Context-aware similarity assessment within semantic space formed in linked data

Abstract

The Web is a constantly growing repository of information. Amount of data that becomes available exceeds our abilities to search and examine this data in a reasonable time and with a practical effort. The data is stored in forms of documents, texts and web pages, which are not suitable for comprehensive analysis and search. In order to make the data stored on the Internet more accessible, a new model of data representation has been introduced—linked data. Linked data provides an open platform for representing and storing structured data as well as metadata. In this paper, we propose a novel approach for calculating the degree of similarity between two entities in the web of linked data. The idea is based on the fact that entities are submerged in the linked data and their semantics is defined via their connections to other entities. Therefore, similarity between two entities is determined by comparing connections of two entities to other entities. Firstly, the approach is introduced to determine semantic similarity in a context-free manner. This method does not select specific types of connections but takes into consideration all of them. Secondly, a context-aware approach is presented as a modification of the original method. In this case, a context is defined by a set of connection types—only connections of specific types are considered for similarity determination. The proposed approach uses concepts of possibility theory to determine lower and upper bounds of similarity intervals. We evaluate the proposed similarity assessment process by applying it to real-world datasets, and we compare it to other related methods.