Refining a Taxonomy by Using Annotated Suffix Trees and Wikipedia Resources
A step-by-step approach to taxonomy construction is presented. On the first step, the upper layer frame of taxonomy is built manually according to educational materials. On the next steps, the frame is refined at a chosen topic using the Wikipedia category tree and articles, both cleaned of noise. Our main tool in this is a naturally defined string-to-text relevance score, based on annotated suffix trees. The relevance scoring is used at several tasks: (1) cleaning the Wikipedia tree or page set of noise; (2) allocating Wikipedia categories to taxonomy topics; (3) deciding whether an allocated category should be included as a child to the taxonomy topic, etc. The resulting fragment of taxonomy consists of three parts: the manually set upper layer topic, the adopted part of the Wikipedia category tree and Wikipedia articles as leaves. Every leaf is assigned a set of so-called descriptors; these are phrases explaining aspects of the leaf topic. The method is illustrated by its application to two domains in the area of Mathematics: (a) “Probability theory and mathematical statistics”, (b) “Numerical mathematics” (both in Russian).
The paper defines an annotated suffix tree (AST) - a data structure used to calculate and store the frequencies of all the fragments of the given string or a collection of strings. The AST is associated with a string to text scoring, which takes all fuzzy matches into account. We show how the AST and the AST scoring can be used for Natural Language Processing tasks. Copyright © by the paper's authors. Copying only for private and academic purposes.
We define a most specific generalization of a fuzzy set of topics assigned to leaves of the rooted tree of a domain taxonomy. This generalization lifts the set to its “head subject” node in the higher ranks of the taxonomy tree. The head subject is supposed to “tightly” cover the query set, possibly bringing in some errors referred to as “gaps” and “offshoots”. Our method, ParGenFS, globally minimizes a penalty function combining the numbers of head subjects and gaps and offshoots, differently weighted. Two applications are considered: (1) analysis of tendencies of research in Data Science; (2) audience extending for programmatic targeted advertising online. The former involves a taxonomy of Data Science derived from the celebrated ACM Computing Classification System 2012. We derive fuzzy clusters of leaf topics in learning, retrieval and clustering. The head subjects of these clusters inform us of some general tendencies of the research. The latter involves publicly available IAB Tech Lab Content Taxonomy.
A two-step approach to taxonomy construction is presented. On the first step the frame of taxonomy is built manually according to some representative educational materials. On the second step, the frame is refined using the Wikipedia category tree and articles. Since the structure of Wikipedia is rather noisy, a procedure to clear the Wikipedia category tree is suggested. A string-to-text relevance score, based on annotated suffix trees, is used several times to 1) clear the Wikipedia data from noise; 2) to assign Wikipedia categories to taxonomy topics; 3) to choose whether the category should be assigned to the taxonomy topic or stay on intermediate levels. The resulting taxonomy consists of three parts: the manully set upper levels, the adopted Wikipedia category tree and the Wikipedia articles as leaves.Also, a set of so-called descriptors is assigned to every leaf; these are phrases explaining aspects of the leaf topic. The method is illustrated by its application to two domains: a) Probability theory and mathematical statistics, b) “Numerical analysis” (both in Russian).
This paper proposes a novel method, referred to as ParGenFS, for finding a most specific generalization of a query set represented by a fuzzy set of topics assigned to leaves of the rooted tree of a taxonomy. The query set is generalized by “lifting” it to one or more “head subjects” in the higher ranks of the taxonomy. The head subjects should cover the query set, with the possible addition of some “gaps”, taxonomy nodes covered by the head subject but irrelevant to the query set. To decrease the numbers of gaps, we admit some “offshoots”, nodes belonging to the query set but not covered by the head subject. The method globally minimizes the total number of head subjects, gaps and offshoots, each suitably weighted. Our algorithm is applied to the structural analysis and description of a collection of 17685 abstracts of research papers published in 17 Springer journals related to Data Science for the 20-year period 1998-2017. Our taxonomy of Data Science (TDS) is extracted from the Association for Computing Machinery Computing Classification System 2012 (ACM-CCS), a six-level hierarchical taxonomy manually developed by a team of ACM experts. The TDS also includes a number of additional leaves that we added to cater for recent developments not represented in the ACM-CCS taxonomy. We find fuzzy clusters of leaf topics over the text collection, using specially developed machinery. Three of the clusters are indeed thematic, relating to the Data Science sub-areas of (a) learning, (b) information retrieval, and (c) clustering. These three clusters are then lifted in the TDS using ParGenFS, which allows us to draw some conclusions about tendencies in developments in these areas.
The series Lecture Notes in Computer Science (LNCS), including its subseries Lecture Notes in Artificial Intelligence (LNAI) and Lecture Notes in Bioinformatics (LNBI), has established itself as a medium for the publication of new developments in computer science and information technology research and teaching - quickly, informally, and at a high level.
The two-volume set LNCS 11508 and 11509 constitutes the refereed proceedings of of the 18th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2019, held in Zakopane, Poland, in June 2019.
The 122 revised full papers presented were carefully reviewed and selected from 333 submissions. The papers included in the first volume are organized in the following five parts: neural networks and their applications; fuzzy systems and their applications; evolutionary algorithms and their applications; pattern classification; artificial intelligence in modeling and simulation.
The papers included in the second volume are organized in the following five parts: computer vision, image and speech analysis; bioinformatics, biometrics, and medical applications; data mining; various problems of artificial intelligence; agent systems, robotics and control.
We define a most specific generalization of a fuzzy set of topics assigned to leaves of the rooted tree of a domain taxonomy. This generalization lifts the set to its “head subject” node in the higher ranks of the taxonomy tree. The head subject is supposed to “tightly” cover the query set, possibly bringing in some errors referred to as “gaps” and “offshoots”. Our method, ParGenFS, globally minimizes a penalty function combining the numbers of head subjects and gaps and offshoots, differently weighted. Two applications are considered: (1) analysis of tendencies of research in Data Science; (2) audience extending for programmatic targeted advertising online. The former involves a taxonomy of Data Science derived from the celebrated ACM Computing Classification System 2012. Based on a collection of research papers published by Springer 1998–2017, and applying in-house methods for text analysis
retrieval and clustering. The head subjects of these clusters inform us of some general tendencies of the research. The latter involves publicly available IAB Tech Lab Content Taxonomy. Each of about 25 mln users is assigned with a fuzzy profile within this taxonomy, which is generalized offline using ParGenFS. Our experiments show that these head subjects effectively extend the size of targeted audiences at least twice without loosing quality.
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.