# Property-Based Semantic Similarity Criteria to Evaluate the Overlaps of Schemas
###### Abstract  
Knowledge graph-based data integration is a practical methodology for heterogeneous legacy database-integrated service construction. However, it is neither efficient nor economical to build a new cross-domain knowledge graph on top of the schemas of each legacy database for the specific integration application rather than reusing the existing high-quality knowledge graphs. Consequently, a question arises as to whether the existing knowledge graph is compatible with cross-domain queries and with heterogenous schemas of the legacy systems. An effective criterion is urgently needed in order to evaluate such compatibility as it limits the quality upbound of the integration. This research studies the semantic similarity of the schemas from the aspect of properties. It provides a set of in-depth criteria, namely coverage and flexibility, to evaluate the pairwise compatibility between the schemas. It takes advantage of the properties of knowledge graphs to evaluate the overlaps between schemas and defines the weights of entity types in order to perform precise compatibility computation. The effectiveness of the criteria obtained to evaluate the compatibility between knowledge graphs and cross-domain queries is demonstrated using a case study. data integration; knowledge graph; ontology evaluation; schema overlap; semantic similarity +
Footnote †: journal: _Appl. the state-of-the-art general-purpose knowledge graphs such as DBpedia, schema.org, etc. Consequently, the question becomes how to choose from the existing knowledge graphs. Different approaches have been proposed, with frameworks from the perspective of engineering demonstrating considerable success, but only a few have considered the evaluation phase of this problem. Furthermore, most of the research involved qualitative discussions rather than computational criteria. This work is based on the European project 'ISCF HDRUK DIH Sprint Exemplar: Graph-Based Data Federation for Healthcare Data Science ([[https://gtr.ukri.org/projects?ref=MC_PC_18029](https://gtr.ukri.org/projects?ref=MC_PC_18029)]([https://gtr.ukri.org/projects?ref=MC_PC_18029](https://gtr.ukri.org/projects?ref=MC_PC_18029)) (accessed on 24 December 2019)), which aims to provide a solution to the integration of medical domain heterogeneous legacy systems of Scotland and Italy. On the surface, reusing the data of these legacy systems leads to data integration problems [1], but it is inefficient to consider only how to integrate the data. Therefore, we realized data integration through schema integration. During the project, we encountered problems related to semantic diversity on both the language level and knowledge level [2]. There were mismatches between the terms in the two natural languages, i.e., English and Italian, even in the same domain. Language-level diversity problems were solved by general-purpose dictionaries in a relatively straightforward manner. Knowledge-level diversity is defined as the many-to-many mappings between the **entity types** and the properties [3]. A knowledge graph can be divided into a schema knowledge graph (_lereinafter referred to as_**schema**) at the knowledge level and a data knowledge graph at the individual level. A **schema** is a similar concept to the _database scheme_, and it contains **entity types (similar to a table in a database)** and **properties (similar to attributes in a database)**. A _data knowledge graph_ is a similar concept to the _record level_ in a database, and it contains entities and property values. Figure 1 shows two different schemas in the same scenario. Individuals are difficult to reuse because of the structural differences, but schemas at the knowledge level can be reused in many general fields. Schema.org and DBpedia [4] contain a large number of high-quality schemas with uniform specifications. When constructing a new knowledge graph, effectively reusing these existing high-quality schemas can save energy and avoid conflicts between new schema and existing schema.