204,638 research outputs found
The Knowledge Life Cycle for e-learning
In this paper, we examine the semantic aspects of e-learning from both pedagogical and technological points of view. We suggest that if semantics are to fulfil their potential in the learning domain then a paradigm shift in perspective is necessary, from information-based content delivery to knowledge-based collaborative learning services. We propose a semantics driven Knowledge Life Cycle that characterises the key phases in managing semantics and knowledge, show how this can be applied to the learning domain and demonstrate the value of semantics via an example of knowledge reuse in learning assessment management
Knowledge Spaces and the Completeness of Learning Strategies
We propose a theory of learning aimed to formalize some ideas underlying
Coquand's game semantics and Krivine's realizability of classical logic. We
introduce a notion of knowledge state together with a new topology, capturing
finite positive and negative information that guides a learning strategy. We
use a leading example to illustrate how non-constructive proofs lead to
continuous and effective learning strategies over knowledge spaces, and prove
that our learning semantics is sound and complete w.r.t. classical truth, as it
is the case for Coquand's and Krivine's approaches
Distributional semantics beyond words: Supervised learning of analogy and paraphrase
There have been several efforts to extend distributional semantics beyond
individual words, to measure the similarity of word pairs, phrases, and
sentences (briefly, tuples; ordered sets of words, contiguous or
noncontiguous). One way to extend beyond words is to compare two tuples using a
function that combines pairwise similarities between the component words in the
tuples. A strength of this approach is that it works with both relational
similarity (analogy) and compositional similarity (paraphrase). However, past
work required hand-coding the combination function for different tasks. The
main contribution of this paper is that combination functions are generated by
supervised learning. We achieve state-of-the-art results in measuring
relational similarity between word pairs (SAT analogies and SemEval~2012 Task
2) and measuring compositional similarity between noun-modifier phrases and
unigrams (multiple-choice paraphrase questions)
Interactive Learning-Based Realizability for Heyting Arithmetic with EM1
We apply to the semantics of Arithmetic the idea of ``finite approximation''
used to provide computational interpretations of Herbrand's Theorem, and we
interpret classical proofs as constructive proofs (with constructive rules for
) over a suitable structure \StructureN for the language of
natural numbers and maps of G\"odel's system \SystemT. We introduce a new
Realizability semantics we call ``Interactive learning-based Realizability'',
for Heyting Arithmetic plus \EM_1 (Excluded middle axiom restricted to
formulas). Individuals of \StructureN evolve with time, and
realizers may ``interact'' with them, by influencing their evolution. We build
our semantics over Avigad's fixed point result, but the same semantics may be
defined over different constructive interpretations of classical arithmetic
(Berardi and de' Liguoro use continuations). Our notion of realizability
extends intuitionistic realizability and differs from it only in the atomic
case: we interpret atomic realizers as ``learning agents''
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