Semantic relation based personalized ranking approach for engineering document retrieval

Since engineering design is heavily informational, engineers want to retrieve existing engineering documents accurately during the product development process. However, engineers have difficulties searching for documents because of low retrieval accuracy. One of the reasons for this is the limitation of existing document ranking approaches, in which relationships between terms in documents are not considered to assess the relevance of the retrieved documents. Therefore, we propose a new ranking approach that provides more correct evaluation of document relevance to a given query. Our approach exploits domain ontology to consider relationships among terms in the relevance scoring process. Based on domain ontology, the semantics of a document are represented by a graph (called Document Semantic Network) and, then, proposed relation-based weighting schemes are used to evaluate the graph to calculate the document relevance score. In our ranking approach, user interests and searching intent are also considered in order to provide personalized services. The experimental results show that the proposed approach outperforms existing ranking approaches. A precisely represented semantics of a document as a graph and multiple relation-based weighting schemes are important factors underlying the notable improvement.     

An architectural design for effective information retrieval in semantic web

The current web IR system retrieves relevant information only based on the keywords which is inadequate for that vast amount of data. It provides limited capabilities to capture the concepts of the user needs and the relation between the keywords. These limitations lead to the idea of the user conceptual search which includes concepts and meanings. This study deals with the Semantic Based Information Retrieval System for a semantic web search and presented with an improved algorithm to retrieve the information in a more efficient way.This architecture takes as input a list of plain keywords provided by the user and the query is converted into semantic query. This conversion is carried out with the help of the domain concepts of the pre-existing domain ontologies and a third party thesaurus and discover semantic relationship between them in runtime. The relevant information for the semantic query is retrieved and ranked according to the relevancy with the help of an improved algorithm. The performance analysis shows that the proposed system can improve the accuracy and effectiveness for retrieving relevant web documents compared to the existing systems.     

Searching and ranking method of relevant resources by user intention on the Semantic Web

As the information on the Internet dramatically increases, more and more limitations in information searching are revealed, because web pages are designed for human use by mixing content with presentation. In order to overcome these limitations, the Semantic Web, based on ontology, was introduced by W3C to bring about significant advancement in web searching. To accomplish this, the Semantic Web must provide search methods based on the different relationships between resources.In this paper, we propose a semantic association search methodology that consists of the evaluation of resources and relationships between resources, as well as the identification of relevant information based on ontology, a semantic network of resources and properties. The proposed semantic search method is based on an extended spreading activation technique. In order to evaluate the importance of a query result, we propose weighting methods for measuring properties and resources based on their specificity and generality. From this work, users can search semantically associated resources for their query, confident that the information is valuable and important. The experimental results show that our method is valid and efficient for searching and ranking semantic search results.     

Tailored semantic annotation for semantic search

This paper presents a novel method for semantic annotation and search of a target corpus using several knowledge resources (KRs). This method relies on a formal statistical framework in which KR concepts and corpus documents are homogeneously represented using statistical language models. Under this framework, we can perform all the necessary operations for an efficient and effective semantic annotation of the corpus. Firstly, we propose a coarse tailoring of the KRs w.r.t the target corpus with the main goal of reducing the ambiguity of the annotations and their computational overhead. Then, we propose the generation of concept profiles, which allow measuring the semantic overlap of the KRs as well as performing a finer tailoring of them. Finally, we propose how to semantically represent documents and queries in terms of the KRs concepts and the statistical framework to perform semantic search. Experiments have been carried out with a corpus about web resources which includes several Life Sciences catalogs and Wikipedia pages related to web resources in general (e.g., databases, tools, services, etc.). Results demonstrate that the proposed method is more effective and efficient than state-of-the-art methods relying on either context-free annotation or keyword-based search.     

Ontology-supported FAQ processing and ranking techniques

This paper describes an FAQ system on the Personal Computer (PC) domain, which employs ontology as the key technique to pre-process FAQs and process user query. It is also equipped with an enhanced ranking technique to present retrieved, query-relevant results. Basically, the system bases on the wrapper technique to help clean, retrieve, and transform FAQ information collected from a heterogeneous environment and stores it in an ontological database. During retrieval of FAQs, the system trims irrelevant query keywords, employs either full keywords match or partial keywords match to retrieve FAQs, and removes conflicting FAQs before turning the final results to the user. Ontology plays the key role in all the above activities. To produce a more effective presentation of the search results, the system employs an enhanced ranking technique, which includes Appearance Probability, Satisfaction Value, Compatibility Value, and Statistic Similarity Value as four measures properly weighted to rank the FAQs. Our experiments show the system does improve precision rate and produces better ranking results. The proposed FAQ system manifests the following interesting features. First, the ontology-supported FAQ extraction from webpages can clean FAQ information by removing redundant data, restore missing data, and resolve inconsistent data. Second, the FAQs are stored in an ontology-directed internal format, which supports semantics-constrained retrieval of FAQs. Third, the ontology-supported natural language processing of user query helps pinpoint user’s intent. Finally, the partial keywords match-based ranking method helps present user-most-wanted, conflict-free FAQ solutions for the user.     

Learning domain ontologies for semantic Web service descriptions

High quality domain ontologies are essential for successful employment of semantic Web services. However, their acquisition is difficult and costly, thus hampering the development of this field. In this paper we report on the first stage of research that aims to develop (semi-)automatic ontology learning tools in the context of Web services that can support domain experts in the ontology building task. The goal of this first stage was to get a better understanding of the problem at hand and to determine which techniques might be feasible to use. To this end, we developed a framework for (semi-)automatic ontology learning from textual sources attached to Web services. The framework exploits the fact that these sources are expressed in a specific sublanguage, making them amenable to automatic analysis. We implement two methods in this framework, which differ in the complexity of the employed linguistic analysis. We evaluate the methods in two different domains, verifying the quality of the extracted ontologies against high quality hand-built ontologies of these domains.

Our evaluation lead to a set of valuable conclusions on which further work can be based. First, it appears that our method, while tailored for the Web services context, might be applicable across different domains. Second, we concluded that deeper linguistic analysis is likely to lead to better results. Finally, the evaluation metrics indicate that good results can be achieved using only relatively simple, off the shelf techniques. Indeed, the novelty of our work is not in the used natural language processing methods but rather in the way they are put together in a generic framework specialized for the context of Web services.     

A framework for modeling and evaluating automatic semantic reconciliation

The introduction of the Semantic Web vision and the shift toward machine understandable Web resources has unearthed the importance of automatic semantic reconciliation. Consequently, new tools for automating the process were proposed. In this work we present a formal model of semantic reconciliation and analyze in a systematic manner the properties of the process outcome, primarily the inherent uncertainty of the matching process and how it reflects on the resulting mappings. An important feature of this research is the identification and analysis of factors that impact the effectiveness of algorithms for automatic semantic reconciliation, leading, it is hoped, to the design of better algorithms by reducing the uncertainty of existing algorithms. Against this background we empirically study the aptitude of two algorithms to correctly match concepts. This research is both timely and practical in light of recent attempts to develop and utilize methods for automatic semantic reconciliation.Received: 6 December 2002, Accepted: 15 September 2003, Published online: 19 December 2003Edited by: V. Atluri.     

语义搜索研究综述

语义搜索将语义Web技术引入搜索引擎,改善当前搜索引擎的搜索效果,近年来得到广泛关注.文章介绍了语义搜索领域的研究基础,包括研究现状和常用的研究方法,对语义搜索进行了分类研究和深入分析,语义搜索主要可分为基于传统搜索的增强型语义搜索和基于本体推理的知识型语义搜索;文章指出了语义搜索研究中存在的问题,并对未来开展语义搜索研究进行了总结和展望.     

MuseumFinland—Finnish museums on the semantic web

This article presents the semantic portal MuseumFinland for publishing heterogeneous museum collections on the Semantic Web. It is shown how museums with their semantically rich and interrelated collection content can create a large, consolidated semantic collection portal together on the web. By sharing a set of ontologies, it is possible to make collections semantically interoperable, and provide the museum visitors with intelligent content-based search and browsing services to the global collection base. The architecture underlying MuseumFinland separates generic search and browsing services from the underlying application dependent schemas and metadata by a layer of logical rules. As a result, the portal creation framework and software developed has been applied successfully to other domains as well. MuseumFinland got the Semantic Web Challence Award (second prize) in 2004.     

An ontology-based retrieval system using semantic indexing

In this paper, we present an ontology-based information extraction and retrieval system and its application in the soccer domain. In general, we deal with three issues in semantic search, namely, usability, scalability and retrieval performance. We propose a keyword-based semantic retrieval approach. The performance of the system is improved considerably using domain-specific information extraction, inferencing and rules. Scalability is achieved by adapting a semantic indexing approach and representing the whole world as small independent models. The system is implemented using the state-of-the-art technologies in Semantic Web and its performance is evaluated against traditional systems as well as the query expansion methods. Furthermore, a detailed evaluation is provided to observe the performance gain due to domain-specific information extraction and inferencing. Finally, we show how we use semantic indexing to solve simple structural ambiguities.     

A semantic collaborative awareness model to deal with resource sharing in grids

Grid computing environments are set up mainly to encourage the shared use of different resources based on business/scientific needs. The way these resources are shared in terms of CPU cycles, storage capacity, software licenses etc., is normally dictated by the availability of these resources outside the local administration context. The Semantic Grid is the extension of Grid computing with Semantic Web-based technologies. The Semantic Grid represents grid management data in a machine-understandable format, and reasoning can handle complicated situations in virtual organization management. This paper presents the extension of the collaborative awareness model (CAM) to manage virtual organizations in Semantic Grid environments. CAM applies some theoretical principles of awareness models to promote resource interaction and management, as well as task delivery.     

语义网在文本分类中的应用

随着因特网上信息的大量增加,如果不依靠自动分类而完全通过手工进行文本分类,文本分类是不可能完成的。因此,文本自动分类成为一个重要的研究领域。首先介绍语义网及其相关技术,最后介绍基于本体技术的语义网的自动分类器。     

Ontological semantic inference based on cognitive map

Cognitive map is a well-known approach to model the dynamics of qualitative systems, and has been studied and used in various fields, such as psychology, education, engineering, and management. Although the validity and usefulness of cognitive maps has been proven in many fields, and a considerable number of cognitive maps have been built during the last decade, cognitive map construction and use was just one-off event. In addition, the high degree of cognitive complexities in large cognitive maps makes it difficult for others to understand and exploit the pre-defined cognitive map in another similar domain problem.In this paper, an ontological semantic inference method, which combines the cognitive map and semantic influence, is proposed. This approach reuses a pre-defined cognitive map and provides an ontological semantic inference mechanism in decision making environments by reducing the degree of cognitive complexities in a large cognitive map.     

语义网中的排序问题

网页排序算法的好坏很大程度上影响了万维网搜索引擎的用户体验.语义网为万维网带来了机器可理解的资源描述信息,也为搜索引擎带来了更大的挑战:搜索引擎的检索和排序的对象将不再局限于网页,而是包括了任何可以由URI唯一标识的对象,比如本体、本体中的词汇等等.本文介绍了语义网中不同的排序问题和目前已有的一些算法,并展望了语义网未来面临的排序问题和可能的解决方法.     

Frame-based ontological view for semantic integration

Semantic integration is crucial for successful collaboration between heterogeneous information systems. Traditional ontology-driven approaches rely on the availability of explicit ontologies. However, in most application domains, this prerequisite cannot be met. In order to address this issue, this paper investigates the theoretical foundation of ontologies and extends the traditional ontology concept to an ontological view concept. To explicitly and formally specify the ontological views, a Frame-based Ontological view Specification Language (FOSL) is proposed. This language is based on the frame knowledge representation paradigm and uses XML as the encoding. The ontological view driven semantic integration can be achieved based on the specifications. A proof-of-concept prototype environment has been implemented to achieve semantic integration based on ontological views specified with FOSL.     

Repeatable and reliable semantic search evaluation

An increasing amount of structured data on the Web has attracted industry attention and renewed research interest in what is collectively referred to as semantic search. These solutions exploit the explicit semantics captured in structured data such as RDF for enhancing document representation and retrieval, or for finding answers by directly searching over the data. These data have been used for different tasks and a wide range of corresponding semantic search solutions have been proposed in the past. However, it has been widely recognized that a standardized setting to evaluate and analyze the current state-of-the-art in semantic search is needed to monitor and stimulate further progress in the field. In this paper, we present an evaluation framework for semantic search, analyze the framework with regard to repeatability and reliability, and report on our experiences on applying it in the Semantic Search Challenge 2010 and 2011.     

IRS-III: A broker-based approach to semantic Web services

A factor limiting the take up of Web services is that all tasks associated with the creation of an application, for example, finding, composing, and resolving mismatches between Web services have to be carried out by a software developer. Semantic Web services is a combination of semantic Web and Web service technologies that promise to alleviate these problems. In this paper we describe IRS-III, a framework for creating and executing semantic Web services, which takes a semantic broker-based approach to mediating between service requesters and service providers. We describe the overall approach and the components of IRS-III from an ontological and architectural viewpoint. We then illustrate our approach through an application in the eGovernment domain.     

语义Web搜索技术研究进展

语义Web搜索技术是综合本体论、信息检索、自然语言处理等多学科理论和方法的新兴技术。介绍了语义Web和语义Web搜索的现状。在此基础上,给出了实现语义Web搜索技术的一般体系结构,并进一步分析了各组成模块的基本任务、现有技术和评价体系。最后给出了所做的相关工作和对语义Web搜索技术的展望。     

Matchmaking semantic security policies in heterogeneous clouds

The adoption of the cloud paradigm to access IT resources and services has posed many security issues which need to be cared of. Security becomes even a much bigger concern when services built on top of many commercial clouds have to interoperate. Among others, the value of the service delivered to end customers is strongly affected by the security of network which providers are able to build in typical SOA contexts. Currently, every provider advertises its own security strategy by means of proprietary policies, which are sometimes ambiguous and very often address the security problem from a non-uniform perspective. Even policies expressed in standardized languages do not appear to fit a dynamic scenario like the SOA’s, where services need to be sought and composed on the fly in a way that is compatible with the end-to-end security requirements. We then propose an approach that leverages on the semantic technology to enrich standardized security policies with an ad-hoc content. The semantic annotation of policies enables machine reasoning which is then used for both the discovery and the composition of security-enabled services. In the presented approach the semantic enrichment of policies is enforced by an automatic procedure. We further developed a semantic framework capable of matchmaking in a smart way security capabilities of providers and security requirements of customers, and tested it on a use case scenario.