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In the perspective of application of distributed technology, this paper, probing into its impact on community education and structure of its service system, proposes how to construct the educational resource service of distributed community, including theoretical frame of the study, resources construction, and path of service, mechanism and safeguard. Then a systematic elaboration is made on the strategies and solutions in the digital service system of distributed community educational resources. [ABSTRACT FROM AUTHOR]

Digitized legacy document marked up with XML can be used in many ways, e.g., to generate RDF statements about the world described. A prerequisite for doing so is that the document markup is of sufficient quality. Since fully automated markup-generation methods cannot ensure this, manual corrections and cleaning are indispensable. In this paper, we report on our experiences from a digitization and markup project for a large corpus of legacy documents from the biosystematics domain, with a focus on the use of modern tools. The markup created covers both document structure and semantic details. In contrast to previous markup projects reported on in literature, our corpus consists of large publications that comprise many different semantic units, and the documents contain OCR noise and layout artifacts. A core insight is that digitization and automated markup on the one hand and manual cleaning and correction on the other hand should be tightly interleaved, and that tools supporting this integration yield a significant improvement. [ABSTRACT FROM AUTHOR]

This paper presents a method, SemSim, for the semantic search and retrieval of digital resources (DRs) that have been previously annotated. The annotation is performed by using a set of characterizing concepts, referred to as features, selected from a reference ontology. The proposed semantic search method requires that the features in the ontology are weighted. The weight represents the probability that a resource is annotated with the associated feature. The SemSim method operates in three stages. In the first stage, the similarity between concepts (consim) is computed by using their weights. In the second stage, the concept weights are used to derive the semantic similarity (semsim) between a user request and the DRs. In the last stage, the answer is returned in the form of a ranked list. An experiment aimed at assessing the proposed method and a comparison against a few among the most popular competing solutions is given. [ABSTRACT FROM AUTHOR]