More and more users prefer to ask their technical questions online. For machines, understanding a question is nontrivial. Current approaches lack explicit background knowledge.In this paper, we introduce a novel technical question understanding approach to recommending probable solutions to users. First, a knowledge graph is constructed which contains abundant technical information, and an augmented knowledge graph is built on the basis of the knowledge graph, to link the knowledge graph and documents. Then we develop a light weight question driven mechanism to select candidate documents. To improve the online performance, we propose an index-based random walk to support the online search. We use comprehensive experiments to evaluate the effectiveness of our approach on a large scale of real-world query logs. Our system outperforms main-stream search engine and the state-of-art information retrieval methods. Meanwhile, extensive experiments confirm the efficiency of our index-based online search mechanism.

Recent years have witnessed extensive attention in binary code learning, a.k.a. hashing, for nearest neighbor search problems. It has been seen that high-dimensional data points can quantize into binary codes to give an efficient similarity approximation via Hamming distance. Among the existing schemes, ranking-based hashing is recent promising that targets at preserving ordinal relations of ranking in the Hamming space to minimize retrieval loss. However, the size of the ranking tuples that show the ordinal relations, is quadratic or cubic to the size of training samples. It is so very expensive to embed such ranking tuples in binary code learning, especially given a large-scale training data set. Besides, it remains difficult to build ranking tuples efficiently for most ranking-preserving hashing, which are deployed over an ordinal graph-based setting. To handle these problems, we propose a novel ranking-preserving hashing method, dubbed Ordinal Constraint Hashing (OCH), which efficiently learns the optimal hashing functions with a graph-based approximation to embed the ordinal relations. The core idea is to reduce the size of ordinal graph with ordinal constraint projection, which preserves the ordinal relations through a small data set (such as clusters or random samples). In particular, to learn such hash functions effectively, we further relax the discrete constraints and design a specific stochastic gradient decent algorithm for optimization. Experimental results on three large-scale visual search benchmark datasets, i.e. LabelMe, Tiny100K and GIST1M, show that the proposed OCH method can achieve superior performance over the state-of-the-arts approaches.

Phrase mining is a key research problem for semantic analysis and text-based information retrieval. The existing approaches based on NLP, frequency, and statistics cannot extract high quality phrases and the processing is also time consuming, which are not suitable for dynamic on-line applications. In this paper, we propose an efficient high-quality phrase mining approach (EQPM). To the best of our knowledge, our work is the first effort that considers both intra-cohesion and inter-isolation in mining phrases, which is able to guarantee appropriateness. We also propose a strategy to eliminate order sensitiveness, and ensure the completeness of phrases. We further design efficient algorithms to make the proposed model and strategy feasible. The empirical evaluations on four real data sets demonstrate that our approach achieved a considerable quality improvement and the processing time was 2.3X - 29X faster than the state-of-the-art works.

A directed graph where there is exactly one edge between every pair of vertices is called a tournament. Finding the “best” set of vertices of a tournament is a well studied problem in social choice theory. A tournament solution takes a tournament as input and outputs a subset of vertices of the input tournament. However, in many applications, for example, choosing the best set of drugs from a given set of drugs, the edges of the tournament are given only implicitly and knowing the orientation of an edge is costly. In such scenarios, we would like to know the best set of vertices (according to some tournament solution) by “querying” as few edges as possible. We, in this paper, precisely study this problem for commonly used tournament solutions: given an oracle access to the edges of a tournament T , find f(T) by querying as few edges as possible, for a tournament solution f. We first show that the set of Condorcet non-losers in a tournament can be found by querying 2n−⌊log n⌋−2 edges only and this is tight in the sense that every algorithm for finding the set of Condorcet non-losers needs to query at least 2n−⌊log n⌋−2 edges in the worst case, where n is the number of vertices in the input tournament. We then move on to study other popular tournament solutions and show that any algorithm for finding the Copeland set, the Slater set, the Markov set, the bipartisan set, the uncovered set, the Banks set, and the top cycle must query Ω(n 2 ) edges in the worst case. On the positive side, we are able to circumvent our strong query complexity lower bound results by proving that, if the size of the top cycle of the input tournament is at most k, then we can find all the tournament solutions mentioned above by querying O(nk + n log n / log(1− 1 / k ) ) edges only.

Keyphrases that efficiently summarize a document’s content are used in various document processing and retrieval tasks. Current state-of-the-art techniques for keyphrase extraction operate at a phrase-level and involve scoring candidate phrases based on features of their component words.In this paper, we learn keyphrase taggers for research papers using token-based features incorporating linguistic, surface-form, and document-structure information through sequence labeling. We experimentally illustrate that using within document features alone, our tagger trained with ConditionalRandom Fields performs on-par with existing state-of-the-art systems that rely on information from Wikipedia and citation networks. In addition, we are also able to harness recent work on feature labeling to seamlessly incorporate expert knowledge and predictions from existing systems to enhance the extraction performance further. We highlight the modeling advantages of our keyphrase taggers and show significant performance improvements on two recently-compiled datasets of keyphrases from Computer Science research papers.

We consider an active search problem in intensionally specified structured spaces. The ultimate goal in this setting is to discover structures from structurally different partitions of a fixed but unknown target class. An example of such a process is that of computer-aided de novo drug design. In the past 20 years several Monte Carlo search heuristics have been developed for this process. Motivated by these hand-crafted search heuristics, we devise a Metropolis--Hastings sampling scheme where the acceptance probability is given by a probabilistic surrogate of the target property, modeled with a max entropy conditional model. The surrogate model is updated in each iteration upon the evaluation of a selected structure. The proposed approach is consistent and the empirical evidence indicates that it achieves a large structural variety of discovered targets.

Elasticsearch is a distributed NoSQL document store search-engine and column-oriented database, whose fast (near real-time) reads and powerful aggregation engine make it an excellent choice as an'analytics database' for R&D, production-use or both. Installation is simple, it ships with sensible default settings that allow it to work effectively out-of-the-box, and all interaction is made via a set of intuitive and extremely well documented RESTful APIs. I've been using it for two years now and I am evangelical. The elasticsearchr package implements a simple Domain-Specific Language (DSL) for indexing, deleting, querying, sorting and aggregating data in Elasticsearch, from within R. The main purpose of this package is to remove the labour involved with assembling HTTP requests to Elasticsearch's REST APIs and processing the responses. Instead, users of this package need only send and receive data frames to Elasticsearch resources.

In this post we will focus on configuring the elasticsearch bit. I have chosen the Wikipedia people dump for the dataset. This is the wiki pages of a subset of people on Wikipedia. This dataset consists of three columns – URI, name, text. As the column names suggest, URI is the actual wiki link to that person's page, name is the person's name.

Search engines use machine learning for pattern detection. While it's impossible to explain in one short article how machine learning influences our lives, understanding the basics of machine learning can give you some insight into search algorithm updates, such as Google's Panda update. To predict the outcome of future tests, scripts can use supervised learning on past outcomes to define a hypothetical prediction line. The three images below show how plotted examples define averages. These averages are more likely to represent some truth as the training set grows.

Credit: "Busted" by Jason Scragz is licensed under CC BY 2.0 You copied an image on your blog that you saw on the internet. You didn't think you were doing anything wrong but it turns out you were. How can you avoid all this by finding images that are free to use? Creative Commons is here to help you out. How can you find these images? Google's Advance Image Search has a drop down box that allows you to restrict a search by different types of Creative Commons license.