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1.
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
2.
Caio Moura Daoud Edleno Silva de Moura David Fernandes Altigran Soares da Silva Cristian Rossi Andre Carvalho 《Information Retrieval》2017,20(3):292-316
In this paper, we present Waves, a novel document-at-a-time algorithm for fast computing of top-k query results in search systems. The Waves algorithm uses multi-tier indexes for processing queries. It performs successive tentative evaluations of results which we call waves. Each wave traverses the index, starting from a specific tier level i. Each wave i may insert only those documents that occur in that tier level into the answer. After processing a wave, the algorithm checks whether the answer achieved might be changed by successive waves or not. A new wave is started only if it has a chance of changing the top-k scores. We show through experiments that such lazy query processing strategy results in smaller query processing times when compared to previous approaches proposed in the literature. We present experiments to compare Waves’ performance to the state-of-the-art document-at-a-time query processing methods that preserve top-k results and show scenarios where the method can be a good alternative algorithm for computing top-k results. 相似文献
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4.
Despite a clear improvement of search and retrieval temporal applications, current search engines are still mostly unaware of the temporal dimension. Indeed, in most cases, systems are limited to offering the user the chance to restrict the search to a particular time period or to simply rely on an explicitly specified time span. If the user is not explicit in his/her search intents (e.g., “philip seymour hoffman”) search engines may likely fail to present an overall historic perspective of the topic. In most such cases, they are limited to retrieving the most recent results. One possible solution to this shortcoming is to understand the different time periods of the query. In this context, most state-of-the-art methodologies consider any occurrence of temporal expressions in web documents and other web data as equally relevant to an implicit time sensitive query. To approach this problem in a more adequate manner, we propose in this paper the detection of relevant temporal expressions to the query. Unlike previous metadata and query log-based approaches, we show how to achieve this goal based on information extracted from document content. However, instead of simply focusing on the detection of the most obvious date we are also interested in retrieving the set of dates that are relevant to the query. Towards this goal, we define a general similarity measure that makes use of co-occurrences of words and years based on corpus statistics and a classification methodology that is able to identify the set of top relevant dates for a given implicit time sensitive query, while filtering out the non-relevant ones. Through extensive experimental evaluation, we mean to demonstrate that our approach offers promising results in the field of temporal information retrieval (T-IR), as demonstrated by the experiments conducted over several baselines on web corpora collections. 相似文献
5.
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
6.
在高维空间中k最近邻搜索(KNNS)应用非常广泛,但是目前很多KNNS算法都根据欧氏距离对数据进行索引和搜索,不适合采用角相似性的应用.本文提出一种基于角相似性的k最近邻搜索算法(AS-KNNS).该算法先提出基于角相似性的数据索引结构(AS-Index),参照一条中心线和一条参照线,将数据以系列壳-超圆锥体方式进行组织并分别线性存储;然后确定查询对象的空间位置,有效确定一个以从原点到查询对象的直线为中心线的超圆锥体并在其中进行搜索.实验结果表明,AS-KNNS算法较其他k最近邻搜索算法有更好的性能. 相似文献
7.
Users are often faced with complex information needs that are not easily represented as a single query. With current technology,
the burden of issuing these individual queries, analysing retrieved documents for relevance, as well as aggregating results
falls upon the time-poor and informationally overloaded user. Aggregated search techniques represent the new generation of
search applications that endeavour to help users perform these complex tasks. However, the way in which different data types
are combined in current aggregated search applications is often performed using static hard-coded structures. We suggest that
a useful alternative is to marry techniques from natural language generation, such as text planning and summarisation, in
order to dynamically determine the best organisation of retrieved information. These organisations can be motivated by linguistic
theories that consider issues such as the role that the information plays to facilitate a task, and the relationships between
different pieces of information. With reference to a discourse strategy, it is possible to draw on several data sources automatically
to generate a useful, focused, and coherent answer. We focus on exploring the parallels between aggregated search and natural
language generation in the hope that the fields can be mutually informed, leading to further advances in the way search technologies
can better serve the user. These issues are discussed and presented with examples of existing systems across different domains. 相似文献
8.
Search effectiveness metrics are used to evaluate the quality of the answer lists returned by search services, usually based
on a set of relevance judgments. One plausible way of calculating an effectiveness score for a system run is to compute the
inner-product of the run’s relevance vector and a “utility” vector, where the ith element in the utility vector represents the relative benefit obtained by the user of the system if they encounter a relevant
document at depth i in the ranking. This paper uses such a framework to examine the user behavior patterns—and hence utility weightings—that
can be inferred from a web query log. We describe a process for extrapolating user observations from query log clickthroughs,
and employ this user model to measure the quality of effectiveness weighting distributions. Our results show that for measures
with static distributions (that is, utility weighting schemes for which the weight vector is independent of the relevance
vector), the geometric weighting model employed in the rank-biased precision effectiveness metric offers the closest fit to
the user observation model. In addition, using past TREC data as to indicate likelihood of relevance, we also show that the
distributions employed in the BPref and MRR metrics are the best fit out of the measures for which static distributions do
not exist. 相似文献
9.
[目的/意义]实现学术查询意图的自动识别,提高学术搜索引擎的效率。[方法/过程]结合已有查询意图特征和学术搜索特点,从基本信息、特定关键词、实体和出现频率4个层面对查询表达式进行特征构造,运用Naive Bayes、Logistic回归、SVM、Random Forest四种分类算法进行查询意图自动识别的预实验,计算不同方法的准确率、召回率和F值。提出了一种将Logistic回归算法所预测的识别结果扩展到大规模数据集、提取"关键词类"特征的方法构建学术查询意图识别的深度学习两层分类器。[结果/结论]两层分类器的宏平均F1值为0.651,优于其他算法,能够有效平衡不同学术查询意图的类别准确率与召回率效果。两层分类器在学术探索类的效果最好,F1值为0.783。 相似文献
10.
Luca Soldaini Andrew Yates Elad Yom-Tov Ophir Frieder Nazli Goharian 《Information Retrieval》2016,19(1-2):149-173
The majority of Internet users search for medical information online; however, many do not have an adequate medical vocabulary. Users might have difficulties finding the most authoritative and useful information because they are unfamiliar with the appropriate medical expressions describing their condition; consequently, they are unable to adequately satisfy their information need. We investigate the utility of bridging the gap between layperson and expert vocabularies; our approach adds the most appropriate expert expression to queries submitted by users, a task we call query clarification. We evaluated the impact of query clarification. Using three different synonym mappings and conducting two task-based retrieval studies, users were asked to answer medically-related questions using interleaved results from a major search engine. Our results show that the proposed system was preferred by users and helped them answer medical concerns correctly more often, with up to a 7 % increase in correct answers over an unmodified query. Finally, we introduce a supervised classifier to select the most appropriate synonym mapping for each query, which further increased the fraction of correct answers (12 %). 相似文献
11.
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
12.
Olivier Chapelle Shihao Ji Ciya Liao Emre Velipasaoglu Larry Lai Su-Lin Wu 《Information Retrieval》2011,14(6):572-592
We study the problem of web search result diversification in the case where intent based relevance scores are available. A
diversified search result will hopefully satisfy the information need of user-L.s who may have different intents. In this
context, we first analyze the properties of an intent-based metric, ERR-IA, to measure relevance and diversity altogether.
We argue that this is a better metric than some previously proposed intent aware metrics and show that it has a better correlation
with abandonment rate. We then propose an algorithm to rerank web search results based on optimizing an objective function
corresponding to this metric and evaluate it on shopping related queries. 相似文献
13.
信息检索扩展技术研究 总被引：1，自引：0，他引：1
本文针对信息检索在查询扩展方面的不足,提出了一种结合本体理论和用户相关反馈技术的查询扩展方法。以FirteX作为检索平台, 选取WordNet作为本体扩展资源来验证本文所提出的查询扩展算法,实现结果表明该方法比基于余弦相似性的查询扩展方法在平均查全率、平均查准率方面有更大的优点。 相似文献
14.
针对现有元数据索引方法因其固有的缺陷而难以应用于复杂的语义网络,提出一种新的基于语义的元数
据索引查询方法。该方法以ＲＤＦ图为数据模型,在路径索引的基础上,为元数据建立索引,并通过路径表达式实现元数据的查询。它能有效的促进领域知识的共享和语义表达,提高元数据的检索效率和查准率,为用户和应用提供语义查询和信息汇集能力。 相似文献
15.
Patent prior art search is a type of search in the patent domain where documents are searched for that describe the work previously carried out related to a patent application. The goal of this search is to check whether the idea in the patent application is novel. Vocabulary mismatch is one of the main problems of patent retrieval which results in low retrievability of similar documents for a given patent application. In this paper we show how the term distribution of the cited documents in an initially retrieved ranked list can be used to address the vocabulary mismatch. We propose a method for query modeling estimation which utilizes the citation links in a pseudo relevance feedback set. We first build a topic dependent citation graph, starting from the initially retrieved set of feedback documents and utilizing citation links of feedback documents to expand the set. We identify the important documents in the topic dependent citation graph using a citation analysis measure. We then use the term distribution of the documents in the citation graph to estimate a query model by identifying the distinguishing terms and their respective weights. We then use these terms to expand our original query. We use CLEF-IP 2011 collection to evaluate the effectiveness of our query modeling approach for prior art search. We also study the influence of different parameters on the performance of the proposed method. The experimental results demonstrate that the proposed approach significantly improves the recall over a state-of-the-art baseline which uses the link-based structure of the citation graph but not the term distribution of the cited documents. 相似文献
16.
Rocchio's similarity-based Relevance feedback algorithm, one of the most important query reformation methods in information retrieval, is essentially an adaptive supervised learning algorithm from examples. In spite of its popularity in various applications there is little rigorous analysis of its learning complexity in literature. In this paper we show that in the binary vector space model, if the initial query vector is 0, then for any of the four typical similarities (inner product, dice coefficient, cosine coefficient, and Jaccard coefficient), Rocchio's similarity-based relevance feedback algorithm makes at least n mistakes when used to search for a collection of documents represented by a monotone disjunction of at most k relevant features (or terms) over the n-dimensional binary vector space {0, 1}
n
. When an arbitrary initial query vector in {0, 1}
n
is used, it makes at least (n + k – 3)/2 mistakes to search for the same collection of documents. The linear lower bounds are independent of the choices of the threshold and coefficients that the algorithm may use in updating its query vector and making its classification. 相似文献
17.
《Microprocessing and Microprogramming》1995,41(3):227-244
Visual queries based on schema graphs simplify access to databases for technical and non-technical users. Unlike relational databases, in object-oriented databases, the basic entity in a query, i.e. a class, is frequently considered as a compound of several entities to which the query operations may apply, which causes the deficiency in describing an entity of designation. In this paper, we propose a visual query language object query diagram (OQD) for object-oriented databases, where a class is decomposed into a number of object sets, each of which is a set of values of one of the attributes of the other classes. By representing each class and object sets in the class using the well-known Venn diagram in a query, OQD explicitly presents all the entities to which the operations in a query can apply. We describe the syntax and semantics of OQD through a number of illustrative examples. 相似文献
18.
Query recommendation has long been considered a key feature of search engines, which can improve users’ search experience by providing useful query suggestions for their search tasks. Most existing approaches on query recommendation aim to recommend relevant queries, i.e., alternative queries similar to a user’s initial query. However, the ultimate goal of query recommendation is to assist users to reformulate queries so that they can accomplish their search task successfully and quickly. Only considering relevance in query recommendation is apparently not directly toward this goal. In this paper, we argue that it is more important to directly recommend queries with high utility, i.e., queries that can better satisfy users’ information needs. For this purpose, we attempt to infer query utility from users’ sequential search behaviors recorded in their search sessions. Specifically, we propose a dynamic Bayesian network, referred as Query Utility Model (QUM), to capture query utility by simultaneously modeling users’ reformulation and click behaviors. We then recommend queries with high utility to help users better accomplish their search tasks. We empirically evaluated the performance of our approach on a publicly released query log by comparing with the state-of-the-art methods. The experimental results show that, by recommending high utility queries, our approach is far more effective in helping users find relevant search results and thus satisfying their information needs. 相似文献
19.
一个构造良好的查询是信息检索质量的基本保证,语义查询扩展技术解决了传统信息检索系统不能很好理解用户查询意图的问题,在提高检索查全率的同时保证了检索准确率。本文以查询关键字之间的语义关联为切入点,辅以隐式反馈技术获取消歧上下文,以WordNet本体库和WordNet Domains扩展库作为消歧数据源,使用基于局部上下文和基于图论的两类无导词义消歧方法进行查询关键字到本体概念的映射,最后基于概念词汇关联完成基于语义的查询扩展。综合WordNet本体库和WordNet Domains扩展库中的各项知识源对查询词义进行判定,保证了词义消歧的精度;采用无导词义消歧实现查询词义的快速判定,保证了信息检索的实时性;根据查询关键词的多寡分别提出两类消歧方法,满足了各种查询需求。 相似文献
20.
《Microprocessing and Microprogramming》1994,40(10-12):905-908
The query language OVAL which is intended for the integration with the database programming language based on C++ is proposed in this paper. The work addresses the impedance mismatch problem [1] between the syntax and the semantics of the programming and query language. The query language OVAL is based on the functional query language FQL [3] extending it for the manipulation of complex objects. The salient features of the OVAL query language are: (i) functional nature of the query language, which makes the language suitable for the integration with the procedural programming languages and provides modular style of query definition, (ii) the use of schema information for expressing queries and (iii) recursive evaluation of the algebraic operations on set structured complex objects. 相似文献