Recommendation algorithm of the app store by using semantic relations between apps

In this paper, we propose a personalized recommendation system for mobile application software (app) to mobile user using semantic relations of apps consumed by users. To do that, we define semantic relations between apps consumed by a specific member and his/her social members using Ontology. Based on the relations, we identify the most similar social members from the reasoning process. The reasoning is explored from measuring the common attributes between apps consumed by the target member and his/her social members. The more attributes shared by them, the more similar is their preference for consuming apps. We also develop a prototype of our system using OWL (Ontology Web Language) by defining ontology-based semantic relations among 50 mobile apps. Using the prototype, we showed the feasibility of our algorithm that our recommendation algorithm can be practical in the real field and useful to analyze the preference of mobile user.     

Hybrid method for text summarization based on statistical and semantic treatment

Text summarization presents several challenges such as considering semantic relationships among words, dealing with redundancy and information diversity issues. Seeking to overcome these problems, we propose in this paper a new graph-based Arabic summarization system that combines statistical and semantic analysis. The proposed approach utilizes ontology hierarchical structure and relations to provide a more accurate similarity measurement between terms in order to improve the quality of the summary. The proposed method is based on a two-dimensional graph model that makes uses statistical and semantic similarities. The statistical similarity is based on the content overlap between two sentences, while the semantic similarity is computed using the semantic information extracted from a lexical database whose use enables our system to apply reasoning by measuring semantic distance between real human concepts. The weighted ranking algorithm PageRank is performed on the graph to produce significant score for all document sentences. The score of each sentence is performed by adding other statistical features. In addition, we address redundancy and information diversity issues by using an adapted version of Maximal Marginal Relevance method. Experimental results on EASC and our own datasets showed the effectiveness of our proposed approach over existing summarization systems.

     

Fast semantic object search and detection for vegetable trading information using Steiner tree

We propose an approach to speed up the semantic object search and detection for vegetable trading information using Steiner Tree. Through analysis, comparing the relevant ontology construction method, we present a set of ontology construction methods based on domain ontology for vegetables transaction information. With Jena2 provides rule-based reasoning engine, More related information could be searched with the help of ontology database and ontology reasoning, query expansion is to achieve sub-vocabulary of user input, the parent class of words, equivalence class of extensions, and use of ontology reasoning to get some hidden information to use of these technologies, we design and implementation of ontology-based semantic vegetables transaction information retrieval system, and through compare to keyword-based matching of large-scale vegetable trading site retrieval systems, the results show that the recall and precision rate of ontology-based information retrieval system much better than keyword-based information retrieval system, and has some practical value.     

Agent based intelligent search framework for product information using ontology mapping

The Semantic Web and Web services provide many opportunities in various applications such as product search and comparison in electronic commerce. We implemented an intelligent meta-search and recommendation system for products through consideration of multiple attributes by using ontology mapping and Web services. Under the assumption that each shopping site offers product ontology and product search service with Web services, we proposed a meta-search framework to configure a customer’s search intent, make and dispatch proper queries to each shopping site, evaluate search results from shopping sites, and show the customer the relevant product list with associated rankings. Ontology mapping is used for generating proper queries for shopping sites that have different product categories. We also implemented our framework and performed empirical evaluation of our approach with two leading shopping sites in the world.     

基于本体的智能检索及其在泌尿外科中的应用

以本体论作为指导理论,通过研究泌尿外科辅助诊断系统模型,在泌尿外科领域本体的基础上研究语义相似度、语义相关度的计算方法,并提出新的相关度计算方法。该方法可以定量地分析领域本体中的概念间相关度。并通过建立泌尿外科本体,实现基于泌尿外科本体的语义推理。     

制造资源智能检索系统研究与实现

基于语义网技术，提出一个原型系统Swirrsm，对网络化制造环境中分布式异地企业资源信息获取与用户透明化检索的关键技术进行了深入研究．Swirrsm系统以网络化制造本体作为语义和推理支撑；提出多层次信息智能检索模型，完成用户透明化的智能检索；提供三种用户交互方式实现基于语义的用户查询表达．     

基于本体特征提取的事例推理研究

本文以智能交易甄别原型系统为例,以反洗钱知识为背景,结合基于本体的半结构化文本特征提取和基于事例的推理方法,通过对可疑案例报告提交的数据进行特征值提取,并与案例库中的案例进行匹配,从而发现大额可疑交易记录。将基于事例推理技术应用于大额可疑交易甄别中,是对该技术应用领域的成功扩充,同时也是对基于模型推理的有效补
充。本文利用本体对领域知识的描述信息来分析特征词之间的关系,描述了基于事例推理的思想及方法,并结合实例对其进行了进一步的说明。     

A Flexible Ontology Reasoning Architecture for the Semantic Web

Knowledge-based systems in the semantic Web era can make use of the power of the semantic Web languages and technologies, in particular those related to ontologies. Recent research has shown that user-defined data types are very useful for semantic Web and ontology applications. The W3C semantic Web best practices and development working group has set up a task force to address this issue. Very recently, OWL-Eu and OWL-E, two decidable extensions of the W3C standard ontology language OWL DL, have been proposed to support customized data types and customized data type predicates, respectively. In this paper, we propose a flexible reasoning architecture for these two expressive semantic Web ontology languages and describe our prototype implementation of the reasoning architecture, based on the well-known FaCT DL reasoner, which witnesses the two key flexibility features of our proposed architecture: 1) It allows users to define their own data types and data type predicates based on built-in ones and 2) new data type reasoners can be added into the architecture without having to change the concept reasoner     

基于混合推理的知识库的构建及其应用研究

该文提出了基于OWL本体与Prolog规则的平面几何知识库的构建方法,从而可形式化地表示平面几何中丰富的语义信息.一方面,用类型、定义域、值域、分类、属性、实例等本体描述来表达结构化的知识,为领域内概念与概念之间关系的描述提供形式化的语义;另一方面,用Prolog规则来解决本体不能有效表达的诸如属性之间的关系和操作等问题,从而支持复杂关系间的推理.在此基础上,用Protégé和Prolog构建了一个基于本体和规则的平面几何知识库.实验证明:此知识库可实现知识和语义层次上的信息查询,还可进行复杂问题求解,其丰富的语义描述和混合推理能力弥补了传统知识库的不足.     

基于OWL的网络化制造本体构建分析

OWL是一种新的标准化本体定义语言,拥有比RDF Schema更丰富的表达能力,能帮助机器自动完成智能搜索和推理。本文尝试把OWL应用到网络化制造本体构建上,首先提出了一种本体构建的通用方法,随后结合OWL的特性分析了网络化制造本体中可能出现的各种元素,最后设计了一些搜索和匹配问题,用于在原型系统中检测所设计的主体。     

ABAC策略语义表示和决策方法

为解决开放式系统环境中基于属性的访问控制（Attribute—Based Access Control,ABAC）策略语义层次上的表示和决策问题,提出了ABAC策略的本体表示方法。该方法基于ABAC策略模型到描述逻辑定义的映射,使用语义Web规则语言（swRL）处理系统内部关系定义。在此基础上,提出了基于封闭世界和实例实现推理的策略决策框架。最后从可靠性和完备性两方面说明了决策方法的正确性,验证实验表明了方法在实际应用中的适用性。     

基于本体的PDM系统应用体系结构

在研究产品数据管理系统的基础上,结合了本体思想,提出一个原型系统OntoPDM。该系统建立了企业资源本体库,并构造企业资源的推理规则。运用Jena实现该领域基于语义的检索功能,得出潜在的语义查询结果,为企业资源领域提高信息检索的查准率和查全率提供一个有效方法。     

Image retrieval with a multi-modality ontology

Ontologies represent domain concepts and relations in a form of semantic network. Many research works use ontologies in the information matchmaking and retrieval. This trend is further accelerated by the convergence of various information sources supported by ontologies. In this paper, we propose a novel multi-modality ontology model that integrates both the low-level image features and the high-level text information to represent image contents for image retrieval. By embedding this ontology into an image retrieval system, we are able to realize intelligent image retrieval with high precision. Moreover, benefiting from the soft-coded ontology model, this system has good flexibility and can be easily extended to the larger domains. Currently, our experiment is conducted on the animal domain canine. An ontology has been built based on the low-level features and the domain knowledge of canine. A prototype retrieval system is set up to assess the performance. We compare our experiment results with traditional text-based image search engine and prove the advantages of our approach.     

装配公差综合领域本体知识库的构建

为了减少机械产品设计过程中的不确定性,解决装配公差信息在异构CAX系统中共享性差和传递不畅的问题,利用本体丰富的语义知识和语法结构,通过分析装配公差综合领域相关知识,采用网络本体语言OWL定义其中的概念和关系,并采用语义网规则语言SWRL定义其中的约束条件和分配经验。将基于OWL的结构化知识转换成事实、基于SWRL的约束化知识转换成规则,并在推理引擎的基础上构建装配公差综合领域本体知识库。同时开发了基于本体的装配公差综合原型系统,实现了装配公差类型和装配公差值的自动生成。     

Converting image to a gateway to an information portal for digital signage

Digital signage has recently emerged as a new channel for communicating with people in diverse domains such as advertising, shopping mall and public service. In this paper, we propose a novel data fusion method for converting an advertisement image into a gateway to an information portal based on steganography technology for digital signage. We make the information portal very flexible just by changing the link or by organizing the contents dynamically. Typical contents include product information and summary of user evaluation. To implement this scheme, we first register products of interest with their representative features and quick response (QR) code. The representative points are used for detecting products in images and their QR code is embedded into the detected product area using our steganography technique. We implement a prototype system based on our scheme, and show its effectiveness through extensive experiments.     

可重构知识管理系统中案例与规则的集成推理方法

传统上的知识管理工具往往在设计时就固定了知识结构,这样的系统不但缺乏通用性,而且也限制了知识检索与推理的效率。介绍一个基于本体的可重构知识管理系统,知识作为本体概念的对象实例,利用本体模型的可定制性,解决了以往知识类型不能扩展的问题。详细阐述了结合案例推理与规则推理的集成推理方法,通过规则学习算法支持了规则库的动态扩充与调整,并将本体类的语义关系应用于推理方法,进一步提高了推理的效率。最后介绍了该系统在某飞机设计研究院的应用情况和今后的研究方向。     

D-S证据理论和本体推理互补的活动识别方法

有效的活动识别是智能辅助的关键。结合D-S证据和本体推理,提出一种互补结构的活动识别方法。该方法通过在证据理论和知识库之间建立对应关系形成互补,既解决了异构数据之间的知识共享,又对本体推理的规则冲突进行了处理,提高推理结果的准确性。通过复杂活动实例在原型系统中的应用,验证了该方法的可行性和有效性,并且能够有效地提高活动识别准确率。     

Implementing situation-aware and user-adaptive music recommendation service in semantic web and real-time multimedia computing environment

With the advent of the ubiquitous era, many studies have been devoted to various situation-aware services in the semantic web environment. One of the most challenging studies involves implementing a situation-aware personalized music recommendation service which considers the user’s situation and preferences. Situation-aware music recommendation requires multidisciplinary efforts including low-level feature extraction and analysis, music mood classification and human emotion prediction. In this paper, we propose a new scheme for a situation-aware/user-adaptive music recommendation service in the semantic web environment. To do this, we first discuss utilizing knowledge for analyzing and retrieving music contents semantically, and a user adaptive music recommendation scheme based on semantic web technologies that facilitates the development of domain knowledge and a rule set. Based on this discussion, we describe our Context-based Music Recommendation (COMUS) ontology for modeling the user’s musical preferences and contexts, and supporting reasoning about the user’s desired emotions and preferences. Basically, COMUS defines an upper music ontology that captures concepts on the general properties of music such as titles, artists and genres. In addition, it provides functionality for adding domain-specific ontologies, such as music features, moods and situations, in a hierarchical manner, for extensibility. Using this context ontology, we believe that logical reasoning rules can be inferred based on high-level (implicit) knowledge such as situations from low-level (explicit) knowledge. As an innovation, our ontology can express detailed and complicated relations among music clips, moods and situations, which enables users to find appropriate music. We present some of the experiments we performed as a case-study for music recommendation.     

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.