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Table 1: Implicit and explicit knowledge
2003
"... In PAGE 2: ... The engines behind the two knowledge spectrum forces are the knowledge processors, natural or artificial entities able to create abstractions from data and to instantiate abstractions in order to fit reality. Knowledge is traditionally categorized into implicit and explicit ( Table1 ) and ranges from rich representations grounded in a reality, to highly abstracted, symbolic rep- resentations of that reality. The classical distinction between data, meta-data, information, knowledge and meta-knowledge is simplified by our subscription to the unified view of Algorithmic Information Theory (AIT) [4] which recasts all knowledge modalities and their process- ing into a general framework requiring a Universal Turing Machine, its programs and data represented as finite binary sequences.... ..."
Table 6. The same train schedule as in Table 5, represented as a knowledge base with explicit indication of hourly trains.
1998
"... In PAGE 26: ... The selected dependency is !! Hour, which has 3 exceptions. After horizontal and vertical decomposition the resulting restructured knowledge base is as in Table6 . The rst two clauses indicate that the original train relation is the union of the new relations hourlytrain and irregtrain; the third clause expresses that hourlytrain has been vertically decomposed14 into hourlytrain1 and hour (the names of the new relations have of course been supplied by the user).... ..."
Cited by 3
TABLE V COMPTON T - TEST ON FITNESS VALUES
Table 1: Relation between knowledge creation steps and knowledge enablers.
2001
"... In PAGE 6: ... The underlying idea is that knowledge transfer is not a verbatim copying operation from sender to receiver but that it has to consider the implicit and explicit knowledge of sender and receiver as well as the local circumstances in which the knowledge has to be re-created and applied. In Table1 the relation between knowledge creation steps and knowledge enablers is sketched. An empty eld denotes no correlation, a + indicates a moderate correlation, and ++ indicates a strong correlation.... ..."
Table 1. Twenty word keyword set for RM experiments performed. Results from the lattice-based wordspotter are compared with more conventional approaches ranging from a whole-word model wordspotter to a phone-based con- nected word recogniser. Viterbi experiments where no vo- cabulary knowledge in used to constrain the garbage model are labelled as VOCIND; those where explicit vocabulary knowledge is included are labelled VOCDEP. For VOCIND experiments in which the keyword models were concate- nated from monophones, it was rst necessary to devise a method for creating garbage models from the set of mono- phones.
1994
"... In PAGE 2: ... The test set contained a total of 494 keyword occurrences. The keyword set is illustrated in Table1 . Several di erent wordspotting experiments were... In PAGE 3: ... To allow for fair comparison between network-based and lattice-based wordspotters, the network-based results were improved by rescoring all puta- tive keywords to obtain a ratio score, as in [8]. The results are obtained as a standard NIST Figure of Merit averaged across 0 to 10 false alarms per keyword per hour and are shown in Table1 . It can be seen that the lattice wordspotter Wordspotter FOM Network { Clustered VOCIND garb model 58.... ..."
Cited by 24
Table 1. Domain analysis relations and examples illustrating kinds of knowledge
"... In PAGE 7: ... But then how are those notes to be correlated and summarized? One way of systematically organizing observations is to classify them according to a framework of relations. We used a domain analysis framework suggested by Spradley ( Table1 ). The relations are illustrated with two examples, one relatively mundane (corresponding to explicit knowledge, which people typically mention in their conversations, e.... ..."
Table 2. Summary of Knowledge Resources Identified in the Frameworks
"... In PAGE 6: ....3. Knowledge Resources Three of the frameworks explicitly address the knowledge resources dimension by identifying different kinds of knowledge resources. These are summarized in Table2 . Leonard-Barton [7] identifies two types of knowledge resources: employee knowledge and physical systems (e.... In PAGE 7: ... The frameworks described and compared here can serve as a starting point for creating a generic framework that unifies KM concepts. Such a framework should be sufficiently comprehensive to address all the main content features of those presented in Table2 , 3 and 4. It should also accommodate other concepts appearing in the KM literature, but outside the scope of extant KM frameworks.... ..."
Table 2 General Purpose Algorithms for RNA Motif Detection. Tools that detect a special class of RNA motifs are not listed here. Program Comparative or
"... In PAGE 6: ... Nevertheless, the de nition of approximative rules also requires explicit knowledge, at least to some extent. Table2 sum-... ..."
Table 1. The Task Structure for Design. For each task, there is a default compiled knowledge method that has domain-specific knowledge to directly achieve it. (This method is not included here.) For subtasks such as critiquing, I only indicate families of generic AI methods, without explicit indication of their subtasks.
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