On Maintaining Semantic Networks: Challenges, Algorithms, Use Cases

PURPOSE: Knowledge workers are confronted with a massive load of data from heterogeneous sources, making it difficult for them to discover information relevant in the context of their daily tasks. As a particular challenge, enterprise information needs to be aligned with business processes. In previous work, the authors introduced the Semantic Network (SN) approach for bridging this gap, i.e., for discovering explicit relations between enterprise information and business processes. What has been neglected so far, however, is SN maintenance, which is required to keep an SN consistent, complete, and up-to-date. The paper tackles this issue and extends the SN approach with methods and algorithms for enabling SN maintenance.

DESIGN/METHODOLOGY/APPROACH: The paper illustrates an approach for SN maintenance. Specifically, the authors show how an SN evolves over time, classify properties of objects and relations captured in an SN, and show how these properties can be maintained. An empirical evaluation, which is based on synthetic and real-world data, investigates the performance, scalability and practicability of the proposed algorithms.

FINDINGS: The authors prove the feasibility of the introduced algorithms in terms of runtime performance with a proof-of-concept implementation. Further, a real-world case from the automotive domain confirms the applicability of the SN maintenance approach.

ORIGINALITY/VALUE: As opposed to existing work, the presented approach allows for the automated and consistent maintenance of SNs. Furthermore, the applicability of the presented SN maintenance approach is validated in the context of a real-world scenario as well as two business cases.