On the possible patterns of inputs for block sorting in the Burrows-Wheeler transformation

Block sorting in the Burrows-Wheeler transformation is to sort all of the n circular shifts of a string of length n lexicographically. We introduce a notion called the width of a sequence of n strings of length n and show that the values of widths are very different between the two types of sequences of strings; (1) a sequence of n randomly generated strings of length n, and (2) the sequence of n circular shifts of a randomly generated string of length n.     

Exploring synergies between content-based filtering and Spreading Activation techniques in knowledge-based recommender systems

Recommender systems fight information overload by selecting automatically items that match the personal preferences of each user. The so-called content-based recommenders suggest items similar to those the user liked in the past, using syntactic matching mechanisms. The rigid nature of such mechanisms leads to recommending only items that bear strong resemblance to those the user already knows. Traditional collaborative approaches face up to overspecialization by considering the preferences of other users, which causes other severe limitations. In this paper, we avoid the intrinsic pitfalls of collaborative solutions and diversify the recommendations by reasoning about the semantics of the user’s preferences. Specifically, we present a novel content-based recommendation strategy that resorts to semantic reasoning mechanisms adopted in the Semantic Web, such as Spreading Activation techniques and semantic associations. We have adopted these mechanisms to fulfill the personalization requirements of recommender systems, enabling to discover extra knowledge about the user’s preferences and leading to more accurate and diverse suggestions. Our approach is generic enough to be used in a wide variety of domains and recommender systems. The proposal has been preliminary evaluated by statistics-driven tests involving real users in the recommendation of Digital TV contents. The results reveal the users’ satisfaction regarding the accuracy and diversity of the reasoning-driven content-based recommendations.     

MoSuMo: A Semantic Web service to generate electrostatic potentials across solvent excluded protein surfaces and binding pockets

Investigations of molecular interactions can benefit from knowledge of the regions that correspond to binding sites and electrostatic potentials across molecular surfaces. In support of simulations aimed at a coarser level of granularity than molecular dynamics, we have developed MoSuMo, a SADI-based Semantic Web service for generating molecular surfaces from a set of 3D atomic coordinates. MoSuMo's service input requires a Protein Data Bank (PDB) identifier from which it obtains the PDB entry and for which it generates electrostatic potentials as vertex values across a geometric mesh that represents the molecular surface. Service inputs and outputs are specified as restrictions on classes from the Semanticscience Integrated Ontology (SIO), which is formalized using the Web Ontology Language (OWL). Binding sites containing their ligands are identified and described as sub-collections of surface vertices. Thus, MoSuMo provides a semantic web service for investigating molecular interactions including visualization.     

Semantic-based web service discovery and chaining for building an Arctic spatial data infrastructure

Increasing interests in a global environment and climate change have led to studies focused on the changes in the multinational Arctic region. To facilitate Arctic research, a spatial data infrastructure (SDI), where Arctic data, information, and services are shared and integrated in a seamless manner, particularly in light of today's climate change scenarios, is urgently needed. In this paper, we utilize the knowledge-based approach and the spatial web portal technology to prototype an Arctic SDI (ASDI) by proposing (1) a hybrid approach for efficient service discovery from distributed web catalogs and the dynamic Internet; (2) a domain knowledge base to model the latent semantic relationships among scientific data and services; and (3) an intelligent logic reasoning mechanism for (semi-)automatic service selection and chaining. A study of the influence of solid water dynamics to the bio-habitat of the Arctic region is used as an example to demonstrate the prototype.     

Double-layered schema integration of heterogeneous XML sources

Schema integration aims to create a mediated schema as a unified representation of existing heterogeneous sources sharing a common application domain. These sources have been increasingly written in XML due to its versatility and expressive power. Unfortunately, these sources often use different elements and structures to express the same concepts and relations, thus causing substantial semantic and structural conflicts. Such a challenge impedes the creation of high-quality mediated schemas and has not been adequately addressed by existing integration methods. In this paper, we propose a novel method, named XINTOR, for automating the integration of heterogeneous schemas. Given a set of XML sources and a set of correspondences between the source schemas, our method aims to create a complete and minimal mediated schema: it completely captures all of the concepts and relations in the sources without duplication, provided that the concepts do not overlap. Our contributions are fourfold. First, we resolve structural conflicts inherent in the source schemas. Second, we introduce a new statistics-based measure, called path cohesion, for selecting concepts and relations to be a part of the mediated schema. The path cohesion is statistically computed based on multiple path quality dimensions such as average path length and path frequency. Third, we resolve semantic conflicts by augmenting the semantics of similar concepts with context-dependent information. Finally, we propose a novel double-layered mediated schema to retain a wider range of concepts and relations than existing mediated schemas, which are at best either complete or minimal, but not both. Performed on both real and synthetic datasets, our experimental results show that XINTOR outperforms existing methods with respect to (i) the mediated-schema quality using precision, recall, F-measure, and schema minimality; and (ii) the execution performance based on execution time and scale-up performance.     

Fast text categorization using concise semantic analysis

Text representation is a necessary procedure for text categorization tasks. Currently, bag of words (BOW) is the most widely used text representation method but it suffers from two drawbacks. First, the quantity of words is huge; second, it is not feasible to calculate the relationship between words. Semantic analysis (SA) techniques help BOW overcome these two drawbacks by interpreting words and documents in a space of concepts. However, existing SA techniques are not designed for text categorization and often incur huge computing cost. This paper proposes a concise semantic analysis (CSA) technique for text categorization tasks. CSA extracts a few concepts from category labels and then implements concise interpretation on words and documents. These concepts are small in quantity and great in generality and tightly related to the category labels. Therefore, CSA preserves necessary information for classifiers with very low computing cost. To evaluate CSA, experiments on three data sets (Reuters-21578, 20-NewsGroup and Tancorp) were conducted and the results show that CSA reaches a comparable micro- and macro-F₁ performance with BOW, if not better one. Experiments also show that CSA helps dimension sensitive learning algorithms such as k-nearest neighbor (kNN) to eliminate the “Curse of Dimensionality” and as a result reaches a comparable performance with support vector machine (SVM) in text categorization applications. In addition, CSA is language independent and performs equally well both in Chinese and English.     

自动文摘中的冗余句消除方法

针对自动文摘的信息冗余问题,提出了一种冗余语句消除的方法。利用《同义词词林》来定义词语语义距离计算公式,根据词语的相似度,建立主题词和主题句之间的一一对应关系,借用编码理论中海明距离的理论,得到了文摘中主题句的相似度,设置阈值过滤掉相似度较高的主题句,从而实现了主题句的约简。实验结果证明,该方法提高了文摘的精度。     

一种语义嫡的社区划分模型及其应用研究

根据复杂网络中整个网络由若干个社区组成和用户通常只对少数主题感兴趣的事实,通过社区语义墒和社区间语义间嫡,提出了一种基于语义信息的社区结构划分模型,将网络划分为几个语义社区,并将其应用在服务注册中心的具体问题中,同时通过社区负载容量等参数进行了实验分析。实验结果表明,该模型充分考虑到了社区间的语义特性,在应用中效率有显著提高,为语义社区结构中的服务注册中心部署提供了新的途径。     

基于编译原理的四则混合运算器的实现

计算机只能执行机器代码,只有专业程序人员才能较好地使用此类语言。因此编译程序已成为计算机系统的最重要的系统程序之一。本文主要通过对四则混合计算器的软件的设计分析讲述了编译程序的工作过程及具体实现。     

K(Knowledge)-net: building up and its dynamics

The essence of intelligence is to use certain abilities to obtain knowledge, to use that knowledge, and to operate with that knowledge. New knowledge learned by a human is often related to old existing knowledge, and sometimes we could have more conceptual knowledge based on old knowledge. So, the knowledge in the brain exists in a related structural form, and this structure is dynamic, and therefore is evolvable. Based on the understanding of the real process of learning by a human being, we discuss how to make a model to describe the dynamic structure of knowledge. This model is also a principle of artificial brain design. Most of the knowledge a child learns is from natural language and perception information, and we define this as semantic knowledge. The model to describe the process and structure of knowledge growing in a network form is called a K-net. It is a dynamic network with two main dynamics: one is new knowledge added, and the other is aggregating knowledge existing in the network with some probability. Under these very natural conditions, we found that the network is originally a simple random net, and then some characteristics of a complex network gradually appear when more new knowledge is added and aggregated. A more interesting phenomenon is the appearance of a random hierarchical structure. Does this mean emergence?