By Amy Felty, Douglas Howe (auth.), Frank Pfenning (eds.)
This quantity provides the complaints of the fifth foreign convention on good judgment Programming and automatic Reasoning, held aboard the send "Marshal Koshevoi" at the Dnieper close to Kiev, Ukraine in July 1994. The LPAR meetings are held every year within the former Soviet Union and aimed toward bringing jointly researchers attracted to LP and AR.
This complaints includes the entire types of the 24 authorised papers evaluated by way of not less than 3 referees making sure a application of very best quality. The papers hide all proper elements of LP and AR starting from concept to implementation and application.
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Additional resources for Logic Programming and Automated Reasoning: 5th International Conference, LPAR '94 Kiev, Ukraine, July 16–22, 1994 Proceedings
P Because r ∈ ER holds, we can get ER⊆ R(X ). ＝ Transformation from OWL Description to Resource Space Model 21 And from R(X ) ⊆ ER, then we have: R(X ) = ER holds. I P From (1) and (2), we get R(X ) = R(X ). Then according to Theorem 2, RS satisfies 3NF. P P From Theorem 3, we know that if every axis in OWL-based resource space is created by a common property, then the resource space satisfies 3NF. Therefore the algorithm using this condition can generate a 3NF resource space. 6 Strategy and Discussion Integration of OWL files developed by team members is very important in ontology engineering.
4 Experiments In this section, we will introduce several experiments to prove the effectiveness of the proposed methods. First, we describe the experiment setup and evaluation of parent topic terms extraction. Second, child topic terms extraction and parent-child relationship are both closely examined. 1 Parent Topic Term Extraction All the experiments are based on 6,458 messages crawled from the websites in Table 1. The main topic of this dataset is about “computer virus”. Table 1. 1 Evaluation Measure We use precision at top N results to measure the performance: |C ∩ R| (13) P@ N = R where R is the set of top N topic terms extracted by our method, and C is the set of manually tagged correct topic terms.
In this section, we will discuss these steps in detail. 1 Parent Topic Term Extraction and Ranking The purpose of this step is to identify topic terms from an initial set of messages. First, we consider some particular features of terms in Chinese BBS messages. Second, we briefly introduce how to use regression models to calculate the score of each candidate term. Third, we present a co-occurrence algorithm to refine the results of topic term extraction. Finally, we will tackle with the problem of synonym.
Logic Programming and Automated Reasoning: 5th International Conference, LPAR '94 Kiev, Ukraine, July 16–22, 1994 Proceedings by Amy Felty, Douglas Howe (auth.), Frank Pfenning (eds.)