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.     

基于本体知识库的自动语义标注*

为了产生语义Web中的元数据,需要提取Web文档中的语义信息。面对海量的Web文档,自动语义标注相对人工和半自动的语义标注是可行的方法。提出的基于本体知识库的自动语义标注方法,旨在提高标注的质量。为识别出文档中的候选命名实体,设计了语义词典的逻辑结构,论述了以实体之间语义关联路径计算语义距离的方法。语义标注中的复杂问题是语义消歧,提出了基于最短路径的语义消歧方法和基于n-gram的语义消歧方法。采用这种方法对文档进行语义标注,将标注结果持久化为语义索引,为实现语义信息检索提供基础。针对构建的测试数据集,进行的标注实验表明该方法能够依据本体知识库,有效地对Web文档进行自动语义标注。     

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.     

A framework for multi-document abstractive summarization based on semantic role labelling

We propose a framework for abstractive summarization of multi-documents, which aims to select contents of summary not from the source document sentences but from the semantic representation of the source documents. In this framework, contents of the source documents are represented by predicate argument structures by employing semantic role labeling. Content selection for summary is made by ranking the predicate argument structures based on optimized features, and using language generation for generating sentences from predicate argument structures. Our proposed framework differs from other abstractive summarization approaches in a few aspects. First, it employs semantic role labeling for semantic representation of text. Secondly, it analyzes the source text semantically by utilizing semantic similarity measure in order to cluster semantically similar predicate argument structures across the text; and finally it ranks the predicate argument structures based on features weighted by genetic algorithm (GA). Experiment of this study is carried out using DUC-2002, a standard corpus for text summarization. Results indicate that the proposed approach performs better than other summarization systems.     

Toward semantic indexing and retrieval using hierarchical audio models

Semantic-level content analysis is a crucial issue in achieving efficient content retrieval and management. We propose a hierarchical approach that models the statistical characteristics of audio events over a time series to accomplish semantic context detection. Two stages, audio event and semantic context modeling, are devised to bridge the semantic gap between physical audio features and semantic concepts. In this work, hidden Markov models (HMMs) are used to model four representative audio events, i.e., gunshot, explosion, engine, and car-braking, in action movies. At the semantic-context level, Gaussian mixture models (GMMs) and ergodic HMMs are investigated to fuse the characteristics and correlations between various audio events. They provide cues for detecting gunplay and car-chasing scenes, two semantic contexts we focus on in this work. The promising experimental results demonstrate the effectiveness of the proposed approach and exhibit that the proposed framework provides a foundation in semantic indexing and retrieval. Moreover, the two fusion schemes are compared, and the relations between audio event and semantic context are studied.     

Peer-to-peer semantic coordination

Semantic coordination, namely the problem of finding an agreement on the meaning of heterogeneous schemas, is one of the key issues in the development of the Semantic Web. In this paper, we propose a method for discovering semantic mappings across hierarchical classifications (HCs) based on a new approach, which shifts the problem of semantic coordination from the problem of computing linguistic or structural similarities (what most other proposed approaches do) to the problem of deducing relations between sets of logical formulae that represent the meaning of concepts belonging to different schema. We show how to apply the approach and the algorithm to an interesting family of schemas, namely hierarchical classifications, and present the results of preliminary tests on two types of hierarchical classifications, web directories and catalogs. Finally, we argue why this is a significant improvement on previous approaches.     

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.     

Semi-structured document categorization with a semantic kernel

Since a decade, text categorization has become an active field of research in the machine learning community. Most of the approaches are based on the term occurrence frequency. The performance of such surface-based methods can decrease when the texts are too complex, i.e., ambiguous. One alternative is to use the semantic-based approaches to process textual documents according to their meaning. Furthermore, research in text categorization has mainly focused on “flat texts” whereas many documents are now semi-structured and especially under the XML format. In this paper, we propose a semantic kernel for semi-structured biomedical documents. The semantic meanings of words are extracted using the unified medical language system (UMLS) framework. The kernel, with a SVM classifier, has been applied to a text categorization task on a medical corpus of free text documents. The results have shown that the semantic kernel outperforms the linear kernel and the naive Bayes classifier. Moreover, this kernel was ranked in the top 10 of the best algorithms among 44 classification methods at the 2007 Computational Medicine Center (CMC) Medical NLP International Challenge.     

基于本体集成的语义标注模型设计

语义Web的全面实现需借助于语义标注,标注网页信息会涉及到多个本体.据此,通过研究桥本体,提出一个在本体集成的基础上建立起来的多本体语义标注模型.该模型利用桥本体集成顶层本体和多个领域本体,同时借助基于本体的信息抽取技术对网页进行语义标注,并将标注信息存入标注库,使标注信息与网页分离,提高语义检索的效率.通过举例说明了本模型的合理性.     

Learning semantic concepts from image database with hybrid generative/discriminative approach

Semantic gap has become a bottleneck of content-based image retrieval in recent years. In order to bridge the gap and improve the retrieval performance, automatic image annotation has emerged as a crucial problem. In this paper, a hybrid approach is proposed to learn the semantic concepts of images automatically. Firstly, we present continuous probabilistic latent semantic analysis (PLSA) and derive its corresponding Expectation–Maximization (EM) algorithm. Continuous PLSA assumes that elements are sampled from a multivariate Gaussian distribution given a latent aspect, instead of a multinomial one in traditional PLSA. Furthermore, we propose a hybrid framework which employs continuous PLSA to model visual features of images in generative learning stage and uses ensembles of classifier chains to classify the multi-label data in discriminative learning stage. Therefore, the framework can learn the correlations between features as well as the correlations between words. Since the hybrid approach combines the advantages of generative and discriminative learning, it can predict semantic annotation precisely for unseen images. Finally, we conduct the experiments on three baseline datasets and the results show that our approach outperforms many state-of-the-art approaches.     

SWMRD:一个面向互联网的制造资源智能发现系统

为了对面向互联网这一海量资源库中提供准确的、自动的、智能的发现制造资源服务,提出一套基于语义网的制造资源智能发现方法.该方法以制造资源本体与知识一体化建模为基础,通过聚焦爬虫从互联网中获取制造资源信息,形成语义标注实例;建立多层次智能发现检索模型,实现制造资源的精确检索、模糊检索、语义检索以及智能推理功能.为直观表达查询语义结构与概念间的关联性,研制了图形化语义查询前端,并在此基础上开发并实现一套制造资源智能发现系统SWMRD.最后展示了系统实例.     

TAPON-MT: A versatile framework for semantic labelling

Semantic labelling refers to the problem of assigning known labels to the elements of structured information from a source such as an HTML table or an RDF dump with unknown semantics. In the recent years it has become progressively more relevant due to the growth of available structured information in the Web of data that need to be labelled in order to integrate it in data systems. The existing approaches for semantic labelling have several drawbacks that make them unappealing if not impossible to use in certain scenarios: not accepting nested structures as input, being unable to label structural elements, not being customisable, requiring groups of instances when labelling, requiring matching instances to named entities in a knowledge base, not detecting numeric data, or not supporting complex features. In this article, we propose TAPON-MT, a framework for machine learning semantic labelling. Our framework does not have the former limitations, which makes it domain-independent and customisable. We have implemented it with a graphical interface that eases the creation and analysis of models, and we offer a web service API for their application. We have also validated it with a subset of the National Science Foundation awards dataset, and our conclusion is that TAPON-MT creates models to label information that are effective and efficient in practice.     

Rational Research model for ranking semantic entities

Ranking plays important role in contemporary information search and retrieval systems. Among existing ranking algorithms, link analysis based algorithms have been proved to be effective for ranking documents retrieved from large-scale text repositories such as the current Web. Recent developments in semantic Web raise considerable interest in designing new ranking paradigms for various semantic search applications. While ranking methods in this context exist, they have not gained much popularity. In this article we introduce the idea of the “Rational Research” model which reflects search behaviour of a “rational” researcher in a scientific research environment, and propose the RareRank algorithm for ranking entities in semantic search systems, in particular, we focus on elaborating the rationale and implementation of the algorithm. Experiments are performed using the RareRank algorithm and the results are evaluated by domain experts using popular ranking performance measures. A comparison study with existing link-based ranking algorithms reveals the benefits of the proposed method.     

WESONet: Applying semantic web technologies and collaborative tagging to multimedia web information systems

The publication of different media types, like images, audio and video in the World Wide Web is getting more importance each day. However, searching and locating content in multimedia sites is challenging. In this paper, we propose a platform for the development of multimedia web information systems. Our approach is based on the combination between semantic web technologies and collaborative tagging. Producers can add meta-data to multimedia content associating it with different domain-specific ontologies. At the same time, users can tag the content in a collaborative way. The proposed system uses a search engine that combines both kinds of meta-data to locate the desired content. It will also provide browsing capabilities through the ontology concepts and the developed tags.     

Semantic representation of multimedia content: Knowledge representation and semantic indexing

In this paper we present a framework for unified, personalized access to heterogeneous multimedia content in distributed repositories. Focusing on semantic analysis of multimedia documents, metadata, user queries and user profiles, it contributes to the bridging of the gap between the semantic nature of user queries and raw multimedia documents. The proposed approach utilizes as input visual content analysis results, as well as analyzes and exploits associated textual annotation, in order to extract the underlying semantics, construct a semantic index and classify documents to topics, based on a unified knowledge and semantics representation model. It may then accept user queries, and, carrying out semantic interpretation and expansion, retrieve documents from the index and rank them according to user preferences, similarly to text retrieval. All processes are based on a novel semantic processing methodology, employing fuzzy algebra and principles of taxonomic knowledge representation. The first part of this work presented in this paper deals with data and knowledge models, manipulation of multimedia content annotations and semantic indexing, while the second part will continue on the use of the extracted semantic information for personalized retrieval.

Patterns of semantic relations to improve image content search

This paper reports on a study to explore how semantic relations can be used to expand a query for objects in an image. The study is part of a project with the overall objective to provide semantic annotation and search facilities for a virtual collection of art resources. In this study we used semantic relations from WordNet for 15 image content queries. The results show that, next to the hyponym/hypernym relation, the meronym/holonym (part-of) relation is particularly useful in query expansion. We identified a number of relation patterns that improve recall without jeopardising precision.