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1.
Search engine results are often biased towards a certain aspect of a query or towards a certain meaning for ambiguous query terms. Diversification of search results offers a way to supply the user with a better balanced result set increasing the probability that a user finds at least one document suiting her information need. In this paper, we present a reranking approach based on minimizing variance of Web search results to improve topic coverage in the top-k results. We investigate two different document representations as the basis for reranking. Smoothed language models and topic models derived by Latent Dirichlet?allocation. To evaluate our approach we selected 240 queries from Wikipedia disambiguation pages. This provides us with ambiguous queries together with a community generated balanced representation of their (sub)topics. For these queries we crawled two major commercial search engines. In addition, we present a new evaluation strategy based on Kullback-Leibler divergence and Wikipedia. We evaluate this method using the TREC sub-topic evaluation on the one hand, and manually annotated query results on the other hand. Our results show that minimizing variance in search results by reranking relevant pages significantly improves topic coverage in the top-k results with respect to Wikipedia, and gives a good overview of the overall search result. Moreover, latent topic models achieve competitive diversification with significantly less reranking. Finally, our evaluation reveals that our automatic evaluation strategy using Kullback-Leibler divergence correlates well with α-nDCG scores used in manual evaluation efforts. 相似文献
2.
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
3.
This paper investigates the impact of three approaches to XML retrieval: using Zettair, a full-text information retrieval system; using eXist, a native XML database; and using a hybrid system that takes full article answers from Zettair and uses eXist to extract elements from those articles. For the content-only topics, we undertake a preliminary analysis of the INEX 2003 relevance assessments in order to identify the types of highly relevant document components. Further analysis identifies two complementary sub-cases of relevance assessments (General and Specific) and two categories of topics (Broad and Narrow). We develop a novel retrieval module that for a content-only topic utilises the information from the resulting answer list of a native XML database and dynamically determines the preferable units of retrieval, which we call Coherent Retrieval Elements. The results of our experiments show that—when each of the three systems is evaluated against different retrieval scenarios (such as different cases of relevance assessments, different topic categories and different choices of evaluation metrics)—the XML retrieval systems exhibit varying behaviour and the best performance can be reached for different values of the retrieval parameters. In the case of INEX 2003 relevance assessments for the content-only topics, our newly developed hybrid XML retrieval system is substantially more effective than either Zettair or eXist, and yields a robust and a very effective XML retrieval. 相似文献
4.
《Journal of Informetrics》2020,14(1):100999
The current study has two objectives. First, we explore the characteristics of biological entities, such as drugs, and their side effects using an author–entity pair bipartite network. Second, we use the constructed network to examine whether there are outstanding features of relations between drugs and side effects. We extracted drug and side effect names from 169,766 PubMed abstracts published between 2010 to 2014 and constructed author–entity pair bipartite networks after ambiguous author names were processed. We propose a new ranking algorithm that takes into consideration the characteristics of bipartite networks to identify top-ranked biological drug and side effect pairs. To investigate the relationship between a particular drug and a side effect, we compared the drug and side effect pairs obtained from the network containing both drug and side effect with those observed in SIDER, a human expert-curated database. The results of this study indicate that our approach was able to identify a wide range of patterns of drug–side effect relations from the perspective of authors’ research interests. Further, our approach also identified the unique characteristics of the relation of biomedical entities obtained using an author–entity pair bipartite network. 相似文献
5.
User queries to the Web tend to have more than one interpretation due to their ambiguity and other characteristics. How to diversify the ranking results to meet users’ various potential information needs has attracted considerable attention recently. This paper is aimed at mining the subtopics of a query either indirectly from the returned results of retrieval systems or directly from the query itself to diversify the search results. For the indirect subtopic mining approach, clustering the retrieval results and summarizing the content of clusters is investigated. In addition, labeling topic categories and concept tags on each returned document is explored. For the direct subtopic mining approach, several external resources, such as Wikipedia, Open Directory Project, search query logs, and the related search services of search engines, are consulted. Furthermore, we propose a diversified retrieval model to rank documents with respect to the mined subtopics for balancing relevance and diversity. Experiments are conducted on the ClueWeb09 dataset with the topics of the TREC09 and TREC10 Web Track diversity tasks. Experimental results show that the proposed subtopic-based diversification algorithm significantly outperforms the state-of-the-art models in the TREC09 and TREC10 Web Track diversity tasks. The best performance our proposed algorithm achieves is α-nDCG@5 0.307, IA-P@5 0.121, and α#-nDCG@5 0.214 on the TREC09, as well as α-nDCG@10 0.421, IA-P@10 0.201, and α#-nDCG@10 0.311 on the TREC10. The results conclude that the subtopic mining technique with the up-to-date users’ search query logs is the most effective way to generate the subtopics of a query, and the proposed subtopic-based diversification algorithm can select the documents covering various subtopics. 相似文献
6.
The deployment of Web 2.0 technologies has led to rapid growth of various opinions and reviews on the web, such as reviews
on products and opinions about people. Such content can be very useful to help people find interesting entities like products,
businesses and people based on their individual preferences or tradeoffs. Most existing work on leveraging opinionated content
has focused on integrating and summarizing opinions on entities to help users better digest all the opinions. In this paper,
we propose a different way of leveraging opinionated content, by directly ranking entities based on a user’s preferences.
Our idea is to represent each entity with the text of all the reviews of that entity. Given a user’s keyword query that expresses
the desired features of an entity, we can then rank all the candidate entities based on how well opinions on these entities
match the user’s preferences. We study several methods for solving this problem, including both standard text retrieval models
and some extensions of these models. Experiment results on ranking entities based on opinions in two different domains (hotels
and cars) show that the proposed extensions are effective and lead to improvement of ranking accuracy over the standard text
retrieval models for this task. 相似文献
7.
Bing Bai Jason Weston David Grangier Ronan Collobert Kunihiko Sadamasa Yanjun Qi Olivier Chapelle Kilian Weinberger 《Information Retrieval》2010,13(3):291-314
In this article we present Supervised Semantic Indexing which defines a class of nonlinear (quadratic) models that are discriminatively
trained to directly map from the word content in a query-document or document-document pair to a ranking score. Like Latent
Semantic Indexing (LSI), our models take account of correlations between words (synonymy, polysemy). However, unlike LSI our
models are trained from a supervised signal directly on the ranking task of interest, which we argue is the reason for our
superior results. As the query and target texts are modeled separately, our approach is easily generalized to different retrieval
tasks, such as cross-language retrieval or online advertising placement. Dealing with models on all pairs of words features
is computationally challenging. We propose several improvements to our basic model for addressing this issue, including low
rank (but diagonal preserving) representations, correlated feature hashing and sparsification. We provide an empirical study
of all these methods on retrieval tasks based on Wikipedia documents as well as an Internet advertisement task. We obtain
state-of-the-art performance while providing realistically scalable methods. 相似文献
8.
Query languages for XML such as XPath or XQuery support Boolean retrieval: a query result is a (possibly restructured) subset of XML elements or entire documents that satisfy the search conditions of the query. This search paradigm works for highly schematic XML data collections such as electronic catalogs. However, for searching information in open environments such as the Web or intranets of large corporations, ranked retrieval is more appropriate: a query result is a ranked list of XML elements in descending order of (estimated) relevance. Web search engines, which are based on the ranked retrieval paradigm, do, however, not consider the additional information and rich annotations provided by the structure of XML documents and their element names.This article presents the XXL search engine that supports relevance ranking on XML data. XXL is particularly geared for path queries with wildcards that can span multiple XML collections and contain both exact-match as well as semantic-similarity search conditions. In addition, ontological information and suitable index structures are used to improve the search efficiency and effectiveness. XXL is fully implemented as a suite of Java classes and servlets. Experiments in the context of the INEX benchmark demonstrate the efficiency of the XXL search engine and underline its effectiveness for ranked retrieval. 相似文献
9.
Ilaria Bordino Mounia Lalmas Yelena Mejova Olivier Van Laere 《Information Retrieval》2016,19(5):447-486
Search engines are increasingly going beyond the pure relevance of search results to entertain users with information items that are interesting and even surprising, albeit sometimes not fully related to their search intent. In this paper, we study this serendipitous search space in the context of entity search, which has recently emerged as a powerful paradigm for building semantically rich answers. Specifically, our work proposes to enhance an explorative search system that represents a large sample of Yahoo Answers as an entity network, with a result structuring that goes beyond ranked lists, using composite entity retrieval, which requires a bundling of the results. We propose and compare six bundling methods, which exploit topical categories, entity specializations, and sentiment, and go beyond simple entity clustering. Two large-scale crowd-sourced studies show that users find a bundled organization—especially based on the topical categories of the query entity—to be better at revealing the most useful results, as well as at organizing the results, helping to discover novel and interesting information, and promoting exploration. Finally, a third study of 30 simulated search tasks reveals the bundled search experience to be less frustrating and more rewarding, with more users willing to recommend it to others. 相似文献
10.
Query suggestions have become pervasive in modern web search, as a mechanism to guide users towards a better representation of their information need. In this article, we propose a ranking approach for producing effective query suggestions. In particular, we devise a structured representation of candidate suggestions mined from a query log that leverages evidence from other queries with a common session or a common click. This enriched representation not only helps overcome data sparsity for long-tail queries, but also leads to multiple ranking criteria, which we integrate as features for learning to rank query suggestions. To validate our approach, we build upon existing efforts for web search evaluation and propose a novel framework for the quantitative assessment of query suggestion effectiveness. Thorough experiments using publicly available data from the TREC Web track show that our approach provides effective suggestions for adhoc and diversity search. 相似文献
11.
TIJAH: Embracing IR Methods in XML Databases 总被引：1，自引：0，他引：1
This paper discusses our participation in INEX (the Initiative for the Evaluation of XML Retrieval) using the TIJAH XML-IR system. TIJAHs system design follows a standard layered database architecture, carefully separating the conceptual, logical and physical levels. At the conceptual level, we classify the INEX XPath-based query expressions into three different query patterns. For each pattern, we present its mapping into a query execution strategy. The logical layer exploits score region algebra (SRA) as the basis for query processing. We discuss the region operators used to select and manipulate XML document components. The logical algebra expressions are mapped into efficient relational algebra expressions over a physical representation of the XML document collection using the pre-post numbering scheme. The paper concludes with an analysis of experiments performed with the INEX test collection. 相似文献
12.
Gianluca Demartini Claudiu S. Firan Tereza Iofciu Ralf Krestel Wolfgang Nejdl 《Information Retrieval》2010,13(5):534-567
Entity Retrieval (ER)—in comparison to classical search—aims at finding individual entities instead of relevant documents.
Finding a list of entities requires therefore techniques different to classical search engines. In this paper, we present
a model to describe entities more formally and how an ER system can be build on top of it. We compare different approaches
designed for finding entities in Wikipedia and report on results using standard test collections. An analysis of entity-centric
queries reveals different aspects and problems related to ER and shows limitations of current systems performing ER with Wikipedia.
It also indicates which approaches are suitable for which kinds of queries. 相似文献
13.
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
14.
INEX与TREC是检索领域的两大检索系统评价平台,在检索技术发展迅速的今天依然保持强大生命力,在当今检索技术评价领域起着十分重要的作用。本篇文章通过对INEX与TREC的研究目标以及平台的构成要素包括三个方面:测试集、检索问题的构造、相关性评估的比较,找出INEX相对于TREC评测平台的创新及不同点,以便更加深入和全面地了解INEX的评测方法。 相似文献
15.
[目的/意义]研究利用维基百科条目的丰富信息来补充完善当前中文名称规范档附加信息不足、名称难以准确区分的问题。[方法/过程]探讨网络环境下个人名称规范档的部分缺陷和维基百科相应的优势,重点阐述维基百科的接口Media Wiki API,并从两个方面探讨实现个人名称规范档与维基百科的链接应用:一是实现从名称规范档到维基百科的链接,二是动态生成个人简介,为名称规范档提供必要的信息来源。[结果/结论]集成维基百科的资源实现中文名称规范档信息的自我完善,有效地建立中文人名名称规范档与维基百科的链接,有助于个人名称的识别与检索。 相似文献
16.
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
17.
Meriem Amina Zingla Chiraz Latiri Philippe Mulhem Catherine Berrut Yahya Slimani 《Information Retrieval》2018,21(4):337-367
Query expansion (QE) is an important process in information retrieval applications that improves the user query and helps in retrieving relevant results. In this paper, we introduce a hybrid query expansion model (HQE) that investigates how external resources can be combined to association rules mining and used to enhance expansion terms generation and selection. The HQE model can be processed in different configurations, starting from methods based on association rules and combining it with external knowledge. The HQE model handles the two main phases of a QE process, namely: the candidate terms generation phase and the selection phase. We propose for the first phase, statistical, semantic and conceptual methods to generate new related terms for a given query. For the second phase, we introduce a similarity measure, ESAC, based on the Explicit Semantic Analysis that computes the relatedness between a query and the set of candidate terms. The performance of the proposed HQE model is evaluated within two experimental validations. The first one addresses the tweet search task proposed by TREC Microblog Track 2011 and an ad-hoc IR task related to the hard topics of the TREC Robust 2004. The second experimental validation concerns the tweet contextualization task organized by INEX 2014. Global results highlighted the effectiveness of our HQE model and of association rules mining for QE combined with external resources. 相似文献
18.
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
19.
《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
20.
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