基于RDF/XML的航空发动机设计知识表达与获取

针对航空发动机设计过程中的显性知识特点,结合元数据（Metadata）与都柏林核心（Dublin Core）方法,进行面向航空发动机设计知识资源描述的“元素集”定义;利用RDF／XML技术建立设计知识资源描述的XML文档,从而实现航空发动机设计知识的表达、获取与管理．     

An MLP-based representation of neural tensor networks for the RDF data models

In this paper, a new representation of neural tensor networks is presented. Recently, state-of-the-art neural tensor networks have been introduced to complete RDF knowledge bases. However, mathematical model representation of these networks is still a challenging problem, due to tensor parameters. To solve this problem, it is proposed that these networks can be represented as two-layer perceptron network. To complete the network topology, the traditional gradient based learning rule is then developed. It should be mentioned that for tensor networks there have been developed some learning rules which are complex in nature due to the complexity of the objective function used. Indeed, this paper is aimed to show that the tensor network can be viewed and represented by the two-layer feedforward neural network in its traditional form. The simulation results presented in the paper easily verify this claim.

     

一种利用RDF(S)建立本体论的方法

首先介绍了如何使用RDF(S)表示本体论 (Ontology)中的对象模型 ,然后分析了如何在RDF(S)中表示本体论中的公理     

Binary feature representation learning for scene retrieval in micro-video

Multimedia Tools and Applications - Micro-video is popular as new social media, and scene retrieval is a useful application in micro-video. At present, few researches focus on scene retrieval in...     

Discrete representation strategies for foreign exchange prediction

This is an extended version of the paper presented at the 4th International Workshop NFMCP 2015 held in conjunction with ECML PKDD 2015. The initial version has been published in NFMCP 2015 conference proceedings as part of Springer Series. This paper presents a novel approach to financial times series (FTS) prediction by mapping hourly foreign exchange data to string representations and deriving simple trading strategies from them. To measure the degree of similarity in these market strings we apply familiar string kernels, bag of words and n-grams, whilst also introducing a new kernel, time-decay n-grams, that captures the temporal nature of FTS. In the process we propose a sequential Parzen windows algorithm based on discrete representations where trading decisions for each string are learned in an online manner and are thus subject to temporal fluctuations. We evaluate the strength of a number of representations using both the string version and its continuous counterpart, whilst also comparing the performance of different learning algorithms on these representations, namely support vector machines, Parzen windows and Fisher discriminant analysis. Our extensive experiments show that the simple string representation coupled with the sequential Parzen windows approach is capable of outperforming other more exotic approaches, supporting the idea that when it comes to working in high noise environments often the simplest approach is the most effective.     

Extending SPARQL with regular expression patterns (for querying RDF)

RDF is a knowledge representation language dedicated to the annotation of resources within the framework of the semantic web. Among the query languages for RDF, SPARQL allows querying RDF through graph patterns, i.e., RDF graphs involving variables. Other languages, inspired by the work in databases, use regular expressions for searching paths in RDF graphs. Each approach can express queries that are out of reach of the other one. Hence, we aim at combining these two approaches. For that purpose, we define a language, called PRDF (for “Path RDF”) which extends RDF such that the arcs of a graph can be labeled by regular expression patterns. We provide PRDF with a semantics extending that of RDF, and propose a correct and complete algorithm which, by computing a particular graph homomorphism, decides the consequence between an RDF graph and a PRDF graph. We then define the PSPARQL query language, extending SPARQL with PRDF graph patterns and complying with RDF model theoretic semantics. PRDF thus offers both graph patterns and path expressions. We show that this extension does not increase the computational complexity of SPARQL and, based on the proposed algorithm, we have implemented a correct and complete PSPARQL query engine.     

RDF（S）三元组的推理控制算法

为实现资源描述框架（RDF）数据的访问控制,提出一种RDF（S）三元组的推理控制算法,通过计算推理依赖图得到三元组的逻辑表达式,对敏感三元组的逻辑表达式求析取范式进而得到推理控制问题的候选解和最优解。实验表明,该算法能够有效地阻止非法推理,合理控制语义信息的丢失。     

对象关系数据库到RDF(S)的映射方法

随着智能信息技术的发展,知识图谱已被广泛应用于智能搜索等各个领域.知识图谱中的信息一般采取RDF(S)的数据模型来表示.知识图谱的构建需要从大量的数据源抽取信息,而数据库是不可忽视的重要数据源.近几年,对象关系数据库得到了广泛的应用,且其中存储着丰富的语义信息,而基于对象关系数据库自动构建RDF(S)的研究却较少.因此...     

基于分布RDF(S)模型的信息查询与集成

在Web应用环境中,可以通过RDF(S)形式描述企业领域内分布信息资源的语义,以提高信息查询的准确性.提出了描述分布异构RDF(S)的分布RDF(S)模型,并基于这一模型给出了实现分布RDF(S)查询的方法,此查询方法既能实现实例层次的查询,也能实现概念层次的查询.基于这一方法,用户能够以统一的形式来查询,获取相关的信息资源,同时还可以实现分布RDF(S)的集成.     

Drawing (Complete) Binary Tanglegrams

A binary tanglegram is a drawing of a pair of rooted binary trees whose leaf sets are in one-to-one correspondence; matching leaves are connected by inter-tree edges. For applications, for example, in phylogenetics, it is essential that both trees are drawn without edge crossings and that the inter-tree edges have as few crossings as possible. It is known that finding a tanglegram with the minimum number of crossings is NP-hard and that the problem is fixed-parameter tractable with respect to that number. We prove that under the Unique Games Conjecture there is no constant-factor approximation for binary trees. We show that the problem is NP-hard even if both trees are complete binary trees. For this case we give an O(n ³)-time 2-approximation and a new, simple fixed-parameter algorithm. We show that the maximization version of the dual problem for binary trees can be reduced to a version of MaxCut for which the algorithm of Goemans and Williamson yields a 0.878-approximation.     

RDF(S)模型到关系数据库模式的转换

针对资源描述框架和资源描述框架模式(RDF(S))的数据管理问题,提出一种RDF(S)模型到关系数据库模式的转换方法。在对RDF(S)模型和关系数据库模式进行形式化定义的基础上,给出RDF(S)模型到关系数据库模式的转换规则和算法,设计并实现相应的转换系统,通过实验验证该转换方法的可行性和有效性。     

利用本体和主题词表的集成构造RDF模式

为了减少语义异构性带来的信息发现、集成和存取的困难,论述了语义元数据构造,提出了通过集成现存的本体和主题词表构造元数据模式的一种新方法,即元数据模式构造的两步方法:在主题词T和本体O之间的连接关系规范;概念主题词表的自动构造.这个集成基于主题词术语和本体概念之间的蕴含关系规范,并产生具体应用的元数据模式,同时也给出利用结果元数据模式构造RDF模式的过程.     

利用本体和主题词表的集成构造RDF模式

为了减少语义异构性带来的信息发现、集成和存取的困难,论述了语义元数据构造,提出了通过集成现存的本体和主题词表构造元数据模式的一种新方法,即元数据模式构造的两步方法:在主题词T和本体O之间的连接关系规范;概念主题词表的自动构造。这个集成基于主题词术语和本体概念之间的蕴含关系规范,并产生具体应用的元数据模式,同时也给出利用结果元数据模式构造RDF模式的过程。     

A representation theorem for (q-)holonomic sequences

Chomsky and Schützenberger showed in 1963 that the sequence _dL(n)$d_L(n)$, which counts the number of words of a given length n in a regular language L, satisfies a linear recurrence relation with constant coefficients for n  , i.e., it is C-finite. It follows that every sequence s(n)$s(n)$ which satisfies a linear recurrence relation with constant coefficients can be represented as _dL1(n)−_dL2(n)$d_{L_1}(n)-d_{L_2}(n)$ for two regular languages. We view this as a representation theorem for C-finite sequences. Holonomic or P-recursive sequences are sequences which satisfy a linear recurrence relation with polynomial coefficients. q-Holonomic sequences are the q-analog of holonomic sequences. In this paper we prove representation theorems of holonomic and q-holonomic sequences based on position specific weights on words, and for holonomic sequences, without using weights, based on sparse regular languages.     

Atlas: Storing, updating and querying RDF(S) data on top of DHTs

The RDF(S) data model has been proposed for encoding metadata about Web resources. As more and more Web resources are annotated using RDF(S), there is an urgent need for efficiently dealing with this large volume of data. In this paper, we present Atlas, a peer-to-peer system for storing, updating and querying RDF(S) data. The Atlas system has been built using the distributed hash table Bamboo. Atlas was developed in the context of project OntoGrid, where it was used as a distributed repository for RDF(S) metadata describing Grid services and resources. The development of Atlas continues in other projects that our group participates currently. This paper gives an overview of the most recent version of Atlas and discusses a representative application.     

Materialisation and data partitioning algorithms for distributed RDF systems

Many RDF systems support reasoning with Datalog rules via materialisation, where all conclusions of RDF data and the rules are precomputed and explicitly stored in a preprocessing step. As the amount of RDF data used in applications keeps increasing, processing large datasets often requires distributing the data in a cluster of shared-nothing servers. While numerous distributed query answering techniques are known, distributed materialisation is less well understood. In this paper, we present several techniques that facilitate scalable materialisation in distributed RDF systems. First, we present a new distributed materialisation algorithm that aims to minimise communication and synchronisation in the cluster. Second, we present two new algorithms for partitioning RDF data, both of which aim to produce tightly connected partitions, but without loading complete datasets into memory. We evaluate our materialisation algorithm against two state-of-the-art distributed Datalog systems and show that our technique offers competitive performance, particularly when the rules are complex. Moreover, we analyse in depth the effects of data partitioning on reasoning performance and show that our techniques offer performance comparable or superior to the state of the art min-cut partitioning, but computing the partitions requires considerably less time and memory.