3026036
Towards a Semantic Wiki-Based Japanese Biodictionary
This paper describes an on-going project to develop and maintain a web-based Japanese Biodictionary within a Semantic Wiki environment. For the development of the dictionary, MediaWiki is extended to enable the writing of labeled links that represent RDF triples. The extension enables Semantic Wiki to provide not only collaborative environment for experts in various biol- ogy fields to create and edit the dictionary, but also the navigation support to manage relations between terms. Using a simple wiki syntax, people can de- velop and maintain the dictionary visually and easily.
11284896
Semantic Wiki as a Lightweight Knowledge Management System
Since its birth in 1995, Wiki has become more and more popular This paper presents a Semantic Wiki, a Wiki extended to include the ideas of Semantic Web The proposed Semantic Wiki uses a simple Wiki syntax to write labeled links which represent RDF triples By enabling the writing of labeled links, Semantic Wiki may provide an easy-to-use and flexible environment for an integrated management of content and metadata, so that Semantic Wiki may be used as a lightweight knowledge management system.
12402506
Learning Decision Lists over Tree Patterns and Its Application
This paper introduces a new concept, a decision tree (or list) over tree patterns, which is a natural extension of a decision tree (or decision list), for dealing with tree structured objects. The learnability of this class is studied within the framework of the Probably Approximately Correct learning model and the Identi cation in the Limit model. It is found that the class k-node-DLRTP, a subclass of decision lists over regular tree patterns, is not polynomial time PAC learnable if NP6=RP, but that the class knode-DLRTP(m), a subclass of k-node-DLRTP where the number of variables in each tree pattern is bounded by m, is polynomial time PAC learnable. We also show that the class of decision lists over regular tree patterns is polynomial time inferable in the limit. Then we propose a more practical learning algorithm which employs heuristics based on an information theoretical evaluation function. This algorithm is applied to learning control strategies of rules in various domains, and its e ectiveness is shown.
5372253
On Approximately Identifying Concept Classes in the Limit
In this paper, we introduce various kinds of approximations of a concept and propose a framework of approximate learning in case that a target concept could be outside the hypothesis space. We present some characterization theorems for approximately identifiability. In particular, we show a remarkable result that the upper-best approximate identifiability from complete data is collapsed into the upper-best approximate identifiability from positive data. Further, some other characterizations for approximate identifiability from positive data are presented, where we establish a relationship between approximate identifiability and some important notions in quasi-order theory and topology theory. The results obtained in this paper are essentially related to the closure property of concept classes under infinite intersections (or infinite unions). We also show that there exist some interesting example concept classes with such properties (including specialized EFS's) by which an upper-best approximation of any concept can be identifiable in the limit from positive data.
30034887
Horn Clause Computation with DNA Molecules
In this paper, we will propose Horn clause computation as an underlying computational framework of DNA computer. Horn clause program is a subclass of the formulas of first order logic and has close relation to PROLOG language. The computational power of Horn clauses was discussed in (Tärnland, 1977) and it was shown that a finite set of Horn clauses is computationally equivalent to Turing Machine. Furthermore, it should be noted that Horn programs are adequate for representing nondeterministic computation. Thus, its parallel implementation with huge number of molecules might have possibility to overcome the computational power of conventional computers. It should also be noted that the clear logical semantics of Horn programs might enable us to accept it as a higher-level programming language of DNA computer. The aim of this paper is to propose an experimental method for implementing deduction with a subclass of Horn programs, called simple Horn programs. The computational power of this model is theoretically investigated and demonstrated with some applications to NP-complete problems.
16900268
On the Universality of Post and Splicing Systems
In search for a universal splicing system, in this paper we present a Post system universal for the class of Post systems, and we discuss its translation into an extended splicing system with multiplicity. We also discuss the complexity of the resulting universal splicing system, comparing our result with recent known results about the translation of universal Turing machines into splicing systems.
32641172
Learning Strategies Using Decision Lists
This paper presents a new technique for improving the efficiency of problem solvers by using trace data. The learning schema of this kind is usually called speedup learning. Although this learning paradigm has been formalized by [Nat89] based on the Probably Approximately Correct Learning model[Val84], his algorithm does not use operator sequence information of traces at all. We give a new algorithm which makes use of operator sequence information, and its effectiveness is shown by experiments in several domains. We also show theoretical backgrounds of the algorithm.