  Services are flourishing drastically both on the Internet and in the real
world. Additionally, services have become much more interconnected to
facilitate transboundary business collaboration to create and deliver distinct
new values to customers. Various service ecosystems have become a focus in both
research and practice. However, due to the lack of widely recognized service
ecosystem models and sufficient data for constructing such models, existing
studies on service ecosystems are limited to very narrow scope and cannot
effectively guide the design, optimization, and evolution of service
ecosystems. We propose a Multilayer network-based Service Ecosystem Model,
which covers a variety of service-related elements, including stakeholders,
channels, functional and nonfunctional features, and domains, and especially,
structural and evolutionary relations between them. "Events" are introduced to
describe the triggers of service ecosystem evolution. We propose a data-driven
approach for constructing MSEM from public media news and external data
sources. Qualitative comparison with state-of-the-art models shows that MSEM
has a higher coverage degree of fine-grained elements/relations in service
ecosystems and richer semantics for higher interpretability. Experiments
conducted on real news corpora show that compared with other approaches, our
approach can construct large-scale models for real-world service ecosystems
with lower cost and higher efficiency.
