Download A Future for Knowledge Acquisition: 8th European Knowledge by Luc Steels, Guus Schreiber, Walter Van de Velde PDF

By Luc Steels, Guus Schreiber, Walter Van de Velde

This quantity includes a range of the main papers awarded on the 8th eu wisdom Acquisition Workshop (EKAW '94), held in Hoegaarden, Belgium in September 1994.
The booklet demonstrates that paintings within the mainstream of information acquisition results in important sensible effects and places the data acquisition company in a broader theoretical and technological context. The 21 revised complete papers are conscientiously chosen key contributions; they deal with wisdom modelling frameworks, the identity of conventional parts, method elements, and architectures and purposes. the quantity opens with a considerable preface by means of the quantity editors surveying the contents.

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Read or Download A Future for Knowledge Acquisition: 8th European Knowledge Acquisition Workshop, EKAW '94 Hoegaarden, Belgium, September 26–29, 1994 Proceedings PDF

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Extra info for A Future for Knowledge Acquisition: 8th European Knowledge Acquisition Workshop, EKAW '94 Hoegaarden, Belgium, September 26–29, 1994 Proceedings

Example text

Hierarchical extracted data Xs of all terms X such that goal G is true (it is assumed that X occurs in G). The solution and the result are shown in Figure 4. Note that we assume that i) the root node of the document has index 0, and ii) predicate content(TextNode, Content) is used to determine the content of a text node. Using Logic Programming and XML Technologies for Data Extraction from Web Pages Definition 3 (Pattern graph) Let W be a set denoting all vertices. A pattern graph G is a quadruple 〈A, V, L, λa〉 such that V ⊆ W, A ⊆ V × V, L ⊆ V and λa : A → {‘c’, ‘n’}.

Note that for expressing logic programs we are using the standard Prolog notation (Sterling, 1994): ? next(E,F). The query can be more conveniently packed as a rule as follows: extract(A) :tag(A,text),child(B,A),child(C,B), child(D,C),tag(D,table),child(E,D),next(E,F). The rule representation has at least three obvious advantages: i) modularity: the knowledge embodied in the query is encapsulated inside the body of the predicate extract; ii) reusability: the query can be more easily reused rather than having to fully copy the conjunction of conditions and iii) information hiding: the variables occurring in the right-hand side of the rule are hidden to the user, that is, running the initial version of the query would produce a tuple of variable bindings  as solution (A, B, C, D, E, and F), while running the rule version would produce the single variable binding A as solution.

A single table from the original target document). For example, wrappers W1 and W2 can be defined as logic programs as shown in Box 4. Note that for the combination of W1 and W2 into a single L-wrapper of arity 3, we need to extend the definition of a L-wrapper by adding a new argument to relation extract for representing the root node of the document fragment to which the wrapper is applied, that is, instead of extract(N1, …, Nk) we shall now have extract(R, N1, …, Nk), R is the new argument.

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