A heuristic hierarchical scheme for academic search and retrieval

We present PubSearch, a hybrid heuristic scheme for re-ranking academic papers retrieved from standard digital libraries such as the ACM Portal. The scheme is based on the hierarchical combination of a custom implementation of the term frequency heuristic, a time-depreciated citation score and a graph-theoretic computed score that relates the paper’s index terms with each other. We designed and developed a meta-search engine that submits user queries to standard digital repositories of academic publications and re-ranks the repository results using the hierarchical heuristic scheme. We evaluate our proposed re-ranking scheme via user feedback against the results of ACM Portal on a total of 58 different user queries specified from 15 different users. The results show that our proposed scheme significantly outperforms ACM Portal in terms of retrieval precision as measured by most common metrics in Information Retrieval including Normalized Discounted Cumulative Gain (NDCG), Expected Reciprocal Rank (ERR) as well as a newly introduced lexicographic rule (LEX) of ranking search results. In particular, PubSearch outperforms ACM Portal by more than 77% in terms of ERR, by more than 11% in terms of NDCG, and by more than 907.5% in terms of LEX. We also re-rank the top-10 results of a subset of the original 58 user queries produced by Google Scholar, Microsoft Academic Search, and ArnetMiner; the results show that PubSearch compares very well against these search engines as well. The proposed scheme can be easily plugged in any existing search engine for retrieval of academic publications.     

Quality and relevance of domain-specific search: A case study in mental health

When searching for health information, results quality can be judged against available scientific evidence: Do search engines return advice consistent with evidence based medicine? We compared the performance of domain-specific health and depression search engines against a general-purpose engine (Google) on both relevance of results and quality of advice. Over 101 queries, to which the term ‘depression’ was added if not already present, Google returned more relevant results than those of the domain-specific engines. However, over the 50 treatment-related queries, Google returned 70 pages recommending for or against a well studied treatment, of which 19 strongly disagreed with the scientific evidence. A domain-specific index of 4 sites selected by domain experts was only wrong in 5 of 50 recommendations. Analysis suggests a tension between relevance and quality. Indexing more pages can give a greater number of relevant results, but selective inclusion can give better quality.     

湖南衡东高湖话语音系统及音韵特点——兼论湘语、赣语过渡地带的语音特征

文章描写了赣语与湘语交接地带的衡东高湖话的语音系统及音韵特点,根据其音系特点及当地移民史得出,高湖话是属于受湘语影响较大的赣方言。     

试论搜索引擎的现状与发展

随着互联网的不断发展,搜索引擎现在已成为网络用户获取信息的一个不可或缺的检索工具.搜索引擎目前存在诸多问题,例如查全率和查准率、功能问题、作弊问题、安全性问题、信息更新问题、规范化问题等.本文就这些问题进行一些探讨.     

Document replication strategies for geographically distributed web search engines

Large-scale web search engines are composed of multiple data centers that are geographically distant to each other. Typically, a user query is processed in a data center that is geographically close to the origin of the query, over a replica of the entire web index. Compared to a centralized, single-center search engine, this architecture offers lower query response times as the network latencies between the users and data centers are reduced. However, it does not scale well with increasing index sizes and query traffic volumes because queries are evaluated on the entire web index, which has to be replicated and maintained in all data centers. As a remedy to this scalability problem, we propose a document replication framework in which documents are selectively replicated on data centers based on regional user interests. Within this framework, we propose three different document replication strategies, each optimizing a different objective: reducing the potential search quality loss, the average query response time, or the total query workload of the search system. For all three strategies, we consider two alternative types of capacity constraints on index sizes of data centers. Moreover, we investigate the performance impact of query forwarding and result caching. We evaluate our strategies via detailed simulations, using a large query log and a document collection obtained from the Yahoo! web search engine.     

基于用户信息需求的元搜索引擎的构建

针对当前网络搜索引擎未能较多地关注用户需求、查全率与查准率不高、彼此间不兼容的缺陷,提出建立基于用户需求的元搜索引擎,并分析其基本原理,给出其基本结构,讨论其运行机制和关键技术。指出基于用户需求的元搜索引擎可使网络信息资源在一定程度上结构化,实现网络信息资源的自组织,提高网络信息检索的查全率和查准率.     

Internet上的军事体育信息资源与检索

如何从Internet上获取军事体育信息资源,目前尚未见诸报导。探讨了Internet上军事体育信息资源的分布与检索问题,从军事体育官方网站、军事体育相关网站、网络数据库和搜索引擎等4个方面,阐述了Internet上军事体育信息资源的获取方法。     

Relevance-based entity selection for ad hoc retrieval

Recent developments have shown that entity-based models that rely on information from the knowledge graph can improve document retrieval performance. However, given the non-transitive nature of relatedness between entities on the knowledge graph, the use of semantic relatedness measures can lead to topic drift. To address this issue, we propose a relevance-based model for entity selection based on pseudo-relevance feedback, which is then used to systematically expand the input query leading to improved retrieval performance. We perform our experiments on the widely used TREC Web corpora and empirically show that our proposed approach to entity selection significantly improves ad hoc document retrieval compared to strong baselines. More concretely, the contributions of this work are as follows: (1) We introduce a graphical probability model that captures dependencies between entities within the query and documents. (2) We propose an unsupervised entity selection method based on the graphical model for query entity expansion and then for ad hoc retrieval. (3) We thoroughly evaluate our method and compare it with the state-of-the-art keyword and entity based retrieval methods. We demonstrate that the proposed retrieval model shows improved performance over all the other baselines on ClueWeb09B and ClueWeb12B, two widely used Web corpora, on the [email protected], and [email protected] metrics. We also show that the proposed method is most effective on the difficult queries. In addition, We compare our proposed entity selection with a state-of-the-art entity selection technique within the context of ad hoc retrieval using a basic query expansion method and illustrate that it provides more effective retrieval for all expansion weights and different number of expansion entities.     

大学生网络信息检索技巧研究

网络信息检索是大学生获取有效信息十分快捷的途径和手段,但由于缺乏相应的检索技能和技巧,大学生网络信息检索的效率相对不高,且存在一定误区。文章基于网络信息检索现状,重点从检索技巧方面对问题作了详细论述,以期为大学生获取所需信息提供帮助。     

The use and abuse of metatags

The web creates manyopportunities for encroachment on intellectualproperty including trademarks. Our principaltask in this paper is an investigation into anunusual form of such encroachment: theimproper use of metatags. A metatag is a pieceof HTML code that provides summary informationabout a web page. If used in an appropriatemanner, these metatags can play a legitimaterole in helping consumers locate information. But the ``keyword'' metatag is particularlysusceptible to manipulation. These tags can beeasily abused by web site creators anxious tobait search engines and bring scores ofvisitors to their sites. The law aboutmetatags is far from settled and many legalscholars are uncomfortable with the conclusionthat the unauthorized use of a trademark in ametatag represents infringement. How should weassess this practice known as ``spamdexing'' froma normative perspective? Is it commercial fairplay or something more sinister? We make thecase here that there are salient moral problemswith spamdexing since it exploits thereputational goodwill of trademark owners andconfuses consumers. It violates basic moralduties and it flouts the golden rule principle. Hence unauthorized use of a competitor'strademark in a metatag is not morallyacceptable.