Enterprise Knowledge Graphs: A Backbone of Linked Enterprise Data

Semantic technologies in enterprises have recently received increasing attention from both the research and industrial side. The concept of Linked Enterprise Data (LED) describes a framework to incorporate benefits of semantic technologies into enterprise IT environments. However, LED still remains an abstract idea lacking a point of origin, i.e., station zero from which it comes to existence. In this paper we argue and demonstrate that Enterprise Knowledge Graphs (EKGs) might be considered as an embodiment of LED lifting corporate information management to a semantic level which ultimately allows for real artificial intelligence applications. By EKG we refer to a semantic network of concepts, properties, individuals and links representing and referencing foundational and domain knowledge relevant for an enterprise. Although the concept of EKGs was not invented yesterday, both enterprise and semantic communities have not yet come up with a formal comprehensive framework for designing such graphs. In this paper we aim to join the dots between the expanding interest in EKGs expressed by those communities and the lack of blueprints for realizing the EKGs. A thorough study of the key design concepts provides a multi-dimensional aspects matrix from which an enterprise is able to choose specific features of the highest priority. We emphasize the importance of various data fusion approaches, e.g., unified and federated. In the extensive evaluation section we investigate the effect of the chosen approach on the EKG performance along several dimensions, e.g., basic reasoning and OWL entailment which account for machine understanding of the EKG data, and access control subsystem which is of the utmost importance in large enterprises.