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On the Implementation of the Probabilistic Logic Programming Language ProbLog
The past few years have seen a surge of interest in the field of
probabilistic logic learning and statistical relational learning. In this
endeavor, many probabilistic logics have been developed. ProbLog is a recent
probabilistic extension of Prolog motivated by the mining of large biological
networks. In ProbLog, facts can be labeled with probabilities. These facts are
treated as mutually independent random variables that indicate whether these
facts belong to a randomly sampled program. Different kinds of queries can be
posed to ProbLog programs. We introduce algorithms that allow the efficient
execution of these queries, discuss their implementation on top of the
YAP-Prolog system, and evaluate their performance in the context of large
networks of biological entities.Comment: 28 pages; To appear in Theory and Practice of Logic Programming
(TPLP
CRL at Ntcir2
We have developed systems of two types for NTCIR2. One is an enhenced version
of the system we developed for NTCIR1 and IREX. It submitted retrieval results
for JJ and CC tasks. A variety of parameters were tried with the system. It
used such characteristics of newspapers as locational information in the CC
tasks. The system got good results for both of the tasks. The other system is a
portable system which avoids free parameters as much as possible. The system
submitted retrieval results for JJ, JE, EE, EJ, and CC tasks. The system
automatically determined the number of top documents and the weight of the
original query used in automatic-feedback retrieval. It also determined
relevant terms quite robustly. For EJ and JE tasks, it used document expansion
to augment the initial queries. It achieved good results, except on the CC
tasks.Comment: 11 pages. Computation and Language. This paper describes our results
of information retrieval in the NTCIR2 contes
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