Extract conceptual graphs from plain texts in patent claims

This paper develops techniques to extract conceptual graphs from a patent claim using syntactic information (POS, and dependency tree) and semantic information (background ontology). Due to plenteous technical domain terms and lengthy sentences prevailing in patent claims, it is difficult to apply a NLP Parser directly to parse the plain texts in the patent claim. This paper combines techniques such as finite state machines, Part-Of-Speech tags, conceptual graphs, domain ontology and dependency tree to convert a patent claim into a formally defined conceptual graph. The method of a finite state machine splits a lengthy patent claim sentence into a set of shortened sub-sentences so that the NLP Parser can parse them one by one effectively. The Part-Of-Speech and dependency tree of a patent claim are used to build the conceptual graph based on the pre-established domain ontology. The result shows that 99% sub-sentences split from 1700 patent claims can be efficiently parsed by the NLP Parser. There are two types of nodes in a conceptual graph, the concept and the relation nodes. Each concept or relation can be extracted directly from a patent claim and each relation can link with a fixed number of concepts in a conceptual graph. From 100 patent claims, the average precision and recall of a concept class mapping from the patent claim to domain ontology are 96% and 89%, respectively, and the average precision and recall for Real relation class mapping are 97% and 98%, respectively. For the concept linking of a relation, the average precision is 79%. Based on the extracted conceptual graphs from patents, it would facilitate automated comparison and summarization among patents for judgment of patent infringement.     

Towards open ontology learning and filtering

Open ontology learning is the process of extracting a domain ontology from a knowledge source in an unsupervised way. Due to its unsupervised nature, it requires filtering mechanisms to rate the importance and correctness of the extracted knowledge. This paper presents OntoCmaps, a domain-independent and open ontology learning tool that extracts deep semantic representations from corpora. OntoCmaps generates rich conceptual representations in the form of concept maps and proposes an innovative filtering mechanism based on metrics from graph theory. Our results show that using metrics such as Betweenness, PageRank, Hits and Degree centrality outperforms the results of standard text-based metrics (TF-IDF, term frequency) for concept identification. We propose voting schemes based on these metrics that provide a good performance in relationship identification, which again provides better results (in terms of precision and F-measure) than other traditional metrics such as frequency of co-occurrences. The approach is evaluated against a gold standard and is compared to the ontology learning tool Text2Onto. The OntoCmaps generated ontology is more expressive than Text2Onto ontology especially in conceptual relationships and leads to better results in terms of precision, recall and F-measure.     

一种从线性概念图中自动抽取本体概念的算法

企业信息集成必须面对大量的遗留系统(Legacy),而获取遗留系统的本体是实现集成遗留系统的关键。依据本体是更抽象概念的论断,建立了关系数据表到线性概念图的映射关系,利用线性概念图这一中介,设计了从线性概念图自动抽取本体的算法,并通过文中的实例加以验证算法的有效性以及复杂性。     

自然语言处理中逻辑词的知识图分析

知识图是一种新的知识表示方法。本文从本体论的角度出发,将知识图的本体论分别与Aristotle、Kant和Peirce的三种知识表示的本体论进行了比较,表明知识图方法的有效性以及本原性,说明知识图是一种更为一般的知识表示方法。从知识图本体论的观点,研究了各类逻辑词的知识图表示。本文结合汉语的特点,从结构的角度,研究并揭示了逻辑词的共性和规律性。进一步阐明知识图“结构就是含义”的思想。逻辑词的知识图分析将为自然语言分析中词典的建立奠定基础。     

Fuzzy logic as a basis for reusing task-based specifications

In this paper, we propose an approach to reusing requirements specification, called task-based specifications in conceptual graphs (TBCG). In TBCG, task-based specification methodology is used to serve as the mechanism to structure the knowledge captured in conceptual models, and conceptual graphs are adopted as the formalism to express requirements specification. TBCG provides several mechanisms to facilitate the reuse of formal specifications: a contextual retrieval mechanism to support context-sensitive specifications retrieval and incremental context acquisition, a graph matching mechanism to compute the similarity between two graphs based on the semantic match and fuzzy logic, and a paraphraser to serve as an explanation mechanism for the retrieval specifications. ©1999 John Wiley & Sons, Inc.     

Graphical Transformation of Multimedia XML Documents

As a commonly acceptable standard for guiding Web markup documents, XML allows the Internet users to create multimedia documents of their preferred structures and share with other people. The creation of various multimedia document structures, typically as trees, implies that some kinds of conversion mechanisms are needed for people using different structures to understand each other. This paper presents a visual approach to the representation and validation of multimedia document structures specified in XML and transformation of one structure to another. The underlying theory of our approach is a context-sensitive graph grammar formalism. The paper demonstrates the conciseness and expressiveness of the graph grammar formalism. An example XML structure is provided and its graph grammar representation, validation and transformation to a multimedia representation are presented.     

一种针对图型结构的本体建模方案

针对传统本体建模方法较为单一、难以适用于复杂且多维的知识对象的问题,提出一种针对图型结构的本体建模表达与实现方案。该方案充分地表达图型结构对象,使用清晰的流程来递增构建本体,并且采用改进前序遍历树模型进行数据库存储,具有较高的检索效率。企业可以高效地复用该套方案来建模图型结构对象。     

气象记录档案知识图谱构建

为提升气象档案管理和社会化利用能力,提出了一种气象记录档案知识图谱构建方法.以安徽省馆藏气象记录档案资源为基础,通过自顶向下的方式设计概念层及层次关系来构建本体库,基于本体库对接实体构建知识模型,分别采取D2RQ、图像识别和定制包装器对不同存储格式的档案进行知识抽取构建数据层,通过建立同义词库等来完成知识融合,利用My...     

Domain ontology graph model and its application in Chinese text classification

This paper proposes an ontology learning method which is used to generate a graphical ontology structure called ontology graph. The ontology graph defines the ontology and knowledge conceptualization model, and the ontology learning process defines the method of semiautomatic learning and generates ontology graphs from Chinese texts of different domains, the so-called domain ontology graph (DOG). Meanwhile, we also define two other ontological operations—document ontology graph generation and ontology graph-based text classification, which can be carried out with the generated DOG. This research focuses on Chinese text data, and furthermore, we conduct two experiments: the DOG generation and ontology graph-based text classification, with Chinese texts as the experimental data. The first experiment generates ten DOGs as the ontology graph instances to represent ten different domains of knowledge. The generated DOGs are then further used for the second experiment to provide performance evaluation. The ontology graph-based approach is able to achieve high text classification accuracy (with 92.3 % in f-measure) over other text classification approaches (such as 86.8 % in f-measure for tf–idf approach). The better performance in the comparative experiments reveals that the proposed ontology graph knowledge model, the ontology learning and generation process, and the ontological operations are feasible and effective.     

基于Neo4j的领域本体存储方法研究*

在分析民航突发事件应急管理领域本体及其存储特点的基础上,提出了一种基于Neo4j的领域本体RDF图数据存储方法,研究了领域本体RDF有向标记图结构与Neo4j图数据库存储模型的关系,结合民航突发事件应急管理领域本体的实例查询,给出了RDF图与Neo4j之间的映射关系及其实现过程。实验验证了Neo4j图数据库在满足领域本体RDF图数据查询的同时,进一步提高了查询的效率,为大数据平台下的RDF图数据语义检索与推理提供了方法支撑。     

k-部排序本体相似度计算

将本体图中每个顶点的相关信息用一个向量表示。根据本体图自身的结构将顶点分成k个部分。在每个部分中选取样本点组成S,并选择相应的排序亏损函数。运用k-部排序学习算法得到最优排序函数,从而将本体结构图中每个顶点映射成一个实数,通过比较实数间的差值判断两概念的相似程度。实验表明该方法对于计算本体概念间的相对相似度是有效的。     

教育测评知识图谱的构建及其表示学习

知识图谱旨在描述现实世界中存在的实体以及实体之间的关系.自2012年谷歌提出“Google Knowledge Graph”以来，知识图谱在学术界和工业界受到广泛关注.针对教育领域中信息缺乏系统性组织的不足，本文构建了面向高中的教育测评知识图谱（Educational Assessment Knowledge Graph，EAKG），其中EAKG的构建包括基于本体技术的知识图谱模式层构建和依托于模式层结构的知识图谱数据层构建.与传统通过网页爬虫等技术手段构建的知识图谱相比，本文构建的知识图谱优点在于逻辑结构清晰，实体间关系的刻画遵循知识图谱模式层的定义.EAKG为领域内知识共享，知识推理，知识表示学习等任务提供了良好的支撑.在真实模考数据上的实验结果表明：在试卷得分预测，知识点得分预测的实体链接预测和三元组分类嵌入式表示学习任务上，引入领域本体作为模式层构建的EAKG的性能优于没有领域本体模式层单纯由数据事实构成的EAKG，实验表明，领域本体的引入对知识图谱的表示学习具有一定的指导意义.     

基于概念相似度计算的多策略本体映射方法

针对本体异构问题,提出基于概念相似度计算的多策略映射方法。选择过滤候选概念集及信息增益计算策略,缩小概念范围,减少计算量。计算时采用基于名称、概念属性和概念关系策略,并综合考虑实例相似度和结构相似度,通过赋予权值进行相似度合并,最终得到准确而完整的映射对。实验结果表明,该方法总体映射效果良好。     

Defining structural properties of ontologies

To define properties of ontologies exactly, a set of ontology models in the form of marked graphs is proposed. To each class of properties its own graph model is attached with established general scheme, way of interpretation, and rules of extracting the structure from the ontology text. Structural properties of ontologies are unambiguously given in terms of graph models. These definitions can be used to evaluate properties of particular ontologies, which is done in two stages. The first stage deals with constructing internal models of ontologies in the form of graph models. At the second stage, the values of structural properties of ontology are obtained using the corresponding graph models of the evaluated ontology and according to the definitions of these properties.     

Graph-based reasoning in collaborative knowledge management for industrial maintenance

Capitalization and sharing of lessons learned play an essential role in managing the activities of industrial systems. This is particularly the case for the maintenance management, especially for distributed systems often associated with collaborative decision-making systems. Our contribution focuses on the formalization of the expert knowledge required for maintenance actors that will easily engage support tools to accomplish their missions in collaborative frameworks. To do this, we use the conceptual graphs formalism with their reasoning operations for the comparison and integration of several conceptual graph rules corresponding to different viewpoint of experts. The proposed approach is applied to a case study focusing on the maintenance management of a rotary machinery system.     

大规模本体分块与映射中一种新的块映射策略

现有的大规模本体分块与映射系统中大多采用基于参考点的块映射策略,映射策略比较单一,块映射质量不高.因此,提出一种新的基于本体块结构的块映射策略,通过重建本体块结构图来获取块与决之间在结构上的相似度,并将其和基于参考点的策略相结合,通过加权求和得到总的相似度.理论分析和实验结果表明,本文的方法块映射准确率高.     

复杂机电产品线缆信息本体表达与存储分析

针对复杂机电产品中线缆工程语义信息统一表达难、线缆复杂拓扑结构难以表示和 存储的问题,提出基于本体和无向图的复杂线缆信息表达与存储分析方法。首先建立了基于布线 设计、装配工艺规划和仿真分析3 个维度的线缆信息集成模型;然后利用本体技术对模型信息进 行了本体建模,增强了线缆语义信息的表达;最后利用一种水平分割算法将线缆零件划分成一个 无向图,在此基础上建立邻接表,实现了线缆零件无向图在计算机中的存储和表达。通过将该方 法应用到某线缆零件的实际分析中,验证了文中方法的可行性。     

Smart VideoText: a video data model based on conceptual graphs

An intelligent annotation-based video data model called Smart VideoText is introduced. It utilizes the conceptual graph knowledge representation formalism to capture the semantic associations among the concepts described in text annotations of video data. The aim is to achieve more effective query, retrieval, and browsing capabilities based on the semantic content of video data. Finally, a generic and modular video database architecture based on the Smart VideoText data model is described.     

Variable weight semantic graph‐based ontology mapping method

The mapping method that is based on the name and structure of the ontology elements is the strategy used in most mapping methods. Methods using the name often only use the similarity between the individual elements in the ontology to predict the semantic relations between two ontologies, while the latter measure the mapping between two ontologies by means of the structural relations between the elements. The effects of these two kinds of mapping strategies are not ideal. Addressing this issue, the work presented in this paper proposes an ontology mapping approach, in which the ontology element name and structure are combined. It uses the approaches based on linguistics and distance to generate a variable weight semantic graph. On this graph, the similarity of element names and structure are calculated through iterative computation. In the process of iteration, similarity result values are constantly adjusted. The approach avoids the problem of single methods that cannot use the entire amount of ontology information; therefore, it provides a more ideal mapping result. For making full use of the message of ontology, our implementation and experimental results are provided to demonstrate the effectiveness of the mapping approach.     

Introducing reasoning into an industrial knowledge management tool

This paper is devoted to an industrial case study focused on the issue of how to enhance an existing knowledge management tool (ITM) with reasoning capabilities, by introducing a semantic query mechanism as well as validation and inference services. ITM knowledge representation language is based on topic maps. We show that these topic maps (and especially those describing the domain ontology and annotation base) can be naturally mapped to the SG\mathcal {SG} family, a sublanguage of conceptual graphs. This mapping equips ITM with a reasoning service. We finally present a media monitoring system benefiting from this transformation and combining ITM with the conceptual graph engine CoGITaNT.