Learning probabilistic logic programming languages is receiving an increasing attention, and systems are available for learning the parameters (PRISM, LeProbLog, LFI-ProbLog and EMBLEM) or both structure and parameters (SEM-CP-logic and SLIPCASE) of these languages. In this paper we present the algorithm SLIPCOVER for “Structure LearnIng of Probabilistic logic programs by searChing OVER the clause space.” It performs a beam search in the space of probabilistic clauses and a greedy search in the space of theories using the log likelihood of the data as the guiding heuristics. To estimate the log likelihood, SLIPCOVER performs Expectation Maximization with EMBLEM. The algorithm has been tested on five real world datasets and compared with SLIPCASE, SEM-CP-logic, Aleph and two algorithms for learning Markov Logic Networks (Learning using Structural Motifs (LSM) and ALEPH++ExactL1). SLIPCOVER achieves higher areas under the precision-recall and receiver operating characteristic curves in most cases.
salgorithm is an evolution'' of SLIPCASE @cite_36 in terms of search strategy. SLIPCASE is based on a simple search strategy that refines LPAD theories by trying all possible theory revisions. SLIPCOVER instead uses bottom clauses to guide the refinement process, thus reducing the number of revisions and exploring more effectively the search space. Moreover, SLIPCOVER separates the search for promising clauses from that of the theory. By means of these modifications we have been able to get better final theories in terms of LL with respect to SLIPCASE, as shown in Section . In the following we highlight in detail the differences of the two algorithms.
@cite_36
2610389136 2110693578 2162152259 2108498102
Ontology evolution is an important problem in the Semantic Web research. Recently, Alchourron, Gardenfors and Markinson's (AGM) theory on belief change has been applied to deal with this problem. However, most of current work only focuses on the feasibility of the application of AGM postulates on contraction to description logics (DLs), a family of ontology languages. So the explicit construction of a revision operator is ignored. In this paper, we first generalize the AGM postulates on revision to DLs. We then define two revision operators in DLs. One is the weakening-based revision operator which is defined by weakening of statements in a DL knowledge base and the other is its refinement. We show that both operators capture some notions of minimal change and satisfy the generalized AGM postulates for revision. We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization (TS). Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We consider a new approach, LexRank, for computing sentence importance based on the concept of eigenvector centrality in a graph representation of sentences. In this model, a connectivity matrix based on intra-sentence cosine similarity is used as the adjacency matrix of the graph representation of sentences. Our system, based on LexRank ranked in first place in more than one task in the recent DUC 2004 evaluation. In this paper we present a detailed analysis of our approach and apply it to a larger data set including data from earlier DUC evaluations. We discuss several methods to compute centrality using the similarity graph. The results show that degree-based methods (including LexRank) outperform both centroid-based methods and other systems participating in DUC in most of the cases. Furthermore, the LexRank with threshold method outperforms the other degree-based techniques including continuous LexRank. We also show that our approach is quite insensitive to the noise in the data that may result from an imperfect topical clustering of documents. Knowledge acquisition is a difficult, error-prone, and time-consuming task. The task of automatically improving an existing knowledge base using learning methods is addressed by the class of systems performing theory refinement. This paper presents a system, forte (First-Order Revision of Theories from Examples), which refines first-order Horn-clause theories by integrating a variety of different revision techniques into a coherent whole. FORTE uses these techniques within a hill-climbing framework, guided by a global heuristic. It identifies possible errors in the theory and calls on a library of operators to develop possible revisions. The best revision is implemented, and the process repeats until no further revisions are possible. Operators are drawn from a variety of sources, including prepositional theory refinement, first-order induction, and inverse resolution. FORTE is demonstrated in several domains, including logic programming and qualitative modelling. Information-hiding in natural language text has mainly consisted of carrying out approximately meaning-preserving modifications on the given cover text until it encodes the intended mark. A major technique for doing so has been synonym-substitution. In these previous schemes, synonym substitutions were done until the text "confessed", i.e., carried the intended mark message. We propose here a better way to use synonym substitution, one that is no longer entirely guided by the mark-insertion process: It is also guided by a resilience requirement, subject to a maximum allowed distortion constraint. Previous schemes for information hiding in natural language text did not use numeric quantification of the distortions introduced by transformations, they mainly used heuristic measures of quality based on conformity to a language model (and not in reference to the original cover text). When there are many alternatives to carry out a substitution on a word, we prioritize these alternatives according to a quantitative resilience criterion and use them in that order. In a nutshell, we favor the more ambiguous alternatives. In fact not only do we attempt to achieve the maximum ambiguity, but we want to simultaneously be as close as possible to the above-mentioned distortion limit, as that prevents the adversary from doing further transformations without exceeding the damage threshold; that is, we continue to modify the document even after the text has "confessed" to the mark, for the dual purpose of maximizing ambiguity while deliberately getting as close as possible to the distortion limit. The quantification we use makes possible an application of the existing information-theoretic framework, to the natural language domain, which has unique challenges not present in the image or audio domains. The resilience stems from both (i) the fact that the adversary does not know where the changes were made, and (ii) the fact that automated disambiguation is a major difficulty faced by any natural language processing system (what is bad news for the natural language processing area, is good news for our scheme's resilience). In addition to the above mentioned design and analysis, another contribution of this paper is the description of the implementation of the scheme and of the experimental data obtained.