A review of building information modeling (BIM) and the internet of things (IoT) devices integration: Present status and future trends

In contrast to other approaches that provide methodological guidance for ontology engineering, the NeOn Methodology does not prescribe a rigid workflow, but instead it suggests a variety of pathways for developing ontologies. The nine scenarios proposed in the methodology cover commonly occurring situations, for example, when available ontologies need to be re-engineered, aligned, modularized, localized to support different languages and cultures, and integrated with ontology design patterns and non-ontological resources, such as folksonomies or thesauri. In addition, the NeOn Methodology framework provides (a) a glossary of processes and activities involved in the development of ontologies, (b) two ontology life cycle models, and (c) a set of methodological guidelines for different processes and activities, which are described (a) functionally, in terms of goals, inputs, outputs, and relevant constraints; (b) procedurally, by means of workflow specifications; and (c) empirically, through a set of illustrative examples.

https://www.springer.com/cda/content/document/cda_downloaddocument/9783642247934-c1.pdf?SGWID=0-0-45-1320441-p174265158

The NeOn Methodology for Ontology Engineering

EXPRESS to OWL for construction industry: Towards a recommendable and usable ifcOWL ontology

Semantic web technologies in AEC industry: A literature overview

One of the major barriers to the deployment of Linked Data is the difficulty that data publishers have in determining which vocabularies to use to describe the semantics of data. This system report describes Linked Open Vocabularies (LOV), a high quality catalogue of reusable vocabularies for the description of data on the Web. The LOV initiative gathers and makes visible indicators that have not been previously harvested such as the interconnections between vocabularies, version history along with past and current referent (individual or organization). LOV goes beyond existing Semantic Web vocabulary search engines and takes into consideration the value's property type, matched with a query, to improve vocabulary terms scoring. By providing an extensive range of data access methods (SPARQL endpoint, API, data dump or UI), we try to facilitate the reuse of well-documented vocabularies in the Linked Data ecosystem. We conclude that the adoption in many applications and methods of LOV shows the benefits of such a set of vocabularies and related features to aid the design and publication of data on the Web.

/pdf/linked-open-vocabularies-lov-a-gateway-to-reusable-semantic-254f9msfsg.pdf

Linked Open Vocabularies (LOV): a gateway to reusable semantic vocabularies on the Web

This paper presents two contributions to the field of Ontology Evaluation. First, a live catalogue of pitfalls that extends previous works on modeling errors with new pitfalls resulting from an empirical analysis of over 693 ontologies. Such a catalogue classifies pitfalls according to the Structural, Functional and Usability-Profiling dimensions. For each pitfall, we incorporate the value of its importance level (critical, important and minor) and the number of ontologies where each pitfall has been detected. Second, OOPS! (OntOlogy Pitfall Scanner!), a tool for detecting pitfalls in ontologies and targeted at newcomers and domain experts unfamiliar with description logics and ontology implementation languages. The tool operates independently of any ontology development platform and is available online. The evaluation of the system is provided both through a survey of users' satisfaction and worldwide usage statistics. In addition, the system is also compared with existing ontology evaluation tools in terms of coverage of pitfalls detected.

/pdf/oops-ontology-pitfall-scanner-an-on-line-tool-for-ontology-l4irgaxzi8.pdf

OOPS! (OntOlogy Pitfall Scanner!): An On-line Tool for Ontology Evaluation

Ontology quality can be affected by the difficulties involved in ontology modelling which may imply the appearance of anomalies in ontologies This situation leads to the need of validating ontologies, that is, assessing their quality and correctness Ontology validation is a key activity in different ontology engineering scenarios such as development and selection This paper contributes to the ontology validation activity by proposing a web-based tool, called OOPS!, independent of any ontology development environment, for detecting anomalies in ontologies This tool will help developers to improve ontology quality by automatically detecting potential errors

/pdf/validating-ontologies-with-oops-27fv4aan6b.pdf

Validating ontologies with OOPS

The widespread use of mobile devices is leading to a next generation of applications that exploit user contextual information to provide a richer experience. One of the activities to perform during the development of these context-aware applications is to define a model to represent and manage context information. Currently, there is a lack of consensual models, and this supposes a handicap when developing these applications. This paper presents a context ontology network to model context-related knowledge that allows adapting applications based on user context. We describe the methodological process followed during the ontology development as well as the ontology network obtained from this process. Besides, we provide an example of how to extend the ontology for a particular use case in a concrete domain.

/pdf/a-context-ontology-for-mobile-environments-4zreln595l.pdf

A Context Ontology for Mobile Environments

The Linked Data initiative continues to grow making more datasets available; however, discovering the type of data contained in a dataset, its structure, and the vocabularies used still remains a challenge hindering the querying and reuse. VoID descriptions provide a starting point but a more detailed analysis is required to unveil the implicit vocabulary usage such as common data patterns. Such analysis helps the selection of datasets, the formulation of effective queries, or the identification of quality issues. Loupe is an online tool for inspecting datasets by looking at both implicit data patterns as well as explicit vocabulary definitions in data. This demo paper presents the dataset inspection capabilities of Loupe.

/pdf/loupe-an-online-tool-for-inspecting-datasets-in-the-linked-2b04bckc6c.pdf

María Poveda-Villalón

Papers

Linked Open Vocabularies (LOV): a gateway to reusable semantic vocabularies on the Web

OOPS! (OntOlogy Pitfall Scanner!): An On-line Tool for Ontology Evaluation

Validating ontologies with OOPS

A Context Ontology for Mobile Environments

Loupe - An Online Tool for Inspecting Datasets in the Linked Data Cloud.