Web-based systems accessed in real environments are analyzed, identifying technical and non-technical factors critical to good practice and effective use. User-centric and e-enabling approaches to successful deployment are suggested, applying theoretical background and interactions with practitioners to assess feasibility of practice. Specific technical innovations by Web and Semantic Web researchers and practitioners are recommended, as is user education and community support, to foster widespread successful Web and Semantic Web use.

No abstract

No abstract

No abstract

Inductive inference is a learning process based on discovering models for bodies of knowledge, given sample information. The inference process we discuss here is concerned with inductive acquisition of syntactic models for context-free languages (CFLs), given appropriate language samples. The knowledge to be modeled in this case is any CFL L, with the model to be determined a recognitive or generative characterization of L's syntactic structure. L will be learned syntactically once a machine M recognizing L, or a context-free grammar (CFG) G generating L, is inductively inferred from a sentence sample. The capability of distinguishing between L and its complement, or of generating all and only L 's sentences, is the knowledge acquired, with the learner (inference process) gaining this knowledge by acquiring M or G. An observer (informant, teacher, or oracle) has such knowledge of L and can provide the learner with appropriate sample information to ensure that M or G is correctly identified.