Very Fast Similarity Queries on Semi-Structured Data from the Web

<p>In this paper, we propose a single low-dimensional representation for entities found in different datasets on the web. Our proposed PIC-D embeddings can represent large D-partite graphs using small number of dimensions enabling fast similarity queries. Our experiments show that this representation can be constructed in small amount of time (linear in number of dimensions). We demonstrate how it can be used for variety of similarity queries like set expansion, automatic set instance acquisition, and column classification. Our approach results in comparable precision with respect to task specific baselines and up to two orders of magnitude improvement in terms of query response time.</p

A Very Fast Method for Clustering Big Text Datasets

Large-scale text datasets have long eluded a family of particularly elegant and effective clustering methods that exploits the power of pair-wise similarities between data points due to the prohibitive cost, time- and space-wise, in operating on a similarity matrix, where the state-of-the-art is at best quadratic in time and in space. We present an extremely fast and simple method also using the power of all pair-wise similarity between data points, and show through experiments that it does as well as previous methods in clustering accuracy, and it does so with in linear time and space, without sampling data points or sparsifying the similarity matrix.</p

Relational retrieval using a combination of path-constrained random walks

Scientific literature with rich metadata can be represented as a labeled directed graph. This graph representation enables a number of scientific tasks such as ad hocretrieval or named entity recognition (NER) to be formulated as typed proximity queries in the graph. One popular proximity measure is called Random Walk with Restart(RWR), and much work has been done on the supervised learning of RWR measures by associating each edge label with a parameter. In this paper, we describe a novel learnable proximity measure which instead uses one weight per edge label sequence: proximity is defined by a weighted combination of simple “path experts”, each corresponding to following a particular sequence of labeled edges. Experiments on eight tasks in two subdomains of biology show that the new learning method significantly outperforms the RWR model (both trained and untrained). We also extend the method to support two additional types of experts to model intrinsic properties of entities: query-independent experts, which generalize the PageRank measure, and popular entity experts which allow rankings to be adjusted for particular entities that are especially important.</p

Power Iteration Clustering

We present a simple and scalable graph clustering method called power iteration clustering (PIC). PIC finds a very low-dimensional embedding of a dataset using truncated power iteration on a normalized pair-wise similarity matrix of the data. This embedding turns out to be an effective cluster indicator, consistently outperforming widely used spectral methods such as NCut on real datasets. PIC is very fast on large datasets, running over 1,000 times faster than an NCut implementation based on the state-of-the-art IRAM eigenvector computation technique</p

Natural Language Models for Predicting Programming Comments

<p>Statistical language models have successfully been used to describe and analyze natural language documents. Recent work applying language models to programming languages is focused on the task of predicting code, while mainly ignoring the prediction of programmer comments. In this work, we predict comments from JAVA source files of open source projects, using topic models and n-grams, and we analyze the performance of the models given varying amounts of background data on the project being predicted. We evaluate models on their comment-completion capability in a setting similar to codecompletion tools built into standard code editors, and show that using a comment completion tool can save up to 47% of the comment typing.</p

Scaling Graph-based Semi Supervised Learning to Large Number of Labels Using Count-Min Sketch

<p>Graph-based Semi-supervised learning (SSL) algorithms have been successfully used in a large number of applications. These methods classify initially unlabeled nodes by propagating label information over the structure of graph starting from seed nodes. Graph-based SSL algorithms usually scale linearly with the number of distinct labels (m), and require O(m) space on each node. Unfortunately, there exist many applications of practical significance with very large m over large graphs, demanding better space and time complexity. In this paper, we propose MAD-Sketch, a novel graph-based SSL algorithm which compactly stores label distribution on each node using Count-min Sketch, a randomized data structure. We present theoretical analysis showing that under mild conditions, MAD-Sketch can reduce space complexity at each node from O(m) to O(log m), and achieve similar savings in time complexity as well. We support our analysis through experiments on multiple real world datasets. We observe that MAD-Sketch achieves similar performance as existing state-of-the-art graph-based SSL algorithms, while requiring smaller memory footprint and at the same time achieving up to 10x speedup. We find that MAD-Sketch is able to scale to datasets with one million labels, which is beyond the scope of existing graph-based SSL algorithms.</p

Bootstrapping Biomedical Ontologies for Scientific Text using NELL

<p>We describe an open information extraction system for biomedical text based on NELL (the Never-Ending Language Learner) (Carlson et al., 2010), a system designed for extraction from Web text. NELL uses a coupled semi-supervised bootstrapping approach to learn new facts from text, given an initial ontology and a small number of “seeds” for each ontology category. In contrast to previous applications of NELL, in our task the initial ontology and seeds are automatically derived from existing resources. We show that NELL’s bootstrapping algorithm is susceptible to ambiguous seeds, which are frequent in the biomedical domain. Using NELL to extract facts from biomedical text quickly leads to semantic drift. To address this problem, we introduce a method for assessing seed quality, based on a larger corpus of data derived from the Web. In our method, seed quality is assessed at each iteration of the bootstrapping process. Experimental results show significant improvements over NELL’s original bootstrapping algorithm on two types of tasks: learning terms from biomedical categories, and named-entity recognition for biomedical entities using a learned lexicon.</p

Entity List Completion Using Set Expansion Techniques

Set expansion refers to expanding a partial set of “seed” objects into a more complete set. In this paper, we focus on relation and list extraction techniques to perform Entity List Completion task through a two stage retrieval process. First stage takes given query entity and target entity examples as seeds and does set expansion. In second stage, only those candidates who have valid URI in Billion Triple dataset are ranked according to type match with given types. First stage of this system focuses on the recall while second stage tries to improve precision of the outputted list. We submitted the results on the Web as well as ClueWeb09 corpus.</p

Node Clustering in Graphs: An Empirical Study

Modeling networks is an active area of research and is used for many applications ranging from bioinformatics to social network analysis. An important operation that is often performed in the course of graph analysis is node clustering. Popular methods for node clustering such as the normalized cut method have their roots in graph partition optimization and spectral graph theory. Recently, there has been increasing interest in modeling graphs probabilistically using stochastic block models and other approaches that extend it. In this paper, we present an empirical study that compares the node clustering performances of state-of-the-art algorithms from both the probabilistic and spectral families on undirected graphs. Our experiments show that no family dominates over the other and that network characteristics play a significant role in determining the best model to use.</p

Random Walk Inference and Learning in A Large Scale Knowledge Base

We consider the problem of performing learning and inference in a large scale knowledge base containing imperfect knowledge with incomplete coverage. We show that a soft inference procedure based on a combination of constrained, weighted, random walks through the knowledge base graph can be used to reliably infer new beliefs for the knowledge base. More specifically, we show that the system can learn to infer different target relations by tuning the weights associated with random walks that follow different paths through the graph, using a version of the Path Ranking Algorithm (Lao and Cohen, 2010b). We apply this approach to a knowledge base of approximately 500,000 beliefs extracted imperfectly from the web by NELL, a never-ending language learner (Carlson et al., 2010). This new system improves significantly over NELL’s earlier Horn-clause learning and inference method: it obtains nearly double the precision at rank 100, and the new learning method is also applicable to many more inference tasks</p