With the expanse of Internet, Web programmers have wide choice of Web services available to them. A need arises for automatic discovery of required Web services and construction of an appropriate sequence of invocation thereof. In this paper, we present a framework for automation of this task based on currently emerging technologies such as ontological knowledge bases, OWL, OWL-S, WSDL, description logic (DL), etc. Background knowledge ontologies are created based on which semantic meanings of Web services can be given through OWL-S. An agent employs OWL-S API to extract Web service metadata, and applies a DL inference engine, called Racer, for reasoning with the metadata with respect to given background knowledge. Reasoning tasks performed by Racer include profile matchmaking, input/output subsumption testing, and preconditions/effects analysis, which are basic mechanisms for Web services discovery and invocation planning. A prototype system has been implemented

Citizen science is increasingly significant in scientific research and disaster management, especially as disaster risks intensify globally. While numerous studies confirm the value of citizen science programs in mitigating disaster risks, further research is needed to understand how these programs contribute to disaster risk reduction and to identify indicators of their success. This study investigates key success factors and challenges of citizen science programs through a case study of Safecast, a radiation monitoring initiative in Japan. Six critical factors were identified: active participation, knowledge production, data accuracy, expansion of spatial and temporal data, and citizen-related outcomes. Safecast’s broad engagement of participants, open-source data collection, and significant contributions to scientific research and policy decisions have been key to its effectiveness. In addition, the rigorous data accuracy measures and data expansion over time and space have further supported disaster risk reduction efforts. However, challenges remain in maintaining participant engagement, addressing the digital divide, and ensuring data consistency. Safecast exemplifies how citizen science can bridge grassroots data collection with formal disaster management, highlighting its potential to strengthen disaster resilience and empower communities.

With the expanse of internet, web programmers have wide choice of web services available to them. A need arises for automatic discovery of required web services and construction of an appropriate sequence of invocation thereof. In this paper, we present a framework for automation of this task based on currently emerging technologies such as ontological knowledge bases, OWL, OWL-S, WSDL, Description Logic (DL), etc. Background-knowledge ontologies are created based on which semantic meanings of web services can be given through OWL-S. An agent employs OWL-S API to extract web service metadata, and applies a DL inference engine, called Racer, for reasoning with the metadata with respect to given background knowledge. Reasoning tasks performed by Racer include profile matchmaking, input/output subsumption testing, and preconditions/effects analysis, which are basic mechanisms for web services discovery and invocation planning. A prototype system has been implemented.