Ontology Builder Research Articles

How are ontologies implemented to represent clinical practice guidelines in clinical decision support systems: protocol for a systematic review

BackgroundClinical practice guidelines are statements which are based on the best available evidence, and their goal is to improve the quality of patient care. Integrating clinical practice guidelines into computer systems can help physicians reduce medical errors and help them to have the best possible practice. Guideline-based clinical decision support systems play a significant role in supporting physicians in their decisions. Meantime, system errors are the most critical concerns in designing decision support systems that can affect their performance and efficacy. A well-developed ontology can be helpful in this matter. The proposed systematic review will specify the methods, components, language of rules, and evaluation methods of current ontology-driven guideline-based clinical decision support systems.MethodsThis review will identify literature through searching MEDLINE (via Ovid), PubMed, EMBASE, Cochrane Library, CINAHL, ScienceDirect, IEEEXplore, and ACM Digital Library. Gray literature, reference lists, and citing articles of the included studies will be searched. The quality of the included studies will be assessed by the mixed methods appraisal tool (MMAT-version 2018). At least two independent reviewers will perform the screening, quality assessment, and data extraction. A third reviewer will resolve any disagreements. Proper data analysis will be performed based on the type of system and ontology engineering evaluation data.DiscussionThe study will provide evidence regarding applying ontologies in guideline-based clinical decision support systems. The findings of this systematic review will be a guide for decision support system designers and developers, technologists, system providers, policymakers, and stakeholders. Ontology builders can use the information in this review to build well-structured ontologies for personalized medicine.Systematic review registrationPROSPERO CRD42018106501

CargO-S: A pattern-based well-founded legal domain ontology for the traceability of goods in logistic sea corridors

Building legal domain ontologies is a prominent challenge in the ontology engineering community. The ontology builders confront issues such as the complexity of the legal domain, the difficulty of applying existing ontology engineering approaches, and the intention of developing legal models faithful to realities. In this paper, we discuss constructing a well-founded legal domain ontology, named CargO-S, for the traceability of goods in logistic sea corridors. For building CargO-S, a pattern-oriented approach is applied, supported by ontology-driven conceptual modeling, ontology layering, and ontology reuse processes. CargO-S is grounded in the unified foundational ontology UFO by using the ontology-driven conceptual modeling language OntoUML. Besides, ontology layering is proposed to simplify the development process by dividing CargO-S into three layers located at different granularity levels: upper, core, and domain. For building the upper and core layers, conceptual ontology patterns are reused from the foundational ontology UFO and the legal core ontology UFO-L. These patterns are applied, either by extension or analogy with legal rules, for building the domain layer. CargO-S is then validated by implementing the ontology as OWL and SWRL rules. Finally, the performance and the semantic accuracy of CargO-S are evaluated using a dual evaluation approach.

SOBA: Semi-automated Ontology Builder for Aspect-based sentiment analysis

This research explores the possibility of improving knowledge-driven aspect-based sentiment analysis (ABSA) in terms of efficiency and effectiveness. This is done by implementing a Semi-automated Ontology Builder for Aspect-based sentiment analysis (SOBA). Semi-automatization of the ontology building process could produce more extensive ontologies, whilst shortening the building time. Furthermore, SOBA aims to improve the effectiveness of its ontologies in ABSA by attaching to concepts the semantics provided by a semantic lexicon. To evaluate the performance of SOBA, ontologies are created using the ontology builder for the restaurant and laptop domains. The use of these ontologies is then compared with the use of manually constructed ontologies in a state-of-the-art knowledge-driven ABSA model, the Two-Stage Hybrid Model (TSHM). The results show that it is difficult for a machine to beat the quality of a human made ontology, as SOBA does not improve the effectiveness of TSHM, achieving similar results. Including the semantics provided by a semantic lexicon in general increases the performance of TSHM, albeit not significantly. However, SOBA decreases by 50% or more the human time needed to build ontologies, so that it is recommended to use SOBA for knowledge-driven ABSA frameworks, as it leads to greater efficiency.

SOBA: Semi-Automated Ontology Builder for Aspect-Based Sentiment Analysis

This research explores the possibility of improving knowledge-driven aspect-based sentiment analysis (ABSA) in terms of efficiency and effectiveness. This is done by implementing a Semi-automated Ontology Builder for Aspect-based sentiment analysis (SOBA). Semi-automatization of the ontology building process could produce more extensive ontologies, whilst shortening the building time. Furthermore, SOBA aims to improve the effectiveness of its ontologies in ABSA by attaching to concepts the semantics provided by a semantic lexicon. To evaluate the performance of SOBA, ontologies are created using the ontology builder for the restaurant and laptop domains. The use of these ontologies is then compared with the use of manually constructed ontologies in a state-of-the-art knowledge-driven ABSA model, the Two-Stage Hybrid Model (TSHM). The results show that it is difficult for a machine to beat the quality of a human made ontology, as SOBA does not improve the effectiveness of TSHM, achieving similar results. Including the semantics provided by a semantic lexicon in general increases the performance of TSHM, albeit not significantly. However, SOBA decreases by 50% or more the human time needed to build ontologies, so that it is recommended to use SOBA for knowledge-driven ABSA frameworks, as it leads to greater efficiency.

TM-Builder: An Ontology Builder based on XML Topic Maps


 
 
 Everyday a huge number of new information resources are linked to the web. This way the web is growing very fast, making search tasks more and more difficult with worse results. To solve the problem several initiatives were undertaken and a new area of research and development emerged: the one called Semantic Web.When we refer to the semantic web we are thinking about a network of concepts. Each concept has a group of related resources and can be related to other concepts; we can then use this concept network to navigate among web resources or simply among information resources. From the undertaken initiatives one became an ISO standard: Topic Maps ISO 13250.
 The aim of this paper is to introduce a Topic Map (TM) Builder, that is a processor that extracts topics and relations from instances of a family of XML documents.A TM-Builder is strongly dependent on the resources structure. So, to extract a topic map for different collections of information resources (sets of documents with different structures) we have to implement several TM-Builders, one for each collection. This is not very easy! To overcome this inconvenient we have created an XML abstraction layer for TM-Builders that enables us to specify the topic map we want to build from a concrete family of resources, in order to generate automatically the intended extractor.
 To describe that process, i.e. the extraction of knowledge from XML documents to produce a TM, we present a language to specify topic maps for a class of XML documents, that we call XSTM (XML Specification for Topic Maps). We also discuss a XSL processor that automatically generates the Extractor from its formal specification written in XSTM, the XSTM-P.
 
 

Ontology Usability Scale: Context-aware Metrics for the Effectiveness, Efficiency and Satisfaction of Ontology Uses

<p class="p1">Both ontology builders and users need a way to evaluate ontologies in terms of usability, but existing ontology evaluation approaches do not fit this purpose. We propose the Ontology Usability Scale (OUS), a ten-item Likert scale derived from statements prepared according to a semiotic framework and an online poll in the Semantic Web community to provide a practical way of ontology usability evaluation. Case studies were conducted to bookkeep current usability evaluation results for ontologies expecting revisions in the future, and discussions of the poll results are presented to help proper use and customization of the OUS.

An Enhanced Web Document Search Engine using a Semantic Network

With the rapid advancement of ICT technology, the World Wide Web (referred to as the Web) has become the biggest information repository whose volume keeps growing on a daily basis. The challenge is how to find the most wanted information from the Web with a minimum effort. This paper presents a novel ontology-based framework for searching the related web pages to a given term within a few given specific websites. With this framework, a web crawler first learns the content of web pages within the given websites, then the topic modeller finds the relations between web pages and topics via key words found on the web pages using the Latent Dirichlet Allocation (LDA) technique. After that, the ontology builder establishes an ontology which is a semantic network of web pages based on the topic model. Finally, a reasoner can find the related web pages to a given term by making use of the ontology. The framework and related modelling techniques have been verified using a few test websites and the results convince its superiority over the existing web search tools.

Canonical Sensor Ontology Builder Based on ISO/IEC 11179 for Sensor Network Environments: A Standardized Approach

The advancement of sensor technology has led to an explosive increase in sensors. It causes semantic heterogeneity problems, and much research has focused on sensor ontology building to solve the problems. However, there are still remaining several issues, and one of the most critical issues is about a method for progressive and dynamic concepts management and reuse of sensor ontology. This paper proposes an ontology generation system based on ISO/IEC 11179–MDR (metadata registry). The proposed system is referred to as the Canonical Sensor Ontology Builder (CaSOB) and can create ontologies by reusing the common concepts registered in a canonical sensor ontology concept registry, an MDR. This paper defines a mapping model and processes to create ontology with the concepts registered in an MDR. Our proposal provides many advantages such as high standardization, consistent concept usage, and easy semantic exchange. Therefore, CaSOB facilitates the high quality sensor ontology creation and reduces the costs of sensor ontology integration and system development.

MOVIO: A Toolkit for Creating Curated Digital Exhibitions

In 2011, the Italian Ministry for Cultural Heritage and Tourism (MiBACT) published a guideline reference book analysing the state of the art and best practices of digital exhibitions made available on-line and offered a handbook successfully translated in English and even in Arabic. To satisfy the needs expressed by museum curators (but not limiting to them) GruppoMeta has implemented the MOVIO platform under the coordination of ICCU: MOVIO is a semantic CMS which provides tools to support the development of virtual/digital exhibitions, touristic and didactic applications. MOVIO supports the creation of a media archive and ‘non-scaring’ ontology builder for a storytelling approach and it allows cultural content publishing (it includes the creation of visit paths, up to mapping, time-line, galleries and social tools). The MOVIO open source SCMS platform is an easy and ready to use toolkit to build online and mobile virtual/digital exhibitions and narrations. It has begun to be experimented by several Italian institutions and several European partners from the AthenaPlus consortium.

GEONTO-MET: an approach to conceptualizing the geographic domain

To date, there are different ontologies for many domains and applications. Users can access them to share information, reuse knowledge, and integrate data sources for several purposes and applications such as semantic web, data warehousing, e-learning, e-commerce, knowledge representation, and so on. Ontology engineering is rapidly becoming a mature discipline, having produced tools and methodologies for building and managing ontologies. However, even with a clearly defined engineering methodology, building an ontology remains a challenging, time-consuming, and error-prone task, because it forces ontology builders to conceptualize their expert knowledge explicitly and to re-organize it in typical ontological categories such as concepts, properties, and axioms. In this article, an approach to conceptualizing the geographic domain is described. It is oriented toward formalizing a geographic domain conceptualization according to specifications from the Mexican Institute of Statistics, Geography and Informatics. The main goal is to provide semantic and ontological descriptions, which represent the properties and relationships that describe the behavior of geographic objects by means of concepts. GEONTO-MET is focused on developing geographic application ontologies for sharing and integrating geospatial information.

Medicine expert system dynamic Bayesian Network and ontology based

The paper proposes an application framework to be used for medicine assisted diagnosis based on ontology and Bayesian Network (DBNO). There are two goals: (1) to separate the domain knowledge from the probabilistic information and (2) to create an intuitive user interface. The framework architecture has three layers: knowledge, uncertainty model and user interface. The contributions of the domain experts are decoupled, the ontology builder will create the domain concepts and relationships focusing on the domain knowledge only. The uncertainty model is Bayesian Network and the probabilities of the variables states are stored in a profile repository. The diagnostician will use the user interface feeded with the domain ontology and one uncertainty profile. The application was tested on a sample medicine model for the diagnose of heart disease.

Geo‐ontology Tools: The Missing Link

AbstractNumerous authors have presented ontology building tools that have all been developed as part of academic projects and that are usually adaptations of more generic tools for geo‐spatial applications. While we trust that these tools do their job for the special purpose they have been built, the GIScience user community is still a long way away from off‐the‐shelf ontology builders that can be used by GIS project managers. In this article, we present a comparative study of ontology building tools described in some twenty peer‐reviewed GIScience journal articles. We analyze them from the perspective of two application domains, crime analysis and transportation/land use. For the latter, we developed a database schema, which is substantially different from the three main templates commonly used. The crime analysis application uses a rule base for an agent‐based model that had no precursor. In both cases, the currently available set of tools cannot replace manual coding of ontologies for use with ESRI‐based application software. Based on these experiences, we outline a requirements list of what the tools described in the first part of the article are missing to make them practical from an applications perspective. The result is an R&amp;D agenda for this important aspect of GIScience.

Concept Extraction from Plain Text for Ontology Builder

Concept Extraction from Plain Text for Ontology Builder