Diversification Based Static Index Pruning - Application to Temporal Collections

Nowadays, web archives preserve the history of large portions of the web. As medias are shifting from printed to digital editions, accessing these huge information sources is drawing increasingly more attention from national and international institutions, as well as from the research community. These collections are intrinsically big, leading to index files that do not fit into the memory and an increase query response time. Decreasing the index size is a direct way to decrease this query response time. Static index pruning methods reduce the size of indexes by removing a part of the postings. In the context of web archives, it is necessary to remove postings while preserving the temporal diversity of the archive. None of the existing pruning approaches take (temporal) diversification into account. In this paper, we propose a diversification-based static index pruning method. It differs from the existing pruning approaches by integrating diversification within the pruning context. We aim at pruning the index while preserving retrieval effectiveness and diversity by pruning while maximizing a given IR evaluation metric like DCG. We show how to apply this approach in the context of web archives. Finally, we show on two collections that search effectiveness in temporal collections after pruning can be improved using our approach rather than diversity oblivious approaches

Static index pruning in web search engines: Combining term and document popularities with query views

Cataloged from PDF version of article.Static index pruning techniques permanently remove a presumably redundant part of an inverted file, to reduce the file size and query processing time. These techniques differ in deciding which parts of an index can be removed safely; that is, without changing the top-ranked query results. As defined in the literature, the query view of a document is the set of query terms that access to this particular document, that is, retrieves this document among its top results. In this paper, we first propose using query views to improve the quality of the top results compared against the original results. We incorporate query views in a number of static pruning strategies, namely term-centric, document-centric, term popularity based and document access popularity based approaches, and show that the new strategies considerably outperform their counterparts especially for the higher levels of pruning and for both disjunctive and conjunctive query processing. Additionally, we combine the notions of term and document access popularity to form new pruning strategies, and further extend these strategies with the query views. The new strategies improve the result quality especially for the conjunctive query processing, which is the default and most common search mode of a search engine

Stochastic Query Covering for Fast Approximate Document Retrieval

We design algorithms that, given a collection of documents and a distribution over user queries, return a small subset of the document collection in such a way that we can efficiently provide high-quality answers to user queries using only the selected subset. This approach has applications when space is a constraint or when the query-processing time increases significantly with the size of the collection. We study our algorithms through the lens of stochastic analysis and prove that even though they use only a small fraction of the entire collection, they can provide answers to most user queries, achieving a performance close to the optimal. To complement our theoretical findings, we experimentally show the versatility of our approach by considering two important cases in the context of Web search. In the first case, we favor the retrieval of documents that are relevant to the query, whereas in the second case we aim for document diversification. Both the theoretical and the experimental analysis provide strong evidence of the potential value of query covering in diverse application scenarios

Static index pruning in web search engines

Static index pruning techniques permanently remove a presumably redundant part of an inverted file, to reduce the file size and query processing time. These techniques differ in deciding which parts of an index can be removed safely; that is, without changing the top-ranked query results. As defined in the literature, the query view of a document is the set of query terms that access to this particular document, that is, retrieves this document among its top results. In this paper, we first propose using query views to improve the quality of the top results compared against the original results. We incorporate query views in a number of static pruning strategies, namely term-centric, document-centric, term popularity based and document access popularity based approaches, and show that the new strategies considerably outperform their counterparts especially for the higher levels of pruning and for both disjunctive and conjunctive query processing. Additionally, we combine the notions of term and document access popularity to form new pruning strategies, and further extend these strategies with the query views. The new strategies improve the result quality especially for the conjunctive query processing, which is the default and most common search mode of a search engine

Index ordering by query-independent measures

Conventional approaches to information retrieval search through all applicable entries in an inverted file for a particular collection in order to find those documents with the highest scores. For particularly large collections this may be extremely time consuming. A solution to this problem is to only search a limited amount of the collection at query-time, in order to speed up the retrieval process. In doing this we can also limit the loss in retrieval efficacy (in terms of accuracy of results). The way we achieve this is to firstly identify the most “important” documents within the collection, and sort documents within inverted file lists in order of this “importance”. In this way we limit the amount of information to be searched at query time by eliminating documents of lesser importance, which not only makes the search more efficient, but also limits loss in retrieval accuracy. Our experiments, carried out on the TREC Terabyte collection, report significant savings, in terms of number of postings examined, without significant loss of effectiveness when based on several measures of importance used in isolation, and in combination. Our results point to several ways in which the computation cost of searching large collections of documents can be significantly reduced

Improving the efficiency of search engines : strategies for focused crawling, searching, and index pruning

Ankara : The Department of Computer Engineering and the Instıtute of Engineering and Science of Bilkent University, 2009.Thesis (Ph. D.) -- Bilkent University, 2009.Includes bibliographical references leaves 157-169.Search engines are the primary means of retrieval for text data that is abundantly available on the Web. A standard search engine should carry out three fundamental tasks, namely; crawling the Web, indexing the crawled content, and finally processing the queries using the index. Devising efficient methods for these tasks is an important research topic. In this thesis, we introduce efficient strategies related to all three tasks involved in a search engine. Most of the proposed strategies are essentially applicable when a grouping of documents in its broadest sense (i.e., in terms of automatically obtained classes/clusters, or manually edited categories) is readily available or can be constructed in a feasible manner. Additionally, we also introduce static index pruning strategies that are based on the query views. For the crawling task, we propose a rule-based focused crawling strategy that exploits interclass rules among the document classes in a topic taxonomy. These rules capture the probability of having hyperlinks between two classes. The rulebased crawler can tunnel toward the on-topic pages by following a path of off-topic pages, and thus yields higher harvest rate for crawling on-topic pages. In the context of indexing and query processing tasks, we concentrate on conducting efficient search, again, using document groups; i.e., clusters or categories. In typical cluster-based retrieval (CBR), first, clusters that are most similar to a given free-text query are determined, and then documents from these clusters are selected to form the final ranked output. For efficient CBR, we first identify and evaluate some alternative query processing strategies. Next, we introduce a new index organization, so-called cluster-skipping inverted index structure (CS-IIS). It is shown that typical-CBR with CS-IIS outperforms previous CBR strategies (with an ordinary index) for a number of datasets and under varying search parameters. In this thesis, an enhanced version of CS-IIS is further proposed, in which all information to compute query-cluster similarities during query evaluation is stored. We introduce an incremental-CBR strategy that operates on top of this latter index structure, and demonstrate its search efficiency for different scenarios. Finally, we exploit query views that are obtained from the search engine query logs to tailor more effective static pruning techniques. This is also related to the indexing task involved in a search engine. In particular, query view approach is incorporated into a set of existing pruning strategies, as well as some new variants proposed by us. We show that query view based strategies significantly outperform the existing approaches in terms of the query output quality, for both disjunctive and conjunctive evaluation of queries.Altıngövde, İsmail SengörPh.D

Entity Linking for Queries by Searching Wikipedia Sentences

We present a simple yet effective approach for linking entities in queries. The key idea is to search sentences similar to a query from Wikipedia articles and directly use the human-annotated entities in the similar sentences as candidate entities for the query. Then, we employ a rich set of features, such as link-probability, context-matching, word embeddings, and relatedness among candidate entities as well as their related entities, to rank the candidates under a regression based framework. The advantages of our approach lie in two aspects, which contribute to the ranking process and final linking result. First, it can greatly reduce the number of candidate entities by filtering out irrelevant entities with the words in the query. Second, we can obtain the query sensitive prior probability in addition to the static link-probability derived from all Wikipedia articles. We conduct experiments on two benchmark datasets on entity linking for queries, namely the ERD14 dataset and the GERDAQ dataset. Experimental results show that our method outperforms state-of-the-art systems and yields 75.0% in F1 on the ERD14 dataset and 56.9% on the GERDAQ dataset