Inferring additional knowledge from QTCN relations

It is widely held that people tend to use qualitative rather than quantitative phrases when raising or answering questions about moving objects. Queries about whether an object is moving towards or away from another object or whether objects are getting closer to each other or further away from each other, require qualitative responses. This characteristic should be reflected in a calculus to be used to describe and reason about continuously moving objects. In this paper, we present a qualitative trajectory calculus of relations between two disjoint moving objects, whose movement is constrained by a network. The proposed calculus (QTC_N) is formally introduced and illustrated. Particular attention is placed on how to infer additional knowledge from QTC_N relations by means of composition tables and the transformation of QTC_N relations into relations defined by the Relative Trajectory Calculus on Networks (RTC_N).     

Extracting focused knowledge from the semantic web

Ontologies are increasingly being recognized as a critical component in making networked knowledge accessible. Software architectures which can assemble knowledge from networked sources coherently according to the requirements of a particular task or perspective will be at a premium in the next generation of web services. We argue that the ability to generate task-relevant ontologies efficiently and relate them to web resources will be essential for creating a machine-inferencable “semantic web”. The Internet-based multi-agent problem solving (IMPS) architecture described here is designed to facilitate the retrieval, restructuring, integration and formalization of task-relevant ontological knowledge from the web. There are rich structured and semi-structured sources of knowledge available on the web that present implicit or explicit ontologies of domains. Knowledge-level models of tasks have an important role to play in extracting and structuring useful focused problem-solving knowledge from these web sources. IMPS uses a multi-agent architecture to combine these models with a selection of web knowledge extraction heuristics to provide clean syntactic integration of ontological knowledge from diverse sources and support a range of ontology merging operations at the semantic level. Whilst our specific aim is to enable on-line knowledge acquisition from web sources to support knowledge-based problem solving by a community of software agents encapsulating problem-sloving inferences, the techniques described here can be applied to more general task-based integration of knowledge from diverse web sources, and the provision of services such as the critical comparison, fusion, maintenance and update of both formal informal ontologies.     

Detecting semantic violations of lock-free data structures through C++ contracts

The Journal of Supercomputing - The use of synchronization mechanisms in multithreaded applications is essential on shared-memory multi-core architectures. However, debugging parallel applications...     

Identifying incompleteness in privacy policy goals using semantic frames

Requirements Engineering - Companies that collect personal information online often maintain privacy policies that are required to accurately reflect their data practices and privacy goals. To be...     

Extracting knowledge from XML document repository: a semantic Web-based approach

XML plays an important role as the standard language for representing structured data for the traditional Web, and hence many Web-based knowledge management repositories store data and documents in XML. If semantics about the data are formally represented in an ontology, then it is possible to extract knowledge: This is done as ontology definitions and axioms are applied to XML data to automatically infer knowledge that is not explicitly represented in the repository. Ontologies also play a central role in realizing the burgeoning vision of the semantic Web, wherein data will be more sharable because their semantics will be represented in Web-accessible ontologies. In this paper, we demonstrate how an ontology can be used to extract knowledge from an exemplar XML repository of Shakespeare’s plays. We then implement an architecture for this ontology using de facto languages of the semantic Web including OWL and RuleML, thus preparing the ontology for use in data sharing. It has been predicted that the early adopters of the semantic Web will develop ontologies that leverage XML, provide intra-organizational value such as knowledge extraction capabilities that are irrespective of the semantic Web, and have the potential for inter-organizational data sharing over the semantic Web. The contribution of our proof-of-concept application, KROX, is that it serves as a blueprint for other ontology developers who believe that the growth of the semantic Web will unfold in this manner.

Partially constructed knowledge for semantic query

Among the developments of information technology, the most popular tools nowadays for seeking the knowledge are the Google or Yahoo keywords-based search engines on the Internet. Users can easily obtain the information they need, but they still have to read and organize those documents by themselves. Due to that reason, users have to spend most of time in browsing and skipping the documents they have searched. In order to facilitate this process, this paper proposes a query-based ontology knowledge acquisition system which dynamically constructs query-based partial ontology to provide proficient answers for users’ queries. To construct the relationships and hierarchy of concepts in such an ontology, the formal concept analysis approach is adopted. After the ontology is built, the system can deduct the specific answer according to the relationships and hierarchy of ontology without asking users to read the whole document sets. We collected three kinds of sports news pages as source documents including those regarding NBA, CPBL and MLB to evaluate the precision of the system function in the experiment, which, as a result, reveals that the proposed approach indeed can work effectively.     

Goals in conflict: semantic foundations of goals in agent programming

This paper addresses the notion of (declarative) goals as used in agent programming. Goals describe desirable states, and semantics of these goals in an agent programming context can be defined in various ways. We focus in this paper on the representation of conflicting goals. In particular, we define two semantics for goals, one for unconditional goals and one for conditional goals. The first is based on propositional logic, and the latter is based on default logic. We establish relations between and properties of these semantics. This title was inspired by the title of the PhD thesis of Harrenstein: Logic in conflict: logical explorations in strategic equilibrium [25].     

Automatically learning semantic knowledge about multiword predicates

Highly frequent and highly polysemous verbs, such as give, take, and make, pose a challenge to automatic lexical acquisition methods. These verbs widely participate in multiword predicates (such as light verb constructions, or LVCs), in which they contribute a broad range of figurative meanings that must be recognized. Here we focus on two properties that are key to the computational treatment of LVCs. First, we consider the degree of figurativeness of the semantic contribution of such a verb to the various LVCs it participates in. Second, we explore the patterns of acceptability of LVCs, and their productivity over semantically related combinations. To assess these properties, we develop statistical measures of figurativeness and acceptability that draw on linguistic properties of LVCs. We demonstrate that these corpus-based measures correlate well with human judgments of the relevant property. We also use the acceptability measure to estimate the degree to which a semantic class of nouns can productively form LVCs with a given verb. The linguistically-motivated measures outperform a standard measure for capturing the strength of collocation of these multiword expressions.

Transformation of semantic knowledge into simulation-based decision support

Simulation is capable to cope with the uncertain and dynamic nature of industrial value chains. However, in-depth system expertise is inevitable for mapping objects and constraints from the real world to a virtual model. This knowledge-intensity leads to long development times of respective projects, which contradicts the need for timely decision support. Since more and more companies use industrial knowledge graphs and ontologies to foster their knowledge management, this paper proposes a framework on how to efficiently derive a simulation model from such semantic knowledge bases. As part of the approach, a novel Simulation Ontology provides a standardized meta-model for hybrid simulations. Its instantiation enables the user to come up with a fully parameterized formal simulation model. Newly developed Mapping Rules facilitate this process by providing guidance on how to turn knowledge from existing ontologies, which describe the system to be simulated, into instances of the Simulation Ontology. The framework is completed by a parsing procedure for an automated transformation of this conceptual model into an executable one. This novel modeling approach makes model development more efficient by reducing its complexity. It is validated in a use case implementation from semiconductor manufacturing, where cross-domain knowledge was required in order to model and simulate the impacts of the COVID-19 pandemic on a global supply chain network.     

基于语义建图的室内机器人实时场景分类

针对室内环境下的机器人场景识别问题,重点研究了场景分类策略的自主性、实时性和准确性,提出了一种语义建图方法.映射深度信息构建二维栅格地图,自主规划场景识别路径;基于卷积网络建立场景分类模型,实时识别脱离特定训练;利用贝叶斯框架融合先验知识,修正了错误分类并完成语义建图.实验结果表明:机器人能够进行全局自主探索,实时判断场景类别,并创建满足要求的语义地图.同时,实际路径规划中,机器人可以根据语义信息改善导航行为,验证了方法的可行性.     

Live semantic data from building digital twins for robot navigation: Overview of data transfer methods

Increasing reliance on automation and robotization presents great opportunities to improve the management of construction sites as well as existing buildings. Crucial in the use of robots in a built environment is their capacity to locate themselves and navigate as autonomously as possible. Robots often rely on planar and 3D laser scanners for that purpose, and building information models (BIM) are seldom used, for a number of reasons, namely their unreliability, unavailability, and mismatch with localization algorithms used in robots. However, while BIM models are becoming increasingly reliable and more commonly available in more standard data formats (JSON, XML, RDF), they become more promising and reliable resources for localization and indoor navigation, in particular in the more static types of existing infrastructure (existing buildings). In this article, we specifically investigate to what extent and how such building data can be used for such robot navigation. Data flows are built from BIM model to local repository and further to the robot, making use of graph data models (RDF) and JSON data formats. The local repository can hereby be considered to be a digital twin of the real-world building. Navigation on the basis of a BIM model is tested in a real world environment (university building) using a standard robot navigation technology stack. We conclude that it is possible to rely on BIM data and we outline different data flows from BIM model to digital twin and to robot. Future work can focus on (1) making building data models more reliable and standard (modelling guidelines and robot world model), (2) improving the ways in which building features in the digital building model can be recognized in 3D point clouds observed by the robots, and (3) investigating possibilities to update the BIM model based on robot feedback.     

基于多知识源的语义搭配知识库的构建及应用

针对汉语文本语义搭配错误,在对《现代汉语语义信息词典》(以下简称《语义词典》)及《知网》相关属性的研究基础上,提出了基于多知识源的语义搭配知识库的构建方法,并设计出了面向汉语文本语义查错的三层语义搭配知识库结构.在此基础上,设计并实现了一个汉语文本自动查错算法.实验结果表明,基于上述方法所设计的语义搭配知识库,可以大大提高汉语文本语义查错算法错误查找的召回率,对于语义查错的研究具有一定的借鉴作用.     

Automatic knowledge acquisition and maintenance for semantic queryoptimization

The authors present an approach to acquiring knowledge from previously processed queries. By using newly acquired knowledge together with given semantic knowledge, it is possible to make the query processor and/or optimizer more intelligent so that future queries can b processed more efficiently. The acquired knowledge is in the form of constraints. While some constraints are to be enforced for all database states, others are known to be valid for the current state of the database. The former constraints are statistic integrity constraints, while the latter are called dynamic integrity constraints. Some situations in which certain dynamic semantic constraints can be automatically extracted are identified. This automatic tool for knowledge acquisition can also be used as an interactive tool for identifying potential static integrity constraints. The concept of minimal knowledge base is introduced, and a method to maintain the knowledge base is presented. An algorithm to compute the restriction (selection) closure, i.e. all deductible restrictions, from a given set of restrictions, join predicates (as given in a query), and constraints is given     

知识库系统中语义网知识的表示

基于对知识库系统中知识表示的理解,提出了在语义网环境下知识库系统的建立和相应的知识表示,并将此扩展到语义网中整个的知识表示系统。     

Geographic knowledge extraction and semantic similarity in OpenStreetMap

In recent years, a web phenomenon known as Volunteered Geographic Information (VGI) has produced large crowdsourced geographic data sets. OpenStreetMap (OSM), the leading VGI project, aims at building an open-content world map through user contributions. OSM semantics consists of a set of properties (called ‘tags’) describing geographic classes, whose usage is defined by project contributors on a dedicated Wiki website. Because of its simple and open semantic structure, the OSM approach often results in noisy and ambiguous data, limiting its usability for analysis in information retrieval, recommender systems and data mining. Devising a mechanism for computing the semantic similarity of the OSM geographic classes can help alleviate this semantic gap. The contribution of this paper is twofold. It consists of (1) the development of the OSM Semantic Network by means of a web crawler tailored to the OSM Wiki website; this semantic network can be used to compute semantic similarity through co-citation measures, providing a novel semantic tool for OSM and GIS communities; (2) a study of the cognitive plausibility (i.e. the ability to replicate human judgement) of co-citation algorithms when applied to the computation of semantic similarity of geographic concepts. Empirical evidence supports the usage of co-citation algorithms—SimRank showing the highest plausibility—to compute concept similarity in a crowdsourced semantic network.     

基于语义的多文种编码方案SemaCode

分析了现有的几种常用编码方案的优点和缺陷,并提出了一个基于语义的多文种编码方案SemaCode及其模型。SemaCode模型分为六个层次,分别为交换传输层、字符码位层、词码位层、属性层、语义层和应用接口层。SemaCode是一种面向信息处理、可扩展的多文种编码方案,它在码位层SemaCode以字符为单位编码,并在编码中嵌入文种信息;在词码位层提出了以语义为轴心,以词为单位的编码理念;在属性层引入了一种对编码进行描述的标签机制,使得编码具有良好的可描述性和可扩展性;另外,在语义层以及其他层次提出了基于码位和描述协议的语义表示方法,并使得SemaCode成为一种具有部分可计算特性的编码方案。最后,在与Unicode对比的基础上,分析了SemaCode所具有的优势。     

语义P2P系统上的知识分享研究*

由于语义P2P系统的知识分散部署于各个peer节点上,而每个peer节点都拥有其自身异构的知识模式和本体表示,这就给语义P2P节点间的知识分享带来了困难。对语义P2P系统进行了深入分析,着重对P2P节点之间的知识分享问题进行了论述,并进一步提出自己的设计思路来解决这一问题。