scholarly journals Using decision trees for inductively driven semantic integration and ontology matching

2021 ◽  
Author(s):  
Bart Gajderowicz
Keyword(s):  
Decision Trees ◽  
Real World ◽  
Semantic Integration ◽  
Use Cases ◽  
Ontology Matching ◽  
Bottom Up ◽  
Matching Algorithm ◽  
Matching Process ◽  
Narrow Context

The popularity of ontologies for representing the semantics behind many real-world domains has created a growing pool of ontologies on various topics. While different ontologists, experts, and organizations create the vast majority of ontologies, often for internal use of for use in a narrow context, their domains frequently overlap in a wider context, specifically for complementary domains. To assist in the reuse of ontologies, this thesis proposes a bottom-up technique for creating concept anchors that are used for ontology matching. Anchors are ontology concepts that have been matched to concepts in an eternal ontology. The matching process is based on inductively derived decision trees rules for an ontology that are compared with rules derived for external ontologies. The matching algorithm is intended to match taxomonies, ontologies which define subsumption relations between concepts, with an associated database used to derive the decision trees. This thesis also introduces several algorithm evolution measures, and presents a set of use cases that demonstrate the strengths and weaknesses of the matching process.

scholarly journals Using decision trees for inductively driven semantic integration and ontology matching

2021 ◽  
Author(s):  
Bart Gajderowicz
Keyword(s):  
Decision Trees ◽  
Real World ◽  
Semantic Integration ◽  
Use Cases ◽  
Ontology Matching ◽  
Bottom Up ◽  
Matching Algorithm ◽  
Matching Process ◽  
Narrow Context

The popularity of ontologies for representing the semantics behind many real-world domains has created a growing pool of ontologies on various topics. While different ontologists, experts, and organizations create the vast majority of ontologies, often for internal use of for use in a narrow context, their domains frequently overlap in a wider context, specifically for complementary domains. To assist in the reuse of ontologies, this thesis proposes a bottom-up technique for creating concept anchors that are used for ontology matching. Anchors are ontology concepts that have been matched to concepts in an eternal ontology. The matching process is based on inductively derived decision trees rules for an ontology that are compared with rules derived for external ontologies. The matching algorithm is intended to match taxomonies, ontologies which define subsumption relations between concepts, with an associated database used to derive the decision trees. This thesis also introduces several algorithm evolution measures, and presents a set of use cases that demonstrate the strengths and weaknesses of the matching process.

scholarly journals Evaluation of two heuristic approaches to solve the ontology meta-matching problem

2017 ◽  
Author(s):  
Jorge Martinez-Gil ◽  
José F. Aldana-Montes
Keyword(s):  
Genetic Algorithm ◽  
Similarity Measure ◽  
Ontology Matching ◽  
Complex Nature ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Greedy Strategy ◽  
Heuristic Approaches ◽  
Matching Process ◽  
Maximum Similarity

Nowadays many techniques and tools are available for addressing the ontology matching problem, however, the complex nature of this problem causes existing solutions to be unsatisfactory. This work aims to shed some light on a more flexible way of matching ontologies. Ontology meta-matching, which is a set of techniques to configure optimum ontology matching functions. In this sense, we propose two approaches to automatically solve the ontology meta-matching problem. The first one is called maximum similarity measure, which is based on a greedy strategy to compute efficiently the parameters which configure a composite matching algorithm. The second approach is called genetics for ontology alignments and is based on a genetic algorithm which scales better for a large number of atomic matching algorithms in the composite algorithm and is able to optimize the results of the matching process.

scholarly journals Pragmatic MDR: a metadata repository with bottom-up standardization of medical metadata through reuse

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Stefan Hegselmann ◽  
Michael Storck ◽  
Sophia Gessner ◽  
Philipp Neuhaus ◽  
Julian Varghese ◽  
...  
Keyword(s):  
Real World ◽  
Medical Data ◽  
Use Cases ◽  
Medical Documentation ◽  
Top Down ◽  
Bottom Up ◽  
Item Quality ◽  
Metadata Repository ◽  
Data Elements ◽  
Medical Concepts

Abstract Background The variety of medical documentation often leads to incompatible data elements that impede data integration between institutions. A common approach to standardize and distribute metadata definitions are ISO/IEC 11179 norm-compliant metadata repositories with top-down standardization. To the best of our knowledge, however, it is not yet common practice to reuse the content of publicly accessible metadata repositories for creation of case report forms or routine documentation. We suggest an alternative concept called pragmatic metadata repository, which enables a community-driven bottom-up approach for agreeing on data collection models. A pragmatic metadata repository collects real-world documentation and considers frequent metadata definitions as high quality with potential for reuse. Methods We implemented a pragmatic metadata repository proof of concept application and filled it with medical forms from the Portal of Medical Data Models. We applied this prototype in two use cases to demonstrate its capabilities for reusing metadata: first, integration into a study editor for the suggestion of data elements and, second, metadata synchronization between two institutions. Moreover, we evaluated the emergence of bottom-up standards in the prototype and two medical data managers assessed their quality for 24 medical concepts. Results The resulting prototype contained 466,569 unique metadata definitions. Integration into the study editor led to a reuse of 1836 items and item groups. During the metadata synchronization, semantic codes of 4608 data elements were transferred. Our evaluation revealed that for less complex medical concepts weak bottom-up standards could be established. However, more diverse disease-related concepts showed no convergence of data elements due to an enormous heterogeneity of metadata. The survey showed fair agreement (Kalpha = 0.50, 95% CI 0.43–0.56) for good item quality of bottom-up standards. Conclusions We demonstrated the feasibility of the pragmatic metadata repository concept for medical documentation. Applications of the prototype in two use cases suggest that it facilitates the reuse of data elements. Our evaluation showed that bottom-up standardization based on a large collection of real-world metadata can yield useful results. The proposed concept shall not replace existing top-down approaches, rather it complements them by showing what is commonly used in the community to guide other researchers.

scholarly journals DAEOM: A Deep Attentional Embedding Approach for Biomedical Ontology Matching

2020 ◽  
Vol 10 (21) ◽  
pp. 7909
Author(s):  
Jifang Wu ◽  
Jianghua Lv ◽  
Haoming Guo ◽  
Shilong Ma
Keyword(s):  
Network Structure ◽  
Threshold Value ◽  
Representation Learning ◽  
Biomedical Ontology ◽  
Semantic Integration ◽  
Ontology Matching ◽  
Matching Process ◽  
Learning Techniques ◽  
Expert User ◽  
Final Alignment

Ontology Matching (OM) is performed to find semantic correspondences between the entity elements of different ontologies to enable semantic integration, reuse, and interoperability. Representation learning techniques have been introduced to the field of OM with the development of deep learning. However, there still exist two limitations. Firstly, these methods only focus on the terminological-based features to learn word vectors for discovering mappings, ignoring the network structure of ontology. Secondly, the final alignment threshold is usually determined manually within these methods. It is difficult for an expert to adjust the threshold value and even more so for a non-expert user. To address these issues, we propose an alternative ontology matching framework called Deep Attentional Embedded Ontology Matching (DAEOM), which models the matching process by embedding techniques with jointly encoding ontology terminological description and network structure. We propose a novel inter-intra negative sampling skill tailored for the structural relations asserted in ontologies, and further improve our iterative final alignment method by introducing an automatic adjustment of the final alignment threshold. The preliminary result on real-world biomedical ontologies indicates that DAEOM is competitive with several OAEI top-ranked systems in terms of F-measure.

scholarly journals Definition of an Ontology Matching Algorithm for Context Integration in Smart Cities

Sensors ◽  
2014 ◽  
Vol 14 (12) ◽  
pp. 23581-23619 ◽  
Author(s):  
Lorena Otero-Cerdeira ◽  
Francisco Rodríguez-Martínez ◽  
Alma Gómez-Rodríguez
Keyword(s):  
Smart Cities ◽  
Ontology Matching ◽  
Matching Algorithm ◽  
Context Integration ◽  
Definition Of

scholarly journals Development of an Innovative Real-World Evidence Registry for the Herpes Simplex Virus: Case Study

10.2196/16933 ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. e16933 ◽  
Author(s):  
Michelle Helena van Velthoven ◽  
Ching Lam ◽  
Caroline de Cock ◽  
Terese Stenfors ◽  
Hassan Chaudhury ◽  
...  
Keyword(s):  
Herpes Simplex Virus ◽  
Herpes Simplex ◽  
Real World ◽  
Physical Symptoms ◽  
Use Cases ◽  
Real World Data ◽  
World Data ◽  
Real World Evidence ◽  
Simplex Virus

Background Infection with the herpes simplex virus (HSV) is common but not well understood. Furthermore, there remains a social stigma surrounding HSV that can have psychosocial implications for those infected. Despite many patients infected with HSV experiencing mild-to-severe physical symptoms, only one subeffective treatment is available. A registry collecting real-world data reported by individuals potentially infected with HSV could help patients to better understand and manage their condition. Objective This study aimed to report on the development of a registry to collect real-world data reported by people who might be infected with HSV. Methods A case study design was selected as it provides a systematic and in-depth approach to investigating the planning phase of the registry. The case study followed seven stages: plan, design, prepare, collect, analyze, create, and share. We carried out semistructured interviews with experts, which were thematically analyzed and used to build use cases for the proposed registry. These use cases will be used to generate detailed models of how a real-world evidence registry might be perceived and used by different users. Results The following key themes were identified in the interviews: (1) stigma and anonymity, (2) selection bias, (3) understanding treatment and outcome gaps, (4) lifestyle factors, (5) individualized versus population-level data, and (6) severe complications of HSV. We developed use cases for different types of users of the registry, including individuals with HSV, members of the public, researchers, and clinicians. Conclusions This case study revealed key considerations and insights for the development of an appropriate registry to collect real-world data reported by people who might be infected with HSV. Further development and testing of the registry with different users is required. The registry must also be evaluated for the feasibility and effectiveness of collecting data to support symptom management. This registry has the potential to contribute to the development of vaccines and treatments and provide insights into the impact of HSV on other conditions.

scholarly journals An Iterative Automatic Final Alignment Method in the Ontology Matching System

2018 ◽  
Vol 42 (1) ◽  
pp. 39-61 ◽  
Author(s):  
Marko Gulić ◽  
Marin Vuković
Keyword(s):  
Heterogeneous Data ◽  
Data Sources ◽  
Ontology Matching ◽  
Alignment Method ◽  
Automatic Adjustment ◽  
Matching Process ◽  
Heterogeneous Data Sources ◽  
Final Alignment

Ontology matching plays an important role in the integration of heterogeneous data sources that are described by ontologies. In order to determine correspondences between ontologies, a set of matchers can be used. After the execution of these matchers and the aggregation of the results obtained by these matchers, a final alignment method is executed in order to select appropriate correspondences between entities of compared ontologies. The final alignment method is an important part of the ontology matching process because it directly determines the output result of this process. In this paper we improve our iterative final alignment method by introducing an automatic adjustment of final alignment threshold as well as a new rule for determining false correspondences with similarity values greater than adjusted threshold. An evaluation of the method is performed on the test ontologies of the OAEI evaluation contest and a comparison with other final alignment methods is given.

scholarly journals The Path of Emergent Experience in the Consumer IoT: From Early Adoption to Radical Changes in Consumers’ Lives

2018 ◽  
Vol 10 (2) ◽  
pp. 10-17
Author(s):  
Donna L. Hoffman ◽  
Thomas P. Novak
Keyword(s):  
Use Cases ◽  
Mass Market ◽  
Bottom Up ◽  
Inherent Value ◽  
Early Adoption ◽  
Main Challenge ◽  
Smart Products ◽  
Single Use

Abstract Up to now, IoT device adoption is happening mainly in the niche segments of technologically sophisticated upscale consumers and technology-focused DIYers. To reach a broader range of users, marketers must do a better job of understanding and offering the inherent value of smart products. Current marketing approaches are fragmented and tend to focus on individual products and single use cases. They may actually be underselling the consumer IoT. The mass-market consumer is not buying a platform or devices controlled by an algorithm, they are buying an experience. We need to ask, in what ways consumers and devices will interact with each other to create the experience they actually seek. Therefore, the main challenge is to implement a bottom-up approach that encourages users to experiment with their devices and their interactions and to integrate their individual experiences into everyday routines.

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