe Rizzo ◽  
Anthony Jameson ◽  
Oscar Corcho ◽  
Julien Plu ◽  
...  
Keyword(s):  
Knowledge Bases ◽  
Comprehensive Knowledge

scholarly journals 3cixty: Building comprehensive knowledge bases for city exploration

2017 ◽  
Vol 46-47 ◽  
pp. 2-13 ◽  
Author(s):  
Raphaël Troncy ◽  
Giuseppe Rizzo ◽  
Anthony Jameson ◽  
Oscar Corcho ◽  
Julien Plu ◽  
...  
Keyword(s):  
Knowledge Bases ◽  
Comprehensive Knowledge

scholarly journals ComMet: A method for comparing metabolic states in genome-scale metabolic models

2020 ◽  
Author(s):  
Chaitra Sarathy ◽  
Marian Breuer ◽  
Martina Kutmon ◽  
Michiel E. Adriaens ◽  
Chris T. Evelo ◽  
...  
Keyword(s):  
Large Scale ◽  
Metabolic Flux ◽  
Knowledge Bases ◽  
Human Models ◽  
Comprehensive Knowledge ◽  
Metabolic States ◽  
Network Modules ◽  
Metabolic Models ◽  
Branched Chain ◽  
Genome Scale

Being a comprehensive knowledge bases of cellular metabolism, Genome-scale metabolic models (GEMs) serve as mathematical tools for studying cellular flux states in various or-ganisms. However, analysis of large-scale GEMs, such as human models, still presents considerable challenges with respect to objective selection and reaction flux constraints. In this study, we introduce a model-based method, ComMet (Comparison of Metabolic states), for comprehensive analysis of large metabolic flux spaces and comparison of various metabolic states. ComMet allows (a) an in-depth characterisation of flux states achievable by GEMs, (b) comparison of flux spaces from several conditions of interest, (c) identification of metabolically distinct network modules and (d) visualisation of network modules as reaction and metabolic map. As a proof-of-principle, we employed ComMet to extract the biochemical differences in the human adipocyte network (iAdipocytes1809) arising due to unlimited/blocked uptake of branched-chain amino acids. Our study opens avenues for exploring several metabolic condi-tions of interest in both microbe and human models. ComMet is open-source and is available at https://github.com/macsbio/commet.

scholarly journals Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases

2021 ◽  
Vol 10 (2-4) ◽  
pp. 108-490
Author(s):  
Gerhard Weikum ◽  
Xin Luna Dong ◽  
Simon Razniewski ◽  
Fabian Suchanek
Keyword(s):  
Knowledge Creation ◽  
Knowledge Bases ◽  
Comprehensive Knowledge

scholarly journals LEVELNET: diving into the multiple layers of protein-protein physical interaction networks

2021 ◽  
Author(s):  
Yasser MOHSENI BEHBAHANI ◽  
Paul SAIGHI ◽  
Flavia CORSI ◽  
Elodie LAINE ◽  
Alessandra CARBONE
Keyword(s):  
Protein Interactions ◽  
High Reliability ◽  
Structural Data ◽  
Knowledge Bases ◽  
Physical Interaction ◽  
Protein Protein Interaction ◽  
Related Data ◽  
Ppi Networks ◽  
Comprehensive Knowledge ◽  
Interactive Visualisation

The complexity underlying protein-protein interaction (PPI) networks calls for the development of comprehensive knowledge bases organizing PPI-related data. The constant growth and high reliability of structural data make them a suitable source of evidence for the determination of PPI. We present LEVELNET, a fully-automated and scalable environment designed to integrate, explore, and infer protein interactions and non-interactions based on physical contacts and other PPI sources, including user-defined annotations. LEVELNET helps to break down the complexity of PPI networks by representing them as multi-layered graphs and allowing the selection of subnetworks and their direct comparison. LEVELNET proposes an interactive visualisation based on a user-friendly web interface. LEVELNET applications are multiple. It allows to explore PPIs of biological processes, identify co-localised partners, assess PPI predictions from computational or experimental sources, unravel cross-interactions, show and compare multiple PPI sources, and help creating PPI benchmarks with specific properties. Availability: LEVELNET is freely available to the community at http://www.lcqb.upmc.fr/levelnet/

scholarly journals Semantic Relatedness Measures for Identifying Relationships in Product Development Processes

2009 ◽  
Author(s):  
Paul Witherell ◽  
Sundar Krishnamurty ◽  
Ian Grosse ◽  
Jack Wileden
Keyword(s):  
Product Development ◽  
Knowledge Integration ◽  
Semantic Relatedness ◽  
Knowledge Bases ◽  
Product Development Process ◽  
Comprehensive Knowledge ◽  
Formal Framework ◽  
Development Processes ◽  
Insight Into ◽  
Product Development Processes

The Semantic Web, especially in relation to ontologies, provides a structured, formal framework for knowledge interoperability. This trait has been exploited by both the biomedical community in development of the Human Gene Ontology [1] and also by geographers in development of geospatial ontologies [2]. Using semantic relatedness techniques, researchers from both communities have been able to develop and integrate comprehensive knowledge bases. Beyond knowledge integration, semantic relatedness techniques have also been able to provide each community with a unique insight into relationships between concepts in their respective domains. In the engineering community, semantic relatedness techniques promise to provide similar insight into product development processes. This paper explores the application of semantic relatedness techniques to ontologies as a means towards improved knowledge management in product development processes. Several different semantic relatedness techniques are reviewed, including a recently developed meronomic technique specific to domain ontologies. Three of these techniques are adopted to create a semantic relatedness measure specifically designed to identify and rank underlying relationships that exist between aspects of the product development process. Four separate case studies are then presented to evaluate the relative accuracy of the developed algorithm and then determine its effectiveness in exposing underlying relationships.

A Model for Structured Data Entry Based on Explicit Descriptional Knowledge

1994 ◽  
Vol 33 (05) ◽  
pp. 454-463 ◽  
Author(s):  
A. M. van Ginneken ◽  
J. van der Lei ◽  
J. H. van Bemmel ◽  
P. W. Moorman
Keyword(s):  
Domain Knowledge ◽  
Data Entry ◽  
Knowledge Bases ◽  
Structured Data ◽  
Research Quality ◽  
Free Text ◽  
Specific Knowledge ◽  
User Input ◽  
The One ◽  
Research Quality Assessment

Abstract:Clinical narratives in patient records are usually recorded in free text, limiting the use of this information for research, quality assessment, and decision support. This study focuses on the capture of clinical narratives in a structured format by supporting physicians with structured data entry (SDE). We analyzed and made explicit which requirements SDE should meet to be acceptable for the physician on the one hand, and generate unambiguous patient data on the other. Starting from these requirements, we found that in order to support SDE, the knowledge on which it is based needs to be made explicit: we refer to this knowledge as descriptional knowledge. We articulate the nature of this knowledge, and propose a model in which it can be formally represented. The model allows the construction of specific knowledge bases, each representing the knowledge needed to support SDE within a circumscribed domain. Data entry is made possible through a general entry program, of which the behavior is determined by a combination of user input and the content of the applicable domain knowledge base. We clarify how descriptional knowledge is represented, modeled, and used for data entry to achieve SDE, which meets the proposed requirements.

Explanatory Power for Medical Expert Systems: Studies in the Representation of Causal Relationships for Clinical Consultations

1982 ◽  
Vol 21 (03) ◽  
pp. 127-136 ◽  
Author(s):  
J. W. Wallis ◽  
E. H. Shortliffe
Keyword(s):  
Explanatory Power ◽  
Prototype System ◽  
Inference Rules ◽  
Medical Expert ◽  
Comprehensive Knowledge ◽  
Different Types ◽  
Medical Expert Systems ◽  
Explanation System ◽  
Systems Studies

This paper reports on experiments designed to identify and implement mechanisms for enhancing the explanation capabilities of reasoning programs for medical consultation. The goals of an explanation system are discussed, as is the additional knowledge needed to meet these goals in a medical domain. We have focussed on the generation of explanations that are appropriate for different types of system users. This task requires a knowledge of what is complex and what is important; it is further strengthened by a classification of the associations or causal mechanisms inherent in the inference rules. A causal representation can also be used to aid in refining a comprehensive knowledge base so that the reasoning and explanations are more adequate. We describe a prototype system which reasons from causal inference rules and generates explanations that are appropriate for the user.

The Distinction between Linguistic and Conceptual Semantics in Medical Terminology and its Implication for NLP-Based Knowledge Acquisition

1998 ◽  
Vol 37 (04/05) ◽  
pp. 327-333 ◽  
Author(s):  
F. Buekens ◽  
G. De Moor ◽  
A. Waagmeester ◽  
W. Ceusters
Keyword(s):  
Natural Language ◽  
Knowledge Acquisition ◽  
Natural Language Understanding ◽  
Knowledge Bases ◽  
Linguistic Knowledge ◽  
Medical Terminology ◽  
Language Understanding ◽  
Conceptual Semantics

AbstractNatural language understanding systems have to exploit various kinds of knowledge in order to represent the meaning behind texts. Getting this knowledge in place is often such a huge enterprise that it is tempting to look for systems that can discover such knowledge automatically. We describe how the distinction between conceptual and linguistic semantics may assist in reaching this objective, provided that distinguishing between them is not done too rigorously. We present several examples to support this view and argue that in a multilingual environment, linguistic ontologies should be designed as interfaces between domain conceptualizations and linguistic knowledge bases.

Methods of parallel computing for multilevel fuzzy Takagi – Sugeno systems

2016 ◽  
pp. 141-149
Author(s):  
S.V. Yershov ◽  
◽  
R.М. Ponomarenko ◽  
Keyword(s):  
Dynamic Models ◽  
Fuzzy Inference ◽  
Knowledge Bases ◽  
Comparative Characteristic ◽  
Software Systems ◽  
Scientific Papers ◽  
Speed Up ◽  
Diagnostic Software ◽  
Takagi Sugeno

Parallel tiered and dynamic models of the fuzzy inference in expert-diagnostic software systems are considered, which knowledge bases are based on fuzzy rules. Tiered parallel and dynamic fuzzy inference procedures are developed that allow speed up of computations in the software system for evaluating the quality of scientific papers. Evaluations of the effectiveness of parallel tiered and dynamic schemes of computations are constructed with complex dependency graph between blocks of fuzzy Takagi – Sugeno rules. Comparative characteristic of the efficacy of parallel-stacked and dynamic models is carried out.

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