# Building Semantic Knowledge Graphs from (Semi-)Structured Data: A Review
## 3 Background
### Quality and Refinement  
Knowledge graph quality and refinement refers to how a knowledge graph may be assessed and subsequently improved. There are numerous frameworks and methods for both assessing and refining knowledge graphs [3; 19; 20]. [PERSON] et al. [20] provide a survey of approaches for evaluating and assessing the quality of linked data, identifying 18 different interlinked quality dimensions as briefly described in the following. Accessibility quality describes the quality aspects concerning how a knowledge graph may be accessed and how well it supports the user to easily retrieve data from it:  
* Availability describes the degree a knowledge graph and its contents are available for interaction (e.g., through a SPARQL endpoint). * Licensing concerns whether a knowledge graph has a license published with it (human-readable and machine-readable format). * Interlinking describes the degree that entities, referring to the same real-world concept, are linked to each other [54]. * Security refers to the degree the knowledge graph is secured through verification of confidentiality in the communication between the consumers and the knowledge graph itself. * Performance relates to how well the knowledge graph may handle latency, throughput, and scalability. Intrinsic quality refers to the quality dimensions that are internal to the knowledge graph. These dimensions refer to quality dimensions that are independent of the user context:  
* Syntactic validity describes the degree to which the knowledge graph content follows syntactic rules [55]. * Semantic accuracy refers to the degree to which a knowledge graph correctly represents real-world facts semantically. * Consistency refers to how well a knowledge graph is free of contradictions in the information contained in it. * Conciseness describes the degree to which a knowledge graph only contains relevant information [56]. * Completeness concerns how complete a knowledge graph is in comparison to all the required information. Contextual quality refers to quality dimensions that usually depend on the context of the implemented knowledge graph [20]:  
* Relevancy describes the extent it is possible to obtain relevant knowledge from a knowledge graph for the task at hand. * Trustworthiness refers to how trustworthy the information contained in a knowledge graph is subjectively accepted to be correct. * Understandability refers to the extent the information contained in a knowledge graph can be used and interpreted by users without ambiguity [57]. * Timeliness refers to how well a knowledge graph is up to date based on the real-world facts [58]. Representational quality refers to the dimensions describing the design aspects of the knowledge graph:  
* Representational conciseness refers to the extent to which information is concisely represented in a knowledge graph. * Interoperability refers to the extent to which a knowledge graph represents data with respect to the existing relevant vocabularies for the subject domain [57]. * Interpretability refers to how well a knowledge graph is technically capable of providing information in an appropriate serialisation and whether a machine is capable of processing the data. * Versatility refers to which extent a knowledge graph is capable of being used in different representations and in different languages. [PERSON] described a five-star rating scheme [59], where datasets could be awarded stars with the following criteria: data are available on the Web under an open license (1 star) in (semi-)structured format (2 stars) and a non-proprietary open format (3 stars) using open standards from the W3C (4 stars) and linked to other data (5 stars). This was extended to a seven-star scheme [60]: data are provided with an explicit schema (6 stars), and data are validated against the schema (7 stars).