1,048 research outputs found
Proximity Full-Text Search with a Response Time Guarantee by Means of Additional Indexes
Full-text search engines are important tools for information retrieval. Term
proximity is an important factor in relevance score measurement. In a proximity
full-text search, we assume that a relevant document contains query terms near
each other, especially if the query terms are frequently occurring words. A
methodology for high-performance full-text query execution is discussed. We
build additional indexes to achieve better efficiency. For a word that occurs
in the text, we include in the indexes some information about nearby words.
What types of additional indexes do we use? How do we use them? These questions
are discussed in this work. We present the results of experiments showing that
the average time of search query execution is 44-45 times less than that
required when using ordinary inverted indexes.
This is a pre-print of a contribution "Veretennikov A.B. Proximity Full-Text
Search with a Response Time Guarantee by Means of Additional Indexes" published
in "Arai K., Kapoor S., Bhatia R. (eds) Intelligent Systems and Applications.
IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868"
published by Springer, Cham. The final authenticated version is available
online at: https://doi.org/10.1007/978-3-030-01054-6_66. The work was supported
by Act 211 Government of the Russian Federation, contract no 02.A03.21.0006.Comment: Alexander B. Veretennikov. Chair of Calculation Mathematics and
Computer Science, INSM. Ural Federal Universit
Toward Entity-Aware Search
As the Web has evolved into a data-rich repository, with the standard "page view," current search engines are becoming increasingly inadequate for a wide range of query tasks. While we often search for various data "entities" (e.g., phone number, paper PDF, date), today's engines only take us indirectly to pages. In my Ph.D. study, we focus on a novel type of Web search that is aware of data entities inside pages, a significant departure from traditional document retrieval. We study the various essential aspects of supporting entity-aware Web search. To begin with, we tackle the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking. We also report a prototype system built to show the initial promise of the proposal. Then, we aim at distilling and abstracting the essential computation requirements of entity search. From the dual views of reasoning--entity as input and entity as output, we propose a dual-inversion framework, with two indexing and partition schemes, towards efficient and scalable query processing. Further, to recognize more entity instances, we study the problem of entity synonym discovery through mining query log data. The results we obtained so far have shown clear promise of entity-aware search, in its usefulness, effectiveness, efficiency and scalability
Real-time Text Queries with Tunable Term Pair Indexes
Term proximity scoring is an established means in information retrieval for improving result quality of full-text queries. Integrating such proximity scores into efficient query processing, however, has not been equally well studied. Existing methods make use of precomputed lists of documents where tuples of terms, usually pairs, occur together, usually incurring a huge index size compared to term-only indexes. This paper introduces a joint framework for trading off index size and result quality, and provides optimization techniques for tuning precomputed indexes towards either maximal result quality or maximal query processing performance, given an upper bound for the index size. The framework allows to selectively materialize lists for pairs based on a query log to further reduce index size. Extensive experiments with two large text collections demonstrate runtime improvements of several orders of magnitude over existing text-based processing techniques with reasonable index sizes
Predicate-based indexing for desktop search
Google and other products have revolutionized the way we search for information. There are, however, still a number of research challenges. One challenge that arises specifically in desktop search is to exploit the structure and semantics of documents, as defined by the application program that generated the data (e.g., Word, Excel, or Outlook). The current generation of search products does not understand these structures and therefore often returns wrong results. This paper shows how today's search technology can be extended in order to take the specific semantics of certain structures into account. The key idea is to extend inverted file index structures with predicates which encode the circumstances under which certain keywords of a document become visible to a user. This paper provides a framework that allows to express the semantics of structures in documents and algorithms to construct enhanced, predicate-based indexes. Furthermore, this paper shows how keyword and phrase queries can be processed efficiently on such enhanced indexes. It is shown that the proposed approach has superior retrieval performance with regard to both recall and precision and has tolerable space and query running time overhead
On Region Algebras, XML Databases, and Information Retrieval
This paper describes some new ideas on developing a logical algebra for databases that manage textual data and support information retrieval functionality. We describe a first prototype of such a system
The Performance of Boolean Retrieval and Vector Space Model in Textual Information Retrieval
Boolean Retrieval (BR) and Vector Space Model (VSM) are very popular methods in information retrieval for creating an inverted index and querying terms. BR method searches the exact results of the textual information retrieval without ranking the results. VSM method searches and ranks the results. This study empirically compares the two methods. The research utilizes a sample of the corpus data obtained from Reuters. The experimental results show that the required times to produce an inverted index by the two methods are nearly the same. However, a difference exists on the querying index. The results also show that the numberof generated indexes, the sizes of the generated files, and the duration of reading and searching an index are proportional with the file number in the corpus and thefile size
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Indexing Proximity-based Dependencies for Information Retrieval
Research into term dependencies for information retrieval has demonstrated that dependency retrieval models are able to consistently improve retrieval effectiveness over bag-of-words models. However, the computation of term dependency statistics is a major efficiency bottleneck in the execution of these retrieval models. This thesis investigates the problem of improving the efficiency of dependency retrieval models without compromising the effectiveness benefits of the term dependency features.
Despite the large number of published comparisons between dependency models and bag-of-words approaches, there has been a lack of direct comparisons between alternate dependency models. We provide this comparison and investigate different types of proximity features. Several bi-term and many-term dependency models over a range of TREC collections, for both short (title) and long (description) queries, are compared to determine the strongest benchmark models. We observe that the weighted sequential dependence model is the most effective model studied. Additionally, we observe that there is some potential in many-term dependencies, but more selective methods are required to exploit these features.
We then investigate two novel index structures to directly index the proximitybased dependencies used in the sequential dependence model and weighted sequential dependence model. The frequent index and the sketch index data structures can both provide efficient access to collection and document level statistics for all indexed term dependencies, while minimizing space costs, relative to a full inverted index of term dependencies. We test whether these structures can improve retrieval efficiency without incurring large space requirements, or degrading retrieval effectiveness significantly. A secondary requirement is that each data structure must be able to be constructed for an input text collection in a scalable and distributed manner.
Based on the observation that the vast majority of term dependencies extracted from queries are relatively frequent in the collection, the “frequent” index of term dependencies omits data for infrequent term dependencies. The sketch index of term dependencies uses techniques from sketch data structures to store probabilisticallybounded estimates of the required statistics. We present analyses of these data structures that include construction and space costs, retrieval efficiency and investigation of any degradation of retrieval effectiveness.
Finally, we investigate the application of these data structures to the execution of the strongest performing dependency models identified. We compare the retrieval efficiency of each of these structures across two query processing algorithms, and across both short and long queries, using two large web collections. We observe that these newly proposed data structures allow the execution of queries considerably faster than when using positional indexes, and as fast as a full index of term dependencies, but with lowered storage overhead
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