This paper is a tutorial on Formal Concept Analysis (FCA) and its applications. FCA is an applied branch of Lattice Theory, a mathematical discipline which enables formalisation of concepts as basic units of human thinking and analysing data in the object-attribute form. Originated in early 80s, during the last three decades, it became a popular human-centred tool for knowledge representation and data analysis with numerous applications. Since the tutorial was specially prepared for RuSSIR 2014, the covered FCA topics include Information Retrieval with a focus on visualisation aspects, Machine Learning, Data Mining and Knowledge Discovery, Text Mining and several others.

Assuming the dataset is named "people_wiki.csv", Executing this script will result in steaming logs which is ultimately leading to the data getting indexed in elasticsearch. That's how easy it is! Let's spend the next few lines on what actually happened. We declare our elasticsearch object configured on our local machine. Once that object is initialized we will use it to index all of our data.

Data matching is the task of identifying, matching, and merging records that correspond to the same entities from several source systems. The entities under consideration most commonly refer to people, places, publications or citations, consumer products, or businesses. Besides data matching, the names most prominently used are record or data linkage, entity resolution, object identification, or field matching. A major challenge in data matching is the lack of common entity identifiers across different source systems to be matched. As a result of this, the matching needs to be conducted using attributes that contain partially identifying information, such as names, addresses, or dates of birth.

I heard a lot of horror stories about SEO not working lately among businesses. Some say search engine optimization has no future. Some others lately added affiliate marketing to the list. Let me set the record straight,once and for all, search engine optimization SEO is one of digital marketing processes. As long as Internet exists, seo will continue to be a part of online marketing.

Hashing has been proven a promising technique for fast nearest neighbor search over massive databases. In many practical tasks it usually builds multiple hash tables for a desired level of recall performance. However, existing multi-table hashing methods suffer from the heavy table redundancy, without strong table complementarity and effective hash code learning. To address the problem, this paper proposes a multi-table learning method which pursues a specified number of complementary and informative hash tables from a perspective of ensemble learning. By regarding each hash table as a neighbor prediction model, the multi-table search procedure boils down to a linear assembly of predictions stemming from multiple tables. Therefore, a sequential updating and learning framework is naturally established in a boosting mechanism, theoretically guaranteeing the table complementarity and algorithmic convergence. Furthermore, each boosting round pursues the discriminative hash functions for each table by a discrete optimization in the binary code space. Extensive experiments carried out on two popular tasks including Euclidean and semantic nearest neighbor search demonstrate that the proposed boosted complementary hash-tables method enjoys the strong table complementarity and significantly outperforms the state-of-the-arts.

For non-expert users, a textual query is the most popular and simple means for communicating with a retrieval or question answering system.However, there is a risk of receiving queries which do not match with the background knowledge.Query expansion and query rewriting are solutions for this problem but they are in danger of potentially yielding a large number of irrelevant words, which in turn negatively influences runtime as well as accuracy.In this paper, we propose a new method for automatic rewriting input queries on graph-structured RDF knowledge bases.We employ a Hidden Markov Model to determine the most suitable derived words from linguistic resources.We introduce the concept of triple-based co-occurrence for recognizing co-occurred words in RDF data.This model was bootstrapped with three statistical distributions.Our experimental study demonstrates the superiority of the proposed approach to the traditional n-gram model.

To compare the similarity of probability distributions, the information-theoretically motivated metrics like Kullback-Leibler divergence (KL) and Jensen-Shannon divergence (JSD) are often more reasonable compared with metrics for vectors like Euclidean and angular distance. However, existing locality-sensitive hashing (LSH) algorithms cannot support the information-theoretically motivated metrics for probability distributions. In this paper, we first introduce a new approximation formula for S2JSD-distance, and then propose a novel LSH scheme adapted to S2JSD-distance for approximate nearest neighbors search in high-dimensional probability distributions. We define the specific hashing functions, and prove their local-sensitivity. Furthermore, extensive empirical evaluations well illustrate the effectiveness of the proposed hashing schema on six public image datasets and two text datasets, in terms of mean Average Precision, Precision@N and Precision-Recall curve.

Binary embedding transforms vectors in Euclidean space into the vertices of Hamming space such that Hamming distance between binary codes reflects a particular distance metric. In machine learning, the similarity metrics induced by Mercer kernels are frequently used, leading to the development of binary embedding with Mercer kernels (BE-MK) where the approximate nearest neighbor search is performed in a reproducing kernel Hilbert space (RKHS). Kernelized locality-sensitive hashing (KLSH), which is one of the representative BE-MK, uses kernel PCA to embed data points into a Euclidean space, followed by the random hyperplane binary embedding. In general, it works well when the query and data points in the database follow the same probability distribution. The streaming data environment, however, continuously requires KLSH to update the leading eigenvectors of the Gram matrix, which can be costly or hard to carry out in practice. In this paper we present a completely randomized binary embedding to work with a family of additive homogeneous kernels, referred to as BE-AHK. The proposed algorithm is easy to implement, built on Vedaldi and Zisserman's work on explicit feature maps for additive homogeneous kernels. We show that our BE-AHK is able to preserve kernel values by developing an upper- and lower-bound on its Hamming distance, which guarantees to solve approximate nearest neighbor search efficiently. Numerical experiments demonstrate that BE-AHK actually yields similarity-preserving binary codes in terms of additive homogeneous kernels and is superior to existing methods in case that training data and queries are generated from different distributions. Moreover, in cases where a large code size is allowed, the performance of BE-AHK is comparable to that of KLSH in general cases.

Automated topic identification is an essential component invarious information retrieval and knowledge representationtasks such as automated summary generation, categorization search and document indexing. In this paper, we present the Wikitop system to automatically generate topic trees from the input text by performing hierarchical classification using the Wikipedia Category Network (WCN). Our preliminary results over a collection of 125 articles are encouraging and show potential of a robust methodology for automated topic tree generation.

Entity resolution (ER) is the task of identifying all records in a database that refer to the same underlying entity, and are therefore duplicates of each other. Due to inherent ambiguity of data representation and poor data quality, ER is a challenging task for any automated process. As a remedy, human-powered ER via crowdsourcing has become popular in recent years. Using crowd to answer queries is costly and time consuming. Furthermore, crowd-answers can often be faulty. Therefore, crowd-based ER methods aim to minimize human participation without sacrificing the quality and use a computer generated similarity matrix actively. While, some of these methods perform well in practice, no theoretical analysis exists for them, and further their worst case performances do not reflect the experimental findings. This creates a disparity in the understanding of the popular heuristics for this problem. In this paper, we make the first attempt to close this gap. We provide a thorough analysis of the prominent heuristic algorithms for crowd-based ER. We justify experimental observations with our analysis and information theoretic lower bounds.