An ontology-driven framework towards building enterprise semantic information layer

Song, Fuqi; Zacharewicz, Grégory; Chen, David

doi:10.1016/j.aei.2012.11.003

Cited by 51 publications

(32 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For that, the team have resorted to the well-known software design pattern Strategy [11]. The resulting design is captured in the UML class diagram depicted in Figure 3 and described next.…”

Section: Software Development Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

Introducing inference-driven OWL ABox enrichment

Canito

Maio

Silva

2013

Proceedings of International Conference on Information Integration and Web-Based Applications &Amp; Services

View full text Add to dashboard Cite

Publically available text-based documents (e.g. news, meeting transcripts) are a very important source of knowledge for organizations and individuals. These documents refer domain entities such as persons, places, professional positions, decisions, actions, etc. Querying these documents (instead of browsing, searching and finding) is a very relevant task for any person in general, and particularly for professionals dealing with intensive knowledge tasks. Querying text-based documents' data, however, is not supported by common technology. For that, such documents' content has to be explicitly and formally captured into knowledge base facts. Making use of automatic NLP processes for capturing such facts is a common approach, but their relatively low precision and recall give rise to data quality problems. Further, facts existing in the documents are often insufficient to answer complex queries and, therefore, it is often necessary to enrich the captured facts with facts from third-party repositories (e.g. public LOD, private IS databases). This paper describes the adopted process to identify what data is currently missing from the knowledge base repository and which is desirable to collect from external repositories. The proposed process aims to foster and is driven by OWL DL inference-based instance (ABox) classification, which is supported by the constraints of the TBox.

show abstract

Section: Software Development Frameworkmentioning

confidence: 99%

“…Some approaches, like the system described in [12] aim to extract and expand an ontology using several sources of structured and unstructured data (e.g. web documents, databases) using NLP.…”

Section: Related Workmentioning

confidence: 99%

Introducing inference-driven OWL ABox enrichment

Canito

Maio

Silva

2013

Proceedings of International Conference on Information Integration and Web-Based Applications &Amp; Services

View full text Add to dashboard Cite

show abstract

“…Song et al [4] classify the ontology matching approaches by considering three levels of source ontology to be matched. At entity level, the class itself is treated as the object of study; the label, comment and internal information of it are investigated.…”

Section: Ontology Matchingmentioning

confidence: 99%

Pattern-based core word recognition to support ontology matching

Song

Zacharewicz

Chen

2013

KES

Self Cite

View full text Add to dashboard Cite

http://www-sop.inria.fr/members/Fuqi.Song/files/Pattern-based%20core%20word%20recognition%20to%20support%20ontology%20matching.%20Fuqi%20Song.pdfInternational audienceOntology matching is a crucial issue in the domain of semantic web and data interoperability. In this paper, a core word based method for measuring similarity from the semantic level of ontology entities is described. In ontology, most labels of entities are compound words rather than single meaningful words. However, the main meaning is represented usually by one word of them, which is called core word. The core word is learned by investigating certain patterns, which are defined based on part of speech (POS) and linguistics knowledge. The other information is noted as complementary information. An algorithm is given to measure the similarity between a pair of compound words and short texts. In order to support diverse situation, especially when core words cannot be recognized, non semantic based ontology matching techniques are applied from lexical and structural level of ontology. The described method is tested on real ontology and benchmarking data sets. It showed good matching ability and obtained promising results

show abstract

“…W3C (World Wide Web Consortium) recommends OWL as the standard semantic web ontology language for modelling ontologies (Song, Zacharewicz and Chen, 2013).…”

Section: Ontologies and Semantic Reasoningmentioning

confidence: 99%

Applying process mining and semantic reasoning for process model customisation in healthcare

Detro

Santos

Panetto

et al. 2019

Enterprise Information Systems

View full text Add to dashboard Cite

Process flexibility plays a key role in high variability environments, such as healthcare. In this type of environment, the process model needs to change some elements to adjust to specific sets of requirements. Thus, this paper proposes a process model customizing method based on ontology and process mining. The method proposed is applied in customizing process models for acute ischemic stroke treatment. During process model customization, the method provides decision-making support for users, thereby ensuring a structurally correct process customization and enabling improves patient treatment by means of recommendations.

show abstract

An ontology-driven framework towards building enterprise semantic information layer

Cited by 51 publications

References 35 publications

Introducing inference-driven OWL ABox enrichment

Introducing inference-driven OWL ABox enrichment

Pattern-based core word recognition to support ontology matching

Applying process mining and semantic reasoning for process model customisation in healthcare

Contact Info

Product

Resources

About