共查询到20条相似文献，搜索用时 31 毫秒
1.
针对基于关键字的传统搜索方法的不足,提出一种基于本体的信息搜索模型。就Web文本特点,介绍一种领域本体的半自动构建方法,利用基于领域本体的主题概念抽取方法完成语义标注,并实现在国防产品信息搜索中的应用。 相似文献
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Relevance feedback methods generally suffer from topic drift caused by word ambiguities and synonymous uses of words. Topic drift is an important issue in patent information retrieval as people tend to use different expressions describing similar concepts causing low precision and recall at the same time. Furthermore, failing to retrieve relevant patents to an application during the examination process may cause legal problems caused by granting an existing invention. A possible cause of topic drift is utilizing a relevance feedback-based search method. As a way to alleviate the inherent problem, we propose a novel query phrase expansion approach utilizing semantic annotations in Wikipedia pages, trying to enrich queries with phrases disambiguating the original query words. The idea was implemented for patent search where patents are classified into a hierarchy of categories, and the analyses of the experimental results showed not only the positive roles of phrases and words in retrieving additional relevant documents through query expansion but also their contributions to alleviating the query drift problem. More specifically, our query expansion method was compared against relevance-based language model, a state-of-the-art query expansion method, to show its superiority in terms of MAP on all levels of the classification hierarchy. 相似文献
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A structured document retrieval (SDR) system aims to minimize the effort users spend to locate relevant information by retrieving parts of documents. To evaluate the range of SDR tasks, from element to passage to tree retrieval, numerous task-specific measures have been proposed. This has resulted in SDR evaluation measures that cannot easily be compared with respect to each other and across tasks. In previous work, we defined the SDR task of tree retrieval where passage and element are special cases. In this paper, we look in greater detail into tree retrieval to identify the main components of SDR evaluation: relevance, navigation, and redundancy. Our goal is to evaluate SDR within a single probabilistic framework based on these components. This framework, called Extended Structural Relevance (ESR), calculates user expected gain in relevant information depending on whether it is seen via hits (relevant results retrieved), unseen via misses (relevant results not retrieved), or possibly seen via near-misses (relevant results accessed via navigation). We use these expectations as parameters to formulate evaluation measures for tree retrieval. We then demonstrate how existing task-specific measures, if viewed as tree retrieval, can be formulated, computed and compared using our framework. Finally, we experimentally validate ESR across a range of SDR tasks. 相似文献
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基于中文自然语言理解的知识检索模型 总被引：6，自引：0，他引：6
基于中文自然语言理解的知识检索模型的设计思路是:通过对用户提问及Web文档信息进行语义层次的自然语言处理,构建概念和概念网络,针对用户真实查询需求与概念网络及其映射的源文档进行推理匹配,然后对检索结果进行排序处理,提交给用户。图2。参考文献5。 相似文献
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Enterprise search is important, and the search quality has a direct impact on the productivity of an enterprise. Enterprise data contain both structured and unstructured information. Since these two types of information are complementary and the structured information such as relational databases is designed based on ER (entity-relationship) models, there is a rich body of information about entities in enterprise data. As a result, many information needs of enterprise search center around entities. For example, a user may formulate a query describing a problem that she encounters with an entity, e.g., the web browser, and want to retrieve relevant documents to solve the problem. Intuitively, information related to the entities mentioned in the query, such as related entities and their relations, would be useful to reformulate the query and improve the retrieval performance. However, most existing studies on query expansion are term-centric. In this paper, we propose a novel entity-centric query expansion framework for enterprise search. Specifically, given a query containing entities, we first utilize both unstructured and structured information to find entities that are related to the ones in the query. We then discuss how to adapt existing feedback methods to use the related entities and their relations to improve search quality. Experimental results over two real-world enterprise collections show that the proposed entity-centric query expansion strategies are more effective and robust to improve the search performance than the state-of-the-art pseudo feedback methods for long natural language-like queries with entities. Moreover, results over a TREC ad hoc retrieval collections show that the proposed methods can also work well for short keyword queries in the general search domain. 相似文献
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An information retrieval (IR) system can often fail to retrieve relevant documents due to the incomplete specification of information need in the user’s query. Pseudo-relevance feedback (PRF) aims to improve IR effectiveness by exploiting potentially relevant aspects of the information need present in the documents retrieved in an initial search. Standard PRF approaches utilize the information contained in these top ranked documents from the initial search with the assumption that documents as a whole are relevant to the information need. However, in practice, documents are often multi-topical where only a portion of the documents may be relevant to the query. In this situation, exploitation of the topical composition of the top ranked documents, estimated with statistical topic modeling based approaches, can potentially be a useful cue to improve PRF effectiveness. The key idea behind our PRF method is to use the term-topic and the document-topic distributions obtained from topic modeling over the set of top ranked documents to re-rank the initially retrieved documents. The objective is to improve the ranks of documents that are primarily composed of the relevant topics expressed in the information need of the query. Our RF model can further be improved by making use of non-parametric topic modeling, where the number of topics can grow according to the document contents, thus giving the RF model the capability to adjust the number of topics based on the content of the top ranked documents. We empirically validate our topic model based RF approach on two document collections of diverse length and topical composition characteristics: (1) ad-hoc retrieval using the TREC 6-8 and the TREC Robust ’04 dataset, and (2) tweet retrieval using the TREC Microblog ’11 dataset. Results indicate that our proposed approach increases MAP by up to 9% in comparison to the results obtained with an LDA based language model (for initial retrieval) coupled with the relevance model (for feedback). Moreover, the non-parametric version of our proposed approach is shown to be more effective than its parametric counterpart due to its advantage of adapting the number of topics, improving results by up to 5.6% of MAP compared to the parametric version. 相似文献
8.
David E. Losada 《Information Retrieval》2010,13(5):485-506
The retrieval of sentences that are relevant to a given information need is a challenging passage retrieval task. In this
context, the well-known vocabulary mismatch problem arises severely because of the fine granularity of the task. Short queries,
which are usually the rule rather than the exception, aggravate the problem. Consequently, effective sentence retrieval methods
tend to apply some form of query expansion, usually based on pseudo-relevance feedback. Nevertheless, there are no extensive
studies comparing different statistical expansion strategies for sentence retrieval. In this work we study thoroughly the
effect of distinct statistical expansion methods on sentence retrieval. We start from a set of retrieved documents in which
relevant sentences have to be found. In our experiments different term selection strategies are evaluated and we provide empirical
evidence to show that expansion before sentence retrieval yields competitive performance. This is particularly novel because expansion for sentence retrieval is often done after sentence retrieval (i.e. expansion terms are mined from a ranked set of sentences) and there are no comparative results available between both
types of expansion. Furthermore, this comparison is particularly valuable because there are important implications in time
efficiency. We also carefully analyze expansion on weak and strong queries and demonstrate clearly that expanding queries
before sentence retrieval is not only more convenient for efficiency purposes, but also more effective when handling poor
queries. 相似文献
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GoPubMed是基于PubMed的语义智能搜索引擎,Quertle是以PubMed为主要数据源的语义智能搜索引擎,两者都是基于本体向未来语义检索发展的尝试。GoPubMed最大的检索特色是对检索结果的分类统计和可视化。Quertle在本体技术和自然语言处理技术的支撑下,形成了Power TermTM检索,能识别单词大写时的特定含义,将检索提问与关系网进行匹配查找获得高度相关的检索结果。 相似文献
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As the volume and variety of information sources continues to grow, there is increasing difficulty with respect to obtaining
information that accurately matches user information needs. A number of factors affect information retrieval effectiveness
(the accuracy of matching user information needs against the retrieved information). First, users often do not present search
queries in the form that optimally represents their information need. Second, the measure of a document’s relevance is often
highly subjective between different users. Third, information sources might contain heterogeneous documents, in multiple formats
and the representation of documents is not unified. This paper discusses an approach for improvement of information retrieval
effectiveness from document databases. It is proposed that retrieval effectiveness can be improved by applying computational
intelligence techniques for modelling information needs, through interactive reinforcement learning. The method combines qualitative
(subjective) user relevance feedback with quantitative (algorithmic) measures of the relevance of retrieved documents. An
information retrieval is developed whose retrieval effectiveness is evaluated using traditional precision and recall. 相似文献
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旨在实现对给定的实体对象集匹配出尽可能宽的实体对象面,以帮助用户快速找到相关信息,尤其是那些需动态整合的特定领域的语义关联信息。分析Web文档中的实体对象结构及其关系,并借助Schema.org方案中的语义分类思想,提出构建具有语义特性的实体对象数据库建设方案。基于该数据库提出一个自适应的实体对象检索框架,该框架能对用户的查询意图进行分析并进行语义分类,形成一条条涵盖实体对象的查询语句,接着“智能地”选择、执行某些具有优先权的查询语句以匹配出那些保存在事实数据库中的相关实体对象。本研究旨在一定程度上实现“滚雪球”式的高效检索思想,满足智能检索技术的需求,促进以实体对象作为研究对象的情报理论研究工作的开展,并为智能情报检索技术的应用规划提供有用参考。 相似文献
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基于领域本体的数字图书馆检索结果动态组织方法研究 总被引：1，自引：1，他引：0
在对现有数字图书馆检索结果的组织方法进行分析的基础上,从忠实于用户提问的角度,提出基于领域本体的检索结果动态组织方法。基本解决思路是将文献的标识与用户的提问进行有效地对接,即以用户提问为基础构造提问模型,并基于检索结果构造标识模型,将提问模型与标识模型在语义层面通过领域本体进行映射,从而实现文献标识与用户提问在语义层面的互通,最终以用户提问的语义方式来展现检索结果。 相似文献
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依据语义检索的特征和文本概念的挖掘,通过楚辞研究数据库的语义实践,提出一种以本体知识库建设为核心,由本体开发、资源管理、检索服务三层架构组成,融语义词典、知识地图、跨库查询和专题搜索为一体的个性化关联语义检索模型,力图使当前的语义检索研究跳出实验的框架,促进相关领域文献知识的组织开发与检索利用。 相似文献
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Most recent document standards like XML rely on structured representations. On the other hand, current information retrieval systems have been developed for flat document representations and cannot be easily extended to cope with more complex document types. The design of such systems is still an open problem. We present a new model for structured document retrieval which allows computing scores of document parts. This model is based on Bayesian networks whose conditional probabilities are learnt from a labelled collection of structured documents—which is composed of documents, queries and their associated assessments. Training these models is a complex machine learning task and is not standard. This is the focus of the paper: we propose here to train the structured Bayesian Network model using a cross-entropy training criterion. Results are presented on the INEX corpus of XML documents. 相似文献
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针对常用信息检索模型存在的两大不足——检索提问与内容表达上的语义缺失与结果返回形式上的单文档局限,提出相应的解决方案,在此基础上进一步提出基于本体的族式返回检索模型,并就该模型中的部分关键问题,如族式返回、查询与文档表示以及语义匹配等进行讨论。 相似文献
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文本检索的潜在语义索引法初探 总被引：5，自引：0，他引：5
传统的文本检索方式是基于提问集合和文本集合的单纯语词匹配检索,然而这并不能解决检索实践过程中存在的同义和多义问题。文章阐述了文本检索的潜在语义索引法的原理并通过实验来验证潜在语义索引可以用来解决同义和多义问题,完善检索系统的性能。 相似文献
19.
基于个体概念语义关系的微内容发现研究 总被引：1，自引：0，他引：1
传统的超文本标记语言只能显示而不能使计算机理解页面信息,导致传统的基于关键词的检索工具的查准率、查全率和智能化程度都较低.虽然语义网可以解决计算机对页面信息的理解问题,但以往基于本体的语义匹配算法通常是面向类概念的,在精度上不能满足微内容的发现需求.本文面向微内容检索应用,在分析了个体概念的语义特征的基础上,提出了基于语义关系的个体匹配规则、索引结构和相应的语义检索算法.最后,通过实验验证,证明了基于个体语义关系的聚合机制具有更高的聚合细腻度,所提出的语义检索算法在面向微内容的检索应用中是有效的. 相似文献
20.
Multilingual information retrieval is generally understood to mean the retrieval of relevant information in multiple target
languages in response to a user query in a single source language. In a multilingual federated search environment, different
information sources contain documents in different languages. A general search strategy in multilingual federated search environments
is to translate the user query to each language of the information sources and run a monolingual search in each information
source. It is then necessary to obtain a single ranked document list by merging the individual ranked lists from the information
sources that are in different languages. This is known as the results merging problem for multilingual information retrieval.
Previous research has shown that the simple approach of normalizing source-specific document scores is not effective. On the
other side, a more effective merging method was proposed to download and translate all retrieved documents into the source
language and generate the final ranked list by running a monolingual search in the search client. The latter method is more
effective but is associated with a large amount of online communication and computation costs. This paper proposes an effective
and efficient approach for the results merging task of multilingual ranked lists. Particularly, it downloads only a small
number of documents from the individual ranked lists of each user query to calculate comparable document scores by utilizing
both the query-based translation method and the document-based translation method. Then, query-specific and source-specific
transformation models can be trained for individual ranked lists by using the information of these downloaded documents. These
transformation models are used to estimate comparable document scores for all retrieved documents and thus the documents can
be sorted into a final ranked list. This merging approach is efficient as only a subset of the retrieved documents are downloaded
and translated online. Furthermore, an extensive set of experiments on the Cross-Language Evaluation Forum (CLEF) () data has demonstrated the effectiveness of the query-specific and source-specific results merging algorithm against other
alternatives. The new research in this paper proposes different variants of the query-specific and source-specific results
merging algorithm with different transformation models. This paper also provides thorough experimental results as well as
detailed analysis. All of the work substantially extends the preliminary research in (Si and Callan, in: Peters (ed.) Results
of the cross-language evaluation forum-CLEF 2005, 2005).
相似文献
| Hao YuanEmail: |