SINA: Semantic interpretation of user queries for question answering on interlinked data

The architectural choices underlying Linked Data have led to a compendium of data sources which contain both duplicated and fragmented information on a large number of domains. One way to enable non-experts users to access this data compendium is to provide keyword search frameworks that can capitalize on the inherent characteristics of Linked Data. Developing such systems is challenging for three main reasons. First, resources across different datasets or even within the same dataset can be homonyms. Second, different datasets employ heterogeneous schemas and each one may only contain a part of the answer for a certain user query. Finally, constructing a federated formal query from keywords across different datasets requires exploiting links between the different datasets on both the schema and instance levels. We present Sina, a scalable keyword search system that can answer user queries by transforming user-supplied keywords or natural-languages queries into conjunctive SPARQL queries over a set of interlinked data sources. Sina uses a hidden Markov model to determine the most suitable resources for a user-supplied query from different datasets. Moreover, our framework is able to construct federated queries by using the disambiguated resources and leveraging the link structure underlying the datasets to query. We evaluate Sina over three different datasets. We can answer 25 queries from the QALD-1 correctly. Moreover, we perform as well as the best question answering system from the QALD-3 competition by answering 32 questions correctly while also being able to answer queries on distributed sources. We study the runtime of SINA in its mono-core and parallel implementations and draw preliminary conclusions on the scalability of keyword search on Linked Data.     

DL-Learner—A framework for inductive learning on the Semantic Web

In this system paper, we describe the DL-Learner framework, which supports supervised machine learning using OWL and RDF for background knowledge representation. It can be beneficial in various data and schema analysis tasks with applications in different standard machine learning scenarios, e.g. in the life sciences, as well as Semantic Web specific applications such as ontology learning and enrichment. Since its creation in 2007, it has become the main OWL and RDF-based software framework for supervised structured machine learning and includes several algorithm implementations, usage examples and has applications building on top of the framework. The article gives an overview of the framework with a focus on algorithms and use cases.     

Guided curation of semistructured data in collaboratively-built knowledge bases

The collaborative curation of semistructured knowledge has become a popular paradigm on the web and also within enterprises. In such knowledge bases a common structure of the stored information is crucial for providing efficient and precise search facilities. However, the task of refining, extending and homogenizing knowledge and its structure is very complex. In this article we present two paradigms for the simplification of this task by providing guidance mechanisms to the user. Both paradigms aim at combining the power of automated extraction algorithms with the semantic awareness of human users to accomplish this refinement task.     

Enabling machine understandable exchange of energy consumption information in intelligent domotic environments

In the 21st century, all the major countries around the world are coming together to reduce the impact of energy generation and consumption on the global environment. Energy conservation and its efficient usage has become a top agenda on the desks of many governments. In the last decade, the drive to make homes automated and to deliver a better assisted living picked pace and the research into home automation systems accelerated, usually based on a centralized residential gateway. However most devised solutions fail to provide users with information about power consumption of different house appliances. The ability to collect power consumption information can lead us to have a more energy efficient society. The goal addressed in this paper is to enable residential gateways to provide the energy consumption information, in a machine understandable format, to support third party applications and services. To reach this goal, we propose a Semantic Energy Information Publishing Framework. The proposed framework publishes, for different appliances in the house, their power consumption information and other properties, in a machine understandable format. Appliance properties are exposed according to the existing semantic modeling supported by residential gateways, while instantaneous power consumption is modeled through a new modular Energy Profile ontology.     

语义网的体系结构与服务功能

近年来,语义网的研究引起了计算机业界的广泛关注。本文作者认为对语义网的研究成果将对现今互联网上的信息发布,信息处理以及信息服务产生巨大的变革。基于此原因,本文对语义网产生的背景、基本概念、体系结构进行介绍与分析,最后讨论了语义网能够提供给我们全新的网络服务。     

垃圾衍生燃料技术研究进展

文中综述了垃圾衍生燃料(简称RDF)分类、应用、制备及研究进展。垃圾衍生燃料按照美国材料与实验协会(ASTM)的分类标准可分为7类;主要应用于供暖供热及干燥、发电工程、水泥行业;制备方法包括散装RDF制备工艺、干燥挤压成型RDF制备工艺和化学处理的RDF制备工艺;研究主要方向有热解特性和燃烧特性研究等。     

三种可燃固体废弃物及煤粉的热重分析

采用热重分析法对三种可燃固体废弃物如废轮胎、废木料、废织物及煤粉的燃烧失重特性进行对比分析,研究它们的失重规律、着火特性和燃尽特性。结果表明：三种可燃固体废弃物的失重及燃烧特性与煤粉有较大的区别,煤粉挥发物少,燃烧失重主要受固定碳燃烧控制,可燃固体废弃物挥发份较多,燃烧失重主要受挥发份的燃烧控制;可燃固体废弃物的渣量明显低于煤粉渣量。这些可燃固体废弃物喷人高炉中,含氢量高,有利于改善高炉冶炼的经济技术指标和减少CO2排放。     

基于FOAF和RDQL的电子商务合作伙伴查询方法

为了在整个互联网内实现基于语义的搜索,提出利用Jena框架进行多条件的复杂检索,实现对合作伙伴的数据进行搜索.使用语义Web框架中的RDF和RDFS作为描述数据,对基于RDFS的FOAF词汇进行扩充,使得对供应商的描述具有语义.进而使用RFQL查询语言对合作伙伴的数据进行搜素,提出利用支持RDQL的Jena框架进行多条件的复杂检索.提出了基于语义Web的合作伙伴查询的实施框架,实现在整个互联网内进行搜索.本研究对下一代互联网的应用进行了探索性的研究.     

煤与垃圾衍生燃料的流化床混烧试验研究

使用鼓泡流化床燃烧炉进行了煤与垃圾衍生燃料(RDF)的混烧实验研究,分析了燃烧尾气中O2、CO、CO2、H2O、NOx、CH4、SO2、HCl、HCN浓度的变化,并对各成分之间的浓度进行了相关性分析。研究结果表明在750~900℃的范围内,高温利于SO2的脱除;NO的浓度随床温的提高而增加,NO2的浓度与床温的关系不大;HCl的浓度随床温的升高而增加,说明高温不利于HCl的脱除;在空气量较低的情况下,CO、CH4、N2O、HCN等气体的浓度较高,相关分析表明,这些气体浓度之间有较强的正相关关系,说明这些气体适宜于还原气氛下生成;随着HCl气体浓度的增加,SO2浓度降低,说明HCl的存在可促进石灰石脱除SO2的反应,同时还证实煤与RDF混烧可使尾气中的SO2和HCl气体浓度均有所下降。