Mining temporal explicit and implicit semantic relations between entities using web search engines

In this paper, we study the problem of mining temporal semantic relations between entities. The goal of the studied problem is to mine and annotate a semantic relation with temporal, concise, and structured information, which can release the explicit, implicit, and diversity semantic relations between entities. The temporal semantic annotations can help users to learn and understand the unfamiliar or new emerged semantic relations between entities. The proposed temporal semantic annotation structure integrates the features from IEEE and Renlifang. We propose a general method to generate temporal semantic annotation of a semantic relation between entities by constructing its connection entities, lexical syntactic patterns, context sentences, context graph, and context communities. Empirical experiments on two different datasets including a LinkedIn dataset and movie star dataset show that the proposed method is effective and accurate. Different from the manually generated annotation repository such as Wikipedia and LinkedIn, the proposed method can automatically mine the semantic relation between entities and does not need any prior knowledge such as ontology or the hierarchical knowledge base. The proposed method can be used on some applications, which proves the effectiveness of the proposed temporal semantic relations on many web mining tasks.     

Evocation: analyzing and propagating a semantic link based on free word association

Studies of lexical–semantic relations aim to understand the mechanism of semantic memory and the organization of the mental lexicon. However, standard paradigmatic relations such as “hypernym” and “hyponym” cannot capture connections among concepts from different parts of speech. WordNet, which organizes synsets (i.e., synonym sets) using these lexical–semantic relations, is rather sparse in its connectivity. According to WordNet statistics, the average number of outgoing/incoming arcs for the hypernym/hyponym relation per synset is 1.33. Evocation, defined as how much a concept (expressed by one or more words) brings to mind another, is proposed as a new directed and weighted measure for the semantic relatedness among concepts. Commonly applied semantic relations and relatedness measures do not seem to be fully compatible with data that reflect evocations among concepts. They are compatible but evocation captures MORE. This work aims to provide a reliable and extendable dataset of concepts evoked by, and evoking, other concepts to enrich WordNet, the existing semantic network. We propose the use of disambiguated free word association data (first responses to verbal stimuli) to infer and collect evocation ratings. WordNet aims to represent the organization of mental lexicon, and free word association which has been used by psycholinguists to explore semantic organization can contribute to the understanding. This work was carried out in two phases. In the first phase, it was confirmed that existing free word association norms can be converted into evocation data computationally. In the second phase, a two-stage association-annotation procedure of collecting evocation data from human judgment was compared to the state-of-the-art method, showing that introducing free association can greatly improve the quality of the evocation data generated. Evocation can be incorporated into WordNet as directed links with scales, and benefits various natural language processing applications.     

Effective Tag Recommendation System Based on Topic Ontology Using Wikipedia and WordNet

In this paper, we proposed a novel approach based on topic ontology for tag recommendation. The proposed approach intelligently generates tag suggestions to blogs. In this approach, we construct topic ontology through enriching the set of categories in existing small ontology called as Open Directory Project. To construct topic ontology, a set of topics and their associated semantic relationships is identified automatically from the corpus‐based external knowledge resources such as Wikipedia and WordNet. The construction relies on two folds such as concept acquisition and semantic relation extraction. In the first fold, a topic‐mapping algorithm is developed to acquire the concepts from the semantic of Wikipedia. A semantic similarity‐clustering algorithm is used to compute the semantic similarity measure to group the set of similar concepts. The second is the semantic relation extraction algorithm, which derives associated semantic relations between the set of extracted topics from the lexical patterns between synsets in WordNet. A suitable software prototype is created to implement the topic ontology construction process. A Jena API framework is used to organize the set of extracted semantic concepts and their corresponding relationship in the form of knowledgeable representation of Web ontology language. Thus, Protégé tool provides the platform to visualize the automatically constructed topic ontology successfully. Using the constructed topic ontology, we can generate and suggest the most suitable tags for the new resource to users. The applicability of topic ontology with a spreading activation algorithm supports efficient recommendation in practice that can recommend the most popular tags for a specific resource. The spreading activation algorithm can assign the interest scores to the existing extracted blog content and tags. The weight of the tags is computed based on the activation score determined from the similarity between the topics in constructed topic ontology and content of the existing blogs. High‐quality tags that has the highest activation score is recommended to the users. Finally, we conducted experimental evaluation of our tag recommendation approach using a large set of real‐world data sets. Our experimental results explore and compare the capabilities of our proposed topic ontology with the spreading activation tag recommendation approach with respect to the existing AutoTag mechanism. And also discuss about the improvement in precision and recall of recommended tags on the data sets of Delicious and BibSonomy. The experiment shows that tag recommendation using topic ontology results in the folksonomy enrichment. Thus, we report the results of an experiment mean to improve the performance of the tag recommendation approach and its quality.     

Taxonomy-based information content and wordnet-wiktionary-wikipedia glosses for semantic relatedness

Computing the semantic similarity/relatedness between terms is an important research area for several disciplines, including artificial intelligence, cognitive science, linguistics, psychology, biomedicine and information retrieval. These measures exploit knowledge bases to express the semantics of concepts. Some approaches, such as the information theoretical approaches, rely on knowledge structure, while others, such as the gloss-based approaches, use knowledge content. Firstly, based on structure, we propose a new intrinsic Information Content (IC) computing method which is based on the quantification of the subgraph formed by the ancestors of the target concept. Taxonomic measures including the IC-based ones consume the topological parameters that must be extracted from taxonomies considered as Directed Acyclic Graphs (DAGs). Accordingly, we propose a routine of graph algorithms that are able to provide some basic parameters, such as depth, ancestors, descendents, Lowest Common Subsumer (LCS). The IC-computing method is assessed using several knowledge structures which are: the noun and verb WordNet “is a” taxonomies, Wikipedia Category Graph (WCG), and MeSH taxonomy. We also propose an aggregation schema that exploits the WordNet “is a” taxonomy and WCG in a complementary way through the IC-based measures to improve coverage capacity. Secondly, taking content into consideration, we propose a gloss-based semantic similarity measure that operates based on the noun weighting mechanism using our IC-computing method, as well as on the WordNet, Wiktionary and Wikipedia resources. Further evaluation is performed on various items, including nouns, verbs, multiword expressions and biomedical datasets, using well-recognized benchmarks. The results indicate an improvement in terms of similarity and relatedness assessment accuracy.     

基于语义Web的空间信息共享服务

存取、交换、集成空间信息是Web应用研究的热点之一。然而,Web是设计为人使用的,人们必须浏览、理解、选择和导航Web信息。此外,由于数据间的语义冲突和缺乏集成存取共享的空间信息的工具,空间信息很难得到利用。利用语义Web技术和常用本体,提出了查找互联网上的空间信息的方法。首先介绍了语义Web的主要概念;其次描述了语义冲突解决本体;最后详细地探讨了如何在语义Web上实现空间信息共享服务。     

A framework for automated construction of resource space based on background knowledge

Resource Space Model is a kind of data model which can effectively and flexibly manage the digital resources in cyber-physical system from multidimensional and hierarchical perspectives. This paper focuses on constructing resource space automatically. We propose a framework that organizes a set of digital resources according to different semantic dimensions combining human background knowledge in WordNet and Wikipedia. The construction process includes four steps: extracting candidate keywords, building semantic graphs, detecting semantic communities and generating resource space. An unsupervised statistical language topic model (i.e., Latent Dirichlet Allocation) is applied to extract candidate keywords of the facets. To better interpret meanings of the facets found by LDA, we map the keywords to Wikipedia concepts, calculate word relatedness using WordNet’s noun synsets and construct corresponding semantic graphs. Moreover, semantic communities are identified by GN algorithm. After extracting candidate axes based on Wikipedia concept hierarchy, the final axes of resource space are sorted and picked out through three different ranking strategies. The experimental results demonstrate that the proposed framework can organize resources automatically and effectively.     

Automatising the learning of lexical patterns: An application to the enrichment of WordNet by extracting semantic relationships from Wikipedia

This paper describes an automatic approach to identify lexical patterns that represent semantic relationships between concepts in an on-line encyclopedia. Next, these patterns can be applied to extend existing ontologies or semantic networks with new relations. The experiments have been performed with the Simple English Wikipedia and WordNet 1.7. A new algorithm has been devised for automatically generalising the lexical patterns found in the encyclopedia entries. We have found general patterns for the hyperonymy, hyponymy, holonymy and meronymy relations and, using them, we have extracted more than 2600 new relationships that did not appear in WordNet originally. The precision of these relationships depends on the degree of generality chosen for the patterns and the type of relation, being around 60–70% for the best combinations proposed.     

Exploiting noun phrases and semantic relationships for text document clustering

Text document clustering plays an important role in providing better document retrieval, document browsing, and text mining. Traditionally, clustering techniques do not consider the semantic relationships between words, such as synonymy and hypernymy. To exploit semantic relationships, ontologies such as WordNet have been used to improve clustering results. However, WordNet-based clustering methods mostly rely on single-term analysis of text; they do not perform any phrase-based analysis. In addition, these methods utilize synonymy to identify concepts and only explore hypernymy to calculate concept frequencies, without considering other semantic relationships such as hyponymy. To address these issues, we combine detection of noun phrases with the use of WordNet as background knowledge to explore better ways of representing documents semantically for clustering. First, based on noun phrases as well as single-term analysis, we exploit different document representation methods to analyze the effectiveness of hypernymy, hyponymy, holonymy, and meronymy. Second, we choose the most effective method and compare it with the WordNet-based clustering method proposed by others. The experimental results show the effectiveness of semantic relationships for clustering are (from highest to lowest): hypernymy, hyponymy, meronymy, and holonymy. Moreover, we found that noun phrase analysis improves the WordNet-based clustering method.     

基于地理空间数据的知识图谱构建技术研究

随着3S技术迅猛发展,地理空间数据呈现出爆发式增长趋势,基于地理空间数据构建知识图谱,实现数据到空间知识的转换成为亟待解决的科学问题。针对通用知识图谱仅以属性和语义关系表示空间知识,以及空间关系相对缺失等问题,该文首先描述了空间关系的表示方法;其次,提出了基于空间关系的知识图谱构建技术流程,重点研究空间关系抽取和表示以及多源地理空间数据融合的问题,实现了地理知识图谱构建;最后,论述知识图谱在地理空间领域的应用方向。该研究可以促进地理空间数据和语义网技术的整合,实现网络文本空间化、空间数据语义化,进一步提高智能化服务水平。     

基于WordNet的Web服务发现模型

针对目前语义Web服务发现机制只能基于一个本体发布,查找的缺点,结合WordNet本体库与OWL-S语义描述语言设计一个语义明确的Web服务发现模型。该模型中采用OWL-S对Web服务进行语义描述,对领域相关本体中的概念进行预处理,以WordNet提供的术语语义为基础,建立一个全局领域相关本体。这种全局领域本体建立方法避免了因对概念理解不统一而产生的不一致性,得到的本体易于扩展。     

语义万维网中基于Agent的智能检索框架

语义万维网是目前国际万维网联盟（world wide web consortium,W3C）为了解决因Web上的数据缺少语义信息而难以实现自动化处理的问题所开展的研究项目,其目的是为了对Web上发布的信息实现智能推理和自动化处理。Agent作为一种智能化主体,非常适合语义万维网环境下的各种应用。在语义万维网和智能Agent研究的基础上,综合信息检索、知识表示、Ontology建模等多方面技术,提出并实现了一个基于Ontology实现语义信息检索的多Agent系统框架,该系统包括信息收集、存储、查询和推理4个主要部分。     

一种基于短文本相似度计算的知识子图融合方法

知识图谱作为语义网的数据支撑,被广泛应用于语义搜索、深度问答和在线教育等领域.知识融合是构建知识图谱的一个重要环节,将知识图中结构信息和语义信息进行融合是目前的研究热点.本文结合众包的方式,提出了一种基于短文本相似度计算的知识子图融合方法.该方法平衡各结点的结构连接和语义信息,通过学习融合权重,将高维向量转换为双邻接矩阵,得到具有高属性语义相似性的密集连接图.实验结果表明,本文提出的"群体智慧"方法能提升文本相似度计算的准确率,提高融合的质量.     

基于知识图谱的案件特征增强法律判决预测

现有基于知识图谱的法律判决预测方法重点关注案件的要素实体和关系,不能充分地获取案件的特征信息。针对该问题,提出了一种增强案件特征融合的知识图谱法律判决预测方法。首先,该方法利用双向门控循环神经网络挖掘事实描述文本深层次的因果、时序等全文语义特征信息。然后通过知识图谱向量空间中案例间相似度注意力计算学习类案特征表示。最后,融合特征信息和知识图谱的结构化知识,丰富实体和关系在案件事实文本中的语义特征表示,实现法律判决链路预测任务。在危险驾驶罪和盗窃罪两类罪名数据集上的实验结果显示,该方法在MRR、Hit@1两个关键评价指标上与当前表现最好的链路预测模型相比提升了1.5%左右,Hit@3和Hit@10等指标也均有提升,验证了案件特征增强融合能补充法律知识图谱中缺失的案件特征信息并提高预测的效果。     

语义Web服务匹配框架模型研究与设计

针对目前语义Web服务匹配缺乏灵活、有效的匹配方法及其框架模型的现状,分析了语义Web服务匹配的各个阶段,对其中的不足进行了改进,提出了本体概念的权重的计算方法,本体概念之间相似度的具体量化计算方法,新的接口匹配方法.在语义Web服务匹配框架方面,根据语义Web服务匹配的特征,提出了基于过滤器的框架,并给出了相应的具体设计.     

基于软件知识图谱的代码语义标签自动生成方法

开源及企业软件项目和各类软件开发网站上的代码片段是重要的软件开发资源.然而,很多开发者代码搜索需求反映的代码的高层意图和主题难以通过基于代码文本的信息检索技术来实现精准的代码搜索.因此,反映代码整体意图和主题的语义标签对于改进代码搜索、辅助代码理解都具有十分重要的作用.现有的标签生成技术主要面向文本内容或依赖于历史数据,无法满足大范围代码语义标注和辅助搜索、理解的需要.针对这一问题,提出了一种基于知识图谱的代码语义标签自动生成方法KGCodeTagger.该方法通过基于API文档和软件开发问答文本的概念和关系抽取构造软件知识图谱,作为代码语义标签生成的基础.针对给定的代码,该方法识别并抽取出通用API调用或概念提及,并链接到软件知识图谱中的相关概念上.在此基础上,该方法进一步识别与所链接的概念相关的其他概念作为候选,然后按照多样性和代表性排序,产生最终的代码语义标签.通过实验对KGCodeTagger软件知识图谱构建的各个步骤进行了评估,并通过与几个已有的基准方法的比较,对所生成的代码语义标签质量进行了评估.实验结果表明,KGCodeTagger的软件知识图谱构建步骤是合理有效的,该方法所生成的代码语义标签是高质量、有意义的,能够帮助开发人员快速理解代码的意图.     

关联数据的自然语言查询方法

以RDF结构为基础的数据网的发展中,高效数据检索成为关键问题之一。形式化查询语言(如SPARQL)因其语法的复杂性及查询本体的相关性阻碍其效用的发挥,迫切需要新的方法或工具实现以自然语言为基础(如关键字检索)的检索。形式化查询语言是检索这类结构化数据的有效方式,用户习惯自然语言为基础的检索方式。因而如何自动将关键词为基础的检索方式转换成以形式化查询为基础的检索方式是实现数据网的重要一环。关联数据的自然语言查询方法自动将自然语言查询转换成SPARQL查询,提高系统的有效性和效率。文中在抽象转换度量模型的基础上,以本体为基础构建查询语义图及实现语义消歧,构建SPARQL查询。实验结果表明,该方法具有更高的召回率、精度及更低的时间消耗。     

Concept vector for semantic similarity and relatedness based on WordNet structure

We define WordNet based hierarchy concept tree (HCT) and hierarchy concept graph (HCG), HCT contains hyponym/hypernym kind of relation in WordNet while HCG has more meronym/holonym kind of edges than in HCT, and present an advanced concept vector model for generalizing standard representations of concept similarity in terms of WordNet-based HCT. In this model, each concept node in the hierarchical tree has ancestor and descendent concept nodes composing its relevancy nodes, thus a concept node is represented as a concept vector according to its relevancy nodes’ local density and the similarity of the two concepts is obtained by computing the cosine similarity of their vectors. In addition, the model is adjustable in terms of multiple descendent concept nodes. This paper also provides a method by which this concept vector may be applied with regard to HCG into HCT. With this model, semantic similarity and relatedness are computed based on HCT and HCG. The model contains structural information inherent to and hidden in the HCT and HCG. Our experiments showed that this model compares favorably to others and is flexible in that it can make comparisons between any two concepts in a WordNet-like structure without relying on any additional dictionary or corpus information.     

A topic-specific crawling strategy based on semantics similarity

With the Internet growing exponentially, search engines are encountering unprecedented challenges. A focused search engine selectively seeks out web pages that are relevant to user topics. Determining the best strategy to utilize a focused search is a crucial and popular research topic. At present, the rank values of unvisited web pages are computed by considering the hyperlinks (as in the PageRank algorithm), a Vector Space Model and a combination of them, and not by considering the semantic relations between the user topic and unvisited web pages. In this paper, we propose a concept context graph to store the knowledge context based on the user's history of clicked web pages and to guide a focused crawler for the next crawling. The concept context graph provides a novel semantic ranking to guide the web crawler in order to retrieve highly relevant web pages on the user's topic. By computing the concept distance and concept similarity among the concepts of the concept context graph and by matching unvisited web pages with the concept context graph, we compute the rank values of the unvisited web pages to pick out the relevant hyperlinks. Additionally, we constitute the focused crawling system, and we retrieve the precision, recall, average harvest rate, and F-measure of our proposed approach, using Breadth First, Cosine Similarity, the Link Context Graph and the Relevancy Context Graph. The results show that our proposed method outperforms other methods.     

A multi-strategy knowledge interoperability framework for heterogeneous learning objects

This paper presents a knowledge exchange framework that can leverage the interoperability among semantically heterogeneous learning objects. With the release of various e-Learning standards, learning contents and digital courses are easy to achieve cross-platform sharing, exchanging, and even reorganizing. However, knowledge sharing in semantic level is still a challenge due to that the learning materials can be presented in any form, such as audios, videos, web pages, and even flash files. The proposed knowledge exchange framework allows users to share their learning materials (also called “learning objects”) in semantic level automatically. This framework contains two methodologies: the first is a semantic mapping between knowledge bases (i.e. ontologies) which have essentially similar concepts, and the second is an ontology-based classification algorithm for sharable learning objects. The proposed algorithm adopts the IMS DRI standard and classifies the sharable learning objects from heterogeneous repositories into a local knowledge base by their inner meaning instead of keyword matching. Significance of this research lies in the semantic inferring rules for ontology mapping and learning objects classification as well as the full automatic processing and self-optimizing capability. Focused on digital learning materials and contrasted to other traditional technologies, the proposed approach has experimentally demonstrated significantly improvement in performance.     

Learning the semantics of structured data sources

Information sources such as relational databases, spreadsheets, XML, JSON, and Web APIs contain a tremendous amount of structured data that can be leveraged to build and augment knowledge graphs. However, they rarely provide a semantic model to describe their contents. Semantic models of data sources represent the implicit meaning of the data by specifying the concepts and the relationships within the data. Such models are the key ingredients to automatically publish the data into knowledge graphs. Manually modeling the semantics of data sources requires significant effort and expertise, and although desirable, building these models automatically is a challenging problem. Most of the related work focuses on semantic annotation of the data fields (source attributes). However, constructing a semantic model that explicitly describes the relationships between the attributes in addition to their semantic types is critical.We present a novel approach that exploits the knowledge from a domain ontology and the semantic models of previously modeled sources to automatically learn a rich semantic model for a new source. This model represents the semantics of the new source in terms of the concepts and relationships defined by the domain ontology. Given some sample data from the new source, we leverage the knowledge in the domain ontology and the known semantic models to construct a weighted graph that represents the space of plausible semantic models for the new source. Then, we compute the top k candidate semantic models and suggest to the user a ranked list of the semantic models for the new source. The approach takes into account user corrections to learn more accurate semantic models on future data sources. Our evaluation shows that our method generates expressive semantic models for data sources and services with minimal user input. These precise models make it possible to automatically integrate the data across sources and provide rich support for source discovery and service composition. They also make it possible to automatically publish semantic data into knowledge graphs.