scholarly journals Identifying Principles for the Construction of an Ontology-Based Knowledge Base: A Case Study Approach (Preprint)

2018 ◽  
Author(s):  
Xia Jing ◽  
Nicholas R Hardiker ◽  
Stephen Kay ◽  
Yongsheng Gao
Keyword(s):  
Cystic Fibrosis ◽  
Knowledge Base ◽  
Health Information ◽  
Domain Knowledge ◽  
Information Needs ◽  
Molecular Mechanisms ◽  
Critical Role ◽  
Clinical Information ◽  
Knowledge Bases ◽  
Ontology Language

BACKGROUND Ontologies are key enabling technologies for the Semantic Web. The Web Ontology Language (OWL) is a semantic markup language for publishing and sharing ontologies. OBJECTIVE The supply of customizable, computable, and formally represented molecular genetics information and health information, via electronic health record (EHR) interfaces, can play a critical role in achieving precision medicine. In this study, we used cystic fibrosis as an example to build an Ontology-based Knowledge Base prototype on Cystic Fibrobis (OntoKBCF) to supply such information via an EHR prototype. In addition, we elaborate on the construction and representation principles, approaches, applications, and representation challenges that we faced in the construction of OntoKBCF. The principles and approaches can be referenced and applied in constructing other ontology-based domain knowledge bases. METHODS First, we defined the scope of OntoKBCF according to possible clinical information needs about cystic fibrosis on both a molecular level and a clinical phenotype level. We then selected the knowledge sources to be represented in OntoKBCF. We utilized top-to-bottom content analysis and bottom-up construction to build OntoKBCF. Protégé-OWL was used to construct OntoKBCF. The construction principles included (1) to use existing basic terms as much as possible; (2) to use intersection and combination in representations; (3) to represent as many different types of facts as possible; and (4) to provide 2-5 examples for each type. HermiT 1.3.8.413 within Protégé-5.1.0 was used to check the consistency of OntoKBCF. RESULTS OntoKBCF was constructed successfully, with the inclusion of 408 classes, 35 properties, and 113 equivalent classes. OntoKBCF includes both atomic concepts (such as amino acid) and complex concepts (such as “adolescent female cystic fibrosis patient”) and their descriptions. We demonstrated that OntoKBCF could make customizable molecular and health information available automatically and usable via an EHR prototype. The main challenges include the provision of a more comprehensive account of different patient groups as well as the representation of uncertain knowledge, ambiguous concepts, and negative statements and more complicated and detailed molecular mechanisms or pathway information about cystic fibrosis. CONCLUSIONS Although cystic fibrosis is just one example, based on the current structure of OntoKBCF, it should be relatively straightforward to extend the prototype to cover different topics. Moreover, the principles underpinning its development could be reused for building alternative human monogenetic diseases knowledge bases.

scholarly journals Building an OWL ontology with Xper3

2018 ◽  
Vol 2 ◽  
pp. e25614 ◽  
Author(s):  
Florian Pellen ◽  
Sylvain Bouquin ◽  
Isabelle Mougenot ◽  
Régine Vignes-Lebbe
Keyword(s):  
Semantic Web ◽  
Knowledge Base ◽  
Expressive Power ◽  
Knowledge Bases ◽  
User Friendliness ◽  
Standard Format ◽  
Ontology Language ◽  
Closed World ◽  
Web Standards ◽  
Description Framework

Xper3 (Vignes Lebbe et al. 2016) is a collaborative knowledge base publishing platform that, since its launch in november 2013, has been adopted by over 2 thousand users (Pinel et al. 2017). This is mainly due to its user friendly interface and the simplicity of its data model. The data are stored in MySQL Relational DBs, but the exchange format uses the TDWG standard format SDD (Structured Descriptive DataHagedorn et al. 2005). However, each Xper3 knowledge base is a closed world that the author(s) may or may not share with the scientific community or the public via publishing content and/or identification key (Kopfstein 2016). The explicit taxonomic, geographic and phenotypic limits of a knowledge base are not always well defined in the metadata fields. Conversely terminology vocabularies, such as Phenotype and Trait Ontology PATO and the Plant Ontology PO, and software to edit them, such as Protégé and Phenoscape, are essential in the semantic web, but difficult to handle for biologist without computer skills. These ontologies constitute open worlds, and are expressed themselves by RDF triples (Resource Description Framework). Protégé offers vizualisation and reasoning capabilities for these ontologies (Gennari et al. 2003, Musen 2015). Our challenge is to combine the user friendliness of Xper3 with the expressive power of OWL (Web Ontology Language), the W3C standard for building ontologies. We therefore focused on analyzing the representation of the same taxonomic contents under Xper3 and under different models in OWL. After this critical analysis, we chose a description model that allows automatic export of SDD to OWL and can be easily enriched. We will present the results obtained and their validation on two knowledge bases, one on parasitic crustaceans (Sacculina) and the second on current ferns and fossils (Corvez and Grand 2014). The evolution of the Xper3 platform and the perspectives offered by this link with semantic web standards will be discussed.

scholarly journals RTPO: A Domain Knowledge Base for Robot Task Planning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1105 ◽  
Author(s):  
Sun ◽  
Zhang ◽  
Chen
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Task Planning ◽  
Specific Domain ◽  
Goal State ◽  
Ontology Language ◽  
Planning Algorithm ◽  
Domain Knowledge Base ◽  
Robot Task ◽  
High Level

Knowledge can enhance the intelligence of robots’ high-level decision-making. However, there is no specific domain knowledge base for robot task planning in this field. Aiming to represent the knowledge in robot task planning, the Robot Task Planning Ontology (RTPO) is first designed and implemented in this work, so that robots can understand and know how to carry out task planning to reach the goal state. In this paper, the RTPO is divided into three parts: task ontology, environment ontology, and robot ontology, followed by a detailed description of these three types of knowledge, respectively. The OWL (Web Ontology Language) is adopted to represent the knowledge in robot task planning. Then, the paper proposes a method to evaluate the scalability and responsiveness of RTPO. Finally, the corresponding task planning algorithm is designed based on RTPO, and then the paper conducts experiments on the basis of the real robot TurtleBot3 to verify the usability of RTPO. The experimental results demonstrate that RTPO has good performance in scalability and responsiveness, and the robot can achieve given high-level tasks based on RTPO.

scholarly journals 4-phenylbutyrate restored GABA uptake and reduced seizures in SLC6A1 variants-mediated disorders

2021 ◽  
Author(s):  
Gerald I Nwosu ◽  
Felicia Mermer ◽  
Carson Flamm ◽  
Sarah Poliquin ◽  
Wangzhen Shen ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Membrane Trafficking ◽  
Protein Trafficking ◽  
Molecular Mechanisms ◽  
Critical Role ◽  
Gaba Uptake ◽  
Functional Protein ◽  
Great Opportunity ◽  
Treatment Development ◽  
The Impact

We have previously studied the molecular mechanisms of solute carrier family 6 member 1 (SLC6A1) associated with a continuum of neurodevelopmental disorders, including various epilepsy syndromes, autism, and intellectual disability. Based on functional assays of variants in a large cohort with heterogenous clinical phenotypes, we conclude that partial or complete loss of GABA uptake function in the mutant GAT-1 is the primary etiology as identified in GABAA receptor mutation-mediated epilepsy and in cystic fibrosis. Importantly, we identified that there are common patterns of the mutant protein trafficking from biogenesis, oligomerization, glycosylation, and translocation to the cell membrane across variants with the conservation of this process across cell types. Conversely any approach to facilitate membrane trafficking would increase presence of the functional protein in the targeted destination in all involved cells. PBA is an FDA-approved drug for pediatric use and is orally bioavailable so it can be quickly translated to patient use. It has been demonstrated that PBA can correct protein misfolding, reduce ER stress, and attenuate unfolded protein response in neurodegenerative diseases, it has also showed promise in treatment of cystic fibrosis. The common cellular mechanisms shared by the mutant GAT-1 and the mutant cystic fibrosis transmembrane conductance regulator led us to test if PBA and other pharmaco-chaperones could be a potential treatment option for SLC6A1 mutations. We examined the impact of PBA and other small molecules in a library of variants and in cell and knockin mouse models. Because of the critical role of astrocytic GAT-1 deficit in seizures, we focused on astrocytes, and demonstrated that the existence of the mutant GAT-1 retained the wildtype GAT-1, suggesting aberrant protein oligomerization and trafficking caused by the mutant GAT-1. PBA increased GABA uptake in both mouse and human astrocytes bearing the mutations. Importantly, PBA increased GAT-1 expression and suppressed spike wave discharges (SWDS) in the heterozygous knockin mice. Although the detailed mechanisms of action for PBA are ambiguous, it is likely that PBA can facilitate the forward trafficking of the wildtype GAT-1 favoring over the mutant GAT-1, thus increasing GABA uptake. Since all patients with SLC6A1 mutations are heterozygous and carry one wildtype functional allele, this suggests a great opportunity for treatment development by leveraging the endogenous protein trafficking pathway to promote forward trafficking of the wildtype in combination with enhancing the disposal of the mutant allele as treatment mode. The study opens a novel avenue of treatment development for genetic epilepsy via drug repurposing.

A Design for Additive Manufacturing Ontology

2017 ◽  
Vol 17 (2) ◽  
Author(s):  
Mahmoud Dinar ◽  
David W. Rosen
Keyword(s):  
Additive Manufacturing ◽  
Knowledge Base ◽  
System Integration ◽  
Domain Knowledge ◽  
Description Logic ◽  
Design For Additive Manufacturing ◽  
Ontology Language ◽  
Subtractive Manufacturing ◽  
Description Framework ◽  
Future Work

Design for additive manufacturing (DFAM) gives designers new freedoms to create complex geometries and combine parts into one. However, it has its own limitations, and more importantly, requires a shift in thinking from traditional design for subtractive manufacturing. There is a lack of formal and structured guidelines, especially for novice designers. To formalize knowledge of DFAM, we have developed an ontology using formal web ontology language (OWL)/resource description framework (RDF) representations in the Protégé tool. The description logic formalism facilitates expressing domain knowledge as well as capturing information from benchmark studies. This is demonstrated in a case study with three design features: revolute joint, threaded assembly (screw connection), and slider–crank. How multiple instances (build events) are stored and retrieved in the knowledge base is discussed in light of modeling requirements for the DFAM knowledge base: knowledge capture and reuse, supporting a tutoring system, integration into cad tools. A set of competency questions are described to evaluate knowledge retrieval. Examples are given with SPARQL queries. Reasoning with semantic web rule language (SWRL) is exemplified for manufacturability analysis. Knowledge documentation is the main objective of the current ontology. However, description logic creates multiple opportunities for future work, including representing and reasoning about DFAM rules in a structured modular hierarchy, discovering new rules with induction, and recognizing patterns with classification, e.g., what leads to “successful” versus “unsuccessful” fabrications.

scholarly journals Ontology for preserving the knowledge base of traditional dances (OTD)

2020 ◽  
Vol 38 (4) ◽  
pp. 785-803
Author(s):  
Deepjyoti Kalita ◽  
Dipen Deka
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Description Logic ◽  
Cultural Knowledge ◽  
Strong Base ◽  
Content Type ◽  
North East India ◽  
North East ◽  
Ontology Language ◽  
Traditional Dance

Purpose Systematic organization of domain knowledge has many advantages in archiving, sharing and retrieval of information. Ontologies provide a cushion for such practices in the semantic Web environment. This study aims to develop an ontology that can preserve the knowledge base of traditional dance practices. Design/methodology/approach It is hypothesized that an ontology-based approach for the chosen domain might boost collaborative research prospects in the domain. A systematic methodology was developed for modeling the ontology based on the analytico-synthetic rule of library classification. Protégé 5.2 was used as an editor for the ontology using the Web ontology language combined with description logic axioms. Ontology was later implemented in a local GraphDB repository to run queries over it. Findings The developed ontology on traditional dances (OTD) was tested using the dances of the Rabha tribes of North East India. Rabha tribes are from an indigenous mongoloid community and have a robust presence in Southeast Asian countries, such as Myanmar, Thailand, Bangladesh, Bhutan and Nepal. The result from HermiT reasoner found the presence of no logical inconsistency in the ontology, while the OOPS! pitfall checker tool reported no major internal inconsistency. The induced knowledge base of traditional dances of the Rabha’s in the developed OTD was further validated based on some competency questions. Research limitations/implications In the growing trend of globalization, preservation of the cultural knowledge base of human societies is an important issue. Traditional dances reflect a strong base of the cultural heritage of human societies as they are closely related to the lifestyle, habitat, religious practices and festivals of a specific community. Originality/value The current study is exclusively designed, keeping in mind the variables of traditional dance domain based on a survey of the user- and domain-specific needs. The ontology finds probable uses in traditional knowledge information systems, lifestyle-based e-commerce sites and e-learning platforms.

scholarly journals ByNowLife: A Novel Framework for OWL and Bayesian Network Integration

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 95
Author(s):  
Foni Setiawan ◽  
Eko Budiardjo ◽  
Wahyu Wibowo
Keyword(s):  
Bayesian Network ◽  
Knowledge Base ◽  
Structural Adjustment ◽  
Description Logic ◽  
Probabilistic Reasoning ◽  
Knowledge Bases ◽  
Third Party ◽  
Probabilistic Information ◽  
Ontology Language ◽  
Novel Approach

An ontology-based system can currently logically reason through the Web Ontology Language Description Logic (OWL DL). To perform probabilistic reasoning, the system must use a separate knowledge base, separate processing, or third-party applications. Previous studies mainly focus on how to represent probabilistic information in ontologies and perform reasoning through them. These approaches are not suitable for systems that already have running ontologies and Bayesian network (BN) knowledge bases because users must rewrite the probabilistic information contained in a BN into an ontology. We present a framework called ByNowLife, which is a novel approach for integrating BN with OWL by providing an interface for retrieving probabilistic information through SPARQL queries. ByNowLife catalyzes the integration process by transforming logical information contained in an ontology into a BN and probabilistic information contained in a BN into an ontology. This produces a system with a complete knowledge base. Using ByNowLife, a system that already has separate ontologies and BN knowledge bases can integrate them into a single knowledge base and perform both logical and probabilistic reasoning through it. The integration not only facilitates the unity of reasoning but also has several other advantages, such as ontology enrichment and BN structural adjustment through structural and parameter learning.

ASSISTED KNOWLEDGE BASE GENERATION, MANAGEMENT AND COMPETENCE RETRIEVAL

2012 ◽  
Vol 22 (08) ◽  
pp. 1007-1037 ◽  
Author(s):  
ANDREA BELLANDI ◽  
PIERFRANCESCO BELLINI ◽  
ANTONIO CAPPUCCIO ◽  
PAOLO NESI ◽  
GIANNI PANTALEO ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Open Space ◽  
Knowledge Bases ◽  
Large Institution ◽  
University Of Florence ◽  
The University ◽  
Development And Management ◽  
Multiple Languages ◽  
Generation Management

Despite the presence of many systems for developing and managing structured taxonomies and/or SKOS models for a given domain for which small documents set are accessible, the production and maintenance of these domain knowledge bases is still a very expensive and time consuming process. This paper proposes a solution for assisting expert users in the development and management of knowledge base, including SKOS and ontologies modeling structures and relationships. The proposed solution accelerates the knowledge production by crawling and exploiting different kinds of sources (in multiple languages and with several inconsistencies among them). The proposed tool supports the experts in defining relationships among the most recurrent concepts, reducing the time to SKOS production and allowing assisted production. The validity of the produced knowledge base has been assessed by using SPARQL query interface and a precision and recall model. The results have demonstrated better performance with respect to the state of the art. The solution has been developed for Open Space Innovative Mind project, with the aim of creating a portal to allow industries at posing semantic queries to discover potential competences in a large institution such as the University of Florence, in which several distinct domains are associated with its own departments.

Knowledge Base Refinement Using Limited Amount of Efforts from Experts

2014 ◽  
Vol 4 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Ki Chan ◽  
Wai Lam ◽  
Tak-Lam Wong
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Pattern Mining ◽  
Knowledge Bases ◽  
Target Domain ◽  
Markov Logic ◽  
Source Domain ◽  
Construction Of Knowledge ◽  
Knowledge Base Refinement ◽  
Intelligent Information

Knowledge bases are essential for supporting decision making during intelligent information processing. Automatic construction of knowledge bases becomes infeasible without labeled data, a complete table of data records including answers to queries. Preparing such information requires huge efforts from experts. The authors propose a new knowledge base refinement framework based on pattern mining and active learning using an existing available knowledge base constructed from a different domain (source domain) solving the same task as well as some data collected from the target domain. The knowledge base investigated in this paper is represented by a model known as Markov Logic Networks. The authors' proposed method first analyzes the unlabeled target domain data and actively asks the expert to provide labels (or answers) a very small amount of automatically selected queries. The idea is to identify the target domain queries whose underlying relations are not sufficiently described by the existing source domain knowledge base. Potential relational patterns are discovered and new logic relations are constructed for the target domain by exploiting the limited amount of labeled target domain data and the unlabeled target domain data. The authors have conducted extensive experiments by applying our approach to two different text mining applications, namely, pronoun resolution and segmentation of citation records, demonstrating consistent improvements.

scholarly journals Metabolite, Protein, And Tissue Dysfunction Associated With COVID-19 Disease Severity

2021 ◽  
Author(s):  
Ali Rahnavard ◽  
Brendan Mann ◽  
Abhigya Giri ◽  
Ranojoy Chatterjee ◽  
Keith Crandall
Keyword(s):  
Disease Severity ◽  
Molecular Mechanisms ◽  
Complete Blood Count ◽  
Critical Role ◽  
Clinical Information ◽  
Integrated Approach ◽  
Machine Learning Algorithms ◽  
Direct Products ◽  
Metabolomics Data ◽  
Presenting Symptoms

Abstract Proteins are direct products of the genome and metabolites are functional products of interactions between the host and other factors such as environment, disease state, clinical information, etc. Omics data, including proteins and metabolites, are useful in characterizing biological processes underlying COVID-19 along with patient data and clinical information, yet few methods are available to effectively analyze such diverse and unstructured data. Using an integrated approach that combines proteomics and metabolomics data, we investigated the changes in metabolites and proteins in relation to patient characteristics (e.g., age, gender, and health outcome) and clinical information (e.g., metabolic panel and complete blood count test results). We found significant enrichment of biological indicators of lung, liver, and gastrointestinal dysfunction associated with disease severity using metabolite and protein profiles. Our analyses specifically identified enriched proteins that play a critical role in responses to injury or infection within these anatomical sites, but may contribute to excessive systemic inflammation within the context of COVID-19. Furthermore, we have used this information in conjunction with machine learning algorithms to predict the health status of patients presenting symptoms of COVID-19. This work provides a roadmap for understanding the biochemical pathways and molecular mechanisms that drive disease severity, progression, and treatment of COVID-19.

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.

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