Annotated suffix tree as a way of text representation for information retrieval in text collections
A method for information retrieval based on annotated suffix trees (AST) is presented. The method is based on a string-to-document relevance score calculated using AST as well as fragment reverse indexing for improving performance. We developed a search engine based on the method. This engine is compared with some other popular text aggregating techniques: probabilistic latent semantic indexing (PLSA) and latent Dirichlet allocation (LDA). We used real data for computation experiments: an online store’s xml-catalogs and collections of web pages (both in Russian) and a real user’s queries from the Yandex. Wordstat service. As quality metrics, we used point quality estimations and graphical representations. Our AST-based method generally leads to results that are similar to those obtained by the other methods. However, in the case of inaccurate queries, AST-based results are superior. The speed of the AST-based method is slightly worse than the speed of the PLSA/LDA-based methods. Due to the observed correlation between the average query performing time and the string lengths at the AST construction phase, one can improve the performance of the algorithm by dividing the texts into smaller fragments at the preprocessing stage. However, the quality of search may suffer if the fragments are too short. Therefore, the applicability of annotated suffix tree techniques for text retrieval problems is demonstrated. Moreover, the AST-based method has significant advantages in the case of fuzzy search.
The article describes the implementation of the service, which allows to automate collection of structured information from unstructured web documents. The service unifies the solution for a variety of data domain by explicitly ontological description of a task. In addition, is not required change program code to increase the number of sources, because sources of information are also described by ontology.
This book constitutes the thoroughly refereed proceedings of the 8 th Russian Summer School on Information Retrieval, RuSSIR 2014, held in Nizhniy Novgorod, Russia, in August 2014.
The 14 papers presented were selected from various submissions. The papers focus on visualization for information retrieval along with other topics related to information retrieval.
he paper presents a framework for fast text analytics developed during the Texterra project. Texterra is a technology for multilingual text mining based on novel text processing methods that exploit knowledge extracted from user-generated content. It delivers a fast scalable solution for text mining without the expensive customization. Depending on use-cases Texterra could be utilized as a library, extendable framework or scalable cloudbased service. This paper describes details of the project, use-cases and results of evaluation for all developed tools. Texterra utilizes Wikipedia as a primary knowledge source to facilitate text mining in arbitrary documents (news, blogs, etc). We mine the graph of Wikipedia’s links to compute semantic relatedness between all concepts described in Wikipedia. As a result, we build a semantic graph with more than 5 million concepts. This graph is exploited to interpret meanings and relationships of terms in text documents. In spite of large size, Wikipedia doesn’t contain information about many domain-specific concepts. In order to increase applicability of the technology we developed several automatic knowledge extraction tools. These tools include systems for knowledge extraction from MediaWiki resources and Linked Data resources, as well as system for knowledge base extension with concepts described in arbitrary text documents using original information extraction techniques. In addition, utilization of information from Wikipedia allows easily extend Texterra for support of new Natural languages. The paper presents evaluation of Texterra applied for different text processing tasks (part-of-speech tagging, word sense disambiguation, keyword extraction and sentiment analysis) for English and Russian.
The present paper deals with word sense induction from lexical co-occurrence graphs. We construct such graphs on large Russian corpora and then apply the data to cluster the results of Mail.ru search according to meanings in the query. We compare different methods of performing such clustering and different source corpora. Models of applying distributional semantics to big linguistic data are described.
Proceedings of the 9th International Symposium on Intelligent Distributed Computing – IDC'2015, Guimarães, Portugal, October 2015
This volume contains the papers selected for presentation at the 2014 IEEE/WIC/ACM International Conference on Web Intelligence (WI'14), held as part of the 2014 Web Intelligence Congress (WIC'14) at the University of Warsaw, Warsaw, Poland, from 11 to 14 in August, 2014. The conference was sponsored and co-organized by the IEEE Computer Society, the Web Intelligence Consortium (WIC), Association for Computing Machinery (ACM), the University of Warsaw, Polish Mathematical Society and Warsaw University of Technology.
The series of Web Intelligence conferences was started in Japan in 2001. Since then, it has been held yearly in several countries, including: Canada, China, France, USA, Australia and Italy. It is recognized as the World's leading forum focusing on the role of Web Intelligence as one of the most important directions for scientific research and development of solutions that contribute to creation of the Knowledge-based Society. In 2014, WI visited Poland as a special event commemorating the 25th anniversary of the Web.
WI'14 received 242 paper submissions, in the areas of foundations of Web Intelligence, semantic aspects of Web Intelligence, World Wide Wisdom Web, Web search and recommendation, Web mining and warehousing, Human-Web interaction, as well as Web Intelligence technologies and applications. After a rigorous evaluation process, 85 papers were selected as regular contributions, giving an acceptance rate of 35.1%.
The first five sections of this volume include 40 regular contributions. Additionally, the first paper in the first section corresponds to one of WIC'14 keynotes. The last four sections of this volume contain 23 papers selected for oral presentations in WI'14 workshops. The remaining 45 regular contributions and 25 papers accepted to WI'14 special sessions are published in another volume of WI’14 proceedings.
Formal Concept Analysis (FCA) is a mathematical technique that has been extensively applied to Boolean data in knowledge discovery, information retrieval, web mining, etc. applications. During the past years, the research on extending FCA theory to cope with imprecise and incomplete information made significant progress. In this paper, we give a systematic overview of the more than 120 papers published between 2003 and 2011 on FCA with fuzzy attributes and rough FCA. We applied traditional FCA as a text-mining instrument to 1072 papers mentioning FCA in the abstract. These papers were formatted in pdf files and using a thesaurus with terms referring to research topics, we transformed them into concept lattices. These lattices were used to analyze and explore the most prominent research topics within the FCA with fuzzy attributes and rough FCA research communities. FCA turned out to be an ideal metatechnique for representing large volumes of unstructured texts.
Models for effective term extraction can depend on the type of a terminological resource under construction. In this paper we study term extraction models for realworking information-retrieval thesauri. The first thesaurus is the English version of EuroVoc thesaurus, the second one is the Russian Banking thesaurus. We study singleword and two-word term extraction separately to reveal the best features and feature combinations, compare best models for two thesauri. In particular, we found for this type of terminological resources the use of association measures does not improve the quality of two-word term extraction based on combining multiple features.
Today web spam is the one of the key problems of modern web search engines. In this paper we investigate the efficiency of various dimensionality reduction methods applying to the spam classifier of go.mail.ru search system. Effective utilization of such techniques can significantly increase the number of features and the quality of the classifier without loss of training and classification speed. We have conducted a series of experiments with PCA (Principal Component Analysis) и RP (Random Projection) dimensionality reduction methods. Unfortunately, these methods are shown to be ineffective applying to such issues, basically because of low-dimensional feature space. However this experiment led to the need for a detailed analysis of features, participating in the education process. For this analysis, we have chosen MRMR (Minimum Redundancy Maximum Relevance) criterion. Application of this criterion has allowed us to detect redundant features and estimate the efficiency of each of participating in education process feature. This research has allowed us significantly increase the quality of our web spam classifier without increasing number of features. This paper shows us all the efficiency of feature selection criterions in practice, and once again emphasizes the importance of a detailed analysis of the data and informative features, which are selected for training.
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traﬃc is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the ﬁnal node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a ﬁnite-dimensional system of diﬀerential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of diﬀerential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.
Event logs collected by modern information and technical systems usually contain enough data for automated process models discovery. A variety of algorithms was developed for process models discovery, conformance checking, log to model alignment, comparison of process models, etc., nevertheless a quick analysis of ad-hoc selected parts of a journal still have not get a full-fledged implementation. This paper describes an ROLAP-based method of multidimensional event logs storage for process mining. The result of the analysis of the journal is visualized as directed graph representing the union of all possible event sequences, ranked by their occurrence probability. Our implementation allows the analyst to discover process models for sublogs defined by ad-hoc selection of criteria and value of occurrence probability
The geographic information system (GIS) is based on the first and only Russian Imperial Census of 1897 and the First All-Union Census of the Soviet Union of 1926. The GIS features vector data (shapefiles) of allprovinces of the two states. For the 1897 census, there is information about linguistic, religious, and social estate groups. The part based on the 1926 census features nationality. Both shapefiles include information on gender, rural and urban population. The GIS allows for producing any necessary maps for individual studies of the period which require the administrative boundaries and demographic information.
Existing approaches suggest that IT strategy should be a reflection of business strategy. However, actually organisations do not often follow business strategy even if it is formally declared. In these conditions, IT strategy can be viewed not as a plan, but as an organisational shared view on the role of information systems. This approach generally reflects only a top-down perspective of IT strategy. So, it can be supplemented by a strategic behaviour pattern (i.e., more or less standard response to a changes that is formed as result of previous experience) to implement bottom-up approach. Two components that can help to establish effective reaction regarding new initiatives in IT are proposed here: model of IT-related decision making, and efficiency measurement metric to estimate maturity of business processes and appropriate IT. Usage of proposed tools is demonstrated in practical cases.