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Lake Poso has high eel potential and a strategic position in supporting the development of eel fisheries in Sulawesi. Lake Poso is out of 15 national priority lakes in natural resources and environmental management programs. The catch of eel from Lake Poso is reported to have been decreasing for the last twenty years. Several factors causing the decline in eel populations are fishing activities, disruption in migration routes, and other factors such as changes in habitat and environmental conditions. The sustainability of the life of the Poso eel is determined by the success of the eel migration process. The migration of adult eels to the sea and young eels from glass eels to freshwater habitats must continue. The perspective plans that can be done to manage Poso's eel resources include 1) Socializing the eco-biological characteristics of eels as catadromous fish; 2) Establishing conservation areas and regulation of eel fishing; 3) Increasing eel population through restocking 4) Integrating fisheries and aquaculture activities; 5) Developing eel culture technology through size segmentation production; 6) Developing an eel migration monitoring system; 7) Building a cross-sectoral forum and put a joint action program; 8) Proposing the Poso eel Research and Conservation Center.

An ontology-based system can currently logically reason through the Web Ontology Language Description Logic (OWL DL). To perform probabilistic reasoning, the system must use a separate knowledge base, separate processing, or third-party applications. Previous studies mainly focus on how to represent probabilistic information in ontologies and perform reasoning through them. These approaches are not suitable for systems that already have running ontologies and Bayesian network (BN) knowledge bases because users must rewrite the probabilistic information contained in a BN into an ontology. We present a framework called ByNowLife, which is a novel approach for integrating BN with OWL by providing an interface for retrieving probabilistic information through SPARQL queries. ByNowLife catalyzes the integration process by transforming logical information contained in an ontology into a BN and probabilistic information contained in a BN into an ontology. This produces a system with a complete knowledge base. Using ByNowLife, a system that already has separate ontologies and BN knowledge bases can integrate them into a single knowledge base and perform both logical and probabilistic reasoning through it. The integration not only facilitates the unity of reasoning but also has several other advantages, such as ontology enrichment and BN structural adjustment through structural and parameter learning.