Microscopía electrónica de barrido

After identifying equivalent entities in the knowledge graphs, fusion policies are employed to decide how equivalent entities are merged [61]. The fusion policies include: i) Union: creates a new entity with the union of the properties of the matched entities; i.e., pairs that are syntactically the same, are unified into a single pair; ii) Subproperty policy. The policy tracks if a property of one RDF molecule is an rdfs:subPropertyOf of a property of another RDF molecule; iii) Semantic based Union: creates a new entity with the union of the properties of the matched entities; and iv) Authoritative Merge: outputs one RDF graph with the data provided from an authoritative source.

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C H A P T E R

3

Related Work

This chapter outlines the state-of-the-art with respect to the work conducted in this thesis. Relevant approaches related to the research problem as well as to the research questions are investigated. Section3.1examines general approaches for conducting semantic data integration. Next, in Section3.2we review existing methods for representing standards by means of ontologies and knowledge graphs. Section 3.3overviews existing works for formalizing standards by means of ontologies. Section 3.4reports similar works in the field of semantically exploiting standards and standardization frameworks using knowledge graphs. Approaches for integrating data of I40 standards are investigated in Section 3.4.2. Finally, in Section 3.5 a review and critical discussion of the current approaches for semantic technologies in manufacturing is carried out.

3.1 Generic Semantic Data Integration Approaches

Generic semantic data integration approaches aim at solving semantic heterogeneity conflicts independently of the domain. Several researchers have tackle the problem of semantic data integration from different views. Ontology-Based Data Integration (OBDI) is one of the most common techniques for solving this problem [62–64]. OBDI approaches are commonly used for semantic data integration since ontologies provide a semantic representation of the domain. In general, the OBDI approach comprises three components: i) the ontology for represent the knowledge of the domain; ii) the data source which typically contain the data of the domain; and iii) the mappings between the two components [62]. Cruz et al. [65] discuss different views of the use of ontologies for semantic data integration: i) Single ontology approach. All sources are directly related to a shared global ontology; ii) Multiple ontology approach. Each data source is described by its local ontology separately; and iii) Hybrid ontology approach. A combination of the single ontology approach for describing each data source in the domain with mappings to a general shared ontology. Other studies focus more on the necessary dimensions for developing the mappings. In this regard, three dimensions for mappings are researched: i) the discovery of mappings among ontologies; ii) the declarative formal representation of the mappings; and iii) the reasoning with the mappings. Mappings are required to link two ontologies representing the same domain and comprising semantic heterogeneity conflicts between them [64].

Knoblock et al. [66] present KARMA, a semi-automatic framework capable to map structured sources to ontologies to build semantic descriptions of the sources. KARMA allows for the modeling of structured sources. Further, KARMA is able to generate a source model where semantic heterogeneity conflicts between the sources are solved. SILK is a framework for

Chapter 3 Related Work

integrating heterogeneous data sources [67]. SILK identifies owl:sameAslinks among entities of two RDF datasets. This framework also enables the application of data transformations, i.e., data cleaning, data transformation, to structured data sources to perform the integration. Sieve [68] is a framework for assessing the quality of the data to be integrated. Then, Sieve determines which data should be conserved, transformed or discarded. Finally, Sieve applies various fusion policies on top of the data to semantically integrate it. Collarana et al. [61] introduce MINTE, an integration framework that collects and integrates data from heterogeneous sources into a knowledge graph. MINTE implements semantic integration techniques that rely on the concept of RDF molecules to represent the meaning of data. This approach also implements fusion policies for merging the RDF molecules and solve semantic heterogeneity conflicts between the heterogeneous sources. Rahm [69] describes the need for a holistic data integration approach capable of scaling to many data sources. The author revises six uses cases where a so-called holistic data integration is applied, i.e., meta-search, open data, integrated ontology, knowledge graphs, entity search engines, and comparison portals. By analyzing these use cases, the author argues that semantic data integration approaches should be performed on the physical level as well as on the use of clustering-based approaches to match entities and metadata (concepts, attributes). Further, a general clustering strategy for entity resolution is proposed with the aim to become a holistic approach that can be used in different domains and use cases. LDIF [70] presents a framework for integrating Linked Data at a large scale. LDIF comprises a mapping language for translating data from the different vocabularies which are used on the Web to a local target vocabulary. To translate the data that is modeled by means of different vocabularies into a local one, LDIF uses the R2R framework. Furthermore, for solving the heterogeneity conflicts LDIF relies on the SILK framework. Finally, this LDIF provides a data quality assessment of the integrated data.

To sum up, the aforementioned approaches for semantic data integration provide generic views to integrate structured data. Still, a lot of manual work is needed for achieving the integration. Most of the revised methods focus on the resolution of semantic interoperability between data sources. Additionally, specific semantic heterogeneity conflicts for standards and standardization frameworks are not considered. On the contrary, in this thesis, we focus on the problem of semantically identifying and integrating equivalent entities in the I40 domain, e.g., standards and standardization frameworks.