Query-Driven Sampling for Collective Entity Resolution

Probabilistic databases play a preeminent role in the processing and management of uncertain data. Recently, many database research efforts have integrated probabilistic models into databases to support tasks such as information extraction and labeling. Many of these efforts are based on batch oriented inference which inhibits a realtime workflow. One important task is entity resolution (ER). ER is the process of determining records (mentions) in a database that correspond to the same real-world entity. Traditional pairwise ER methods can lead to inconsistencies and low accuracy due to localized decisions. Leading ER systems solve this problem by collectively resolving all records using a probabilistic graphical model and Markov chain Monte Carlo (MCMC) inference. However, for large datasets this is an extremely expensive process. One key observation is that, such exhaustive ER process incurs a huge up-front cost, which is wasteful in practice because most users are interested in only a small subset of entities. In this paper, we advocate pay-as-you-go entity resolution by developing a number of query-driven collective ER techniques. We introduce two classes of SQL queries that involve ER operators --- selection-driven ER and join-driven ER. We implement novel variations of the MCMC Metropolis Hastings algorithm to generate biased samples and selectivity-based scheduling algorithms to support the two classes of ER queries. Finally, we show that query-driven ER algorithms can converge and return results within minutes over a database populated with the extraction from a newswire dataset containing 71 million mentions

Name Disambiguation from link data in a collaboration graph using temporal and topological features

In a social community, multiple persons may share the same name, phone number or some other identifying attributes. This, along with other phenomena, such as name abbreviation, name misspelling, and human error leads to erroneous aggregation of records of multiple persons under a single reference. Such mistakes affect the performance of document retrieval, web search, database integration, and more importantly, improper attribution of credit (or blame). The task of entity disambiguation partitions the records belonging to multiple persons with the objective that each decomposed partition is composed of records of a unique person. Existing solutions to this task use either biographical attributes, or auxiliary features that are collected from external sources, such as Wikipedia. However, for many scenarios, such auxiliary features are not available, or they are costly to obtain. Besides, the attempt of collecting biographical or external data sustains the risk of privacy violation. In this work, we propose a method for solving entity disambiguation task from link information obtained from a collaboration network. Our method is non-intrusive of privacy as it uses only the time-stamped graph topology of an anonymized network. Experimental results on two real-life academic collaboration networks show that the proposed method has satisfactory performance.Comment: The short version of this paper has been accepted to ASONAM 201

Robust Algorithms for Clustering with Applications to Data Integration

A growing number of data-based applications are used for decision-making that have far-reaching consequences and significant societal impact. Entity resolution, community detection and taxonomy construction are some of the building blocks of these applications and for these methods, clustering is the fundamental underlying concept. Therefore, the use of accurate, robust and scalable methods for clustering cannot be overstated. We tackle the various facets of clustering with a multi-pronged approach described below. 1. While identification of clusters that refer to different entities is challenging for automated strategies, it is relatively easy for humans. We study the robustness of clustering methods that leverage supervision through an oracle i.e an abstraction of crowdsourcing. Additionally, we focus on scalability to handle web-scale datasets. 2. In community detection applications, a common setback in evaluation of the quality of clustering techniques is the lack of ground truth data. We propose a generative model that considers dependent edge formation and devise techniques for efficient cluster recovery

A Suit of Record Normalization Methods, From Naive Ones, Globally Mine a Group of Duplicate Records

The promise of Big Data pivots after tending to a few big data integration challenges, for example, record linkage at scale, continuous data combination, and incorporating Deep Web. Although much work has been directed on these issues, there is restricted work on making a uniform, standard record from a gathering of records comparing to a similar genuine element. We allude to this errand as record normalization. Such a record portrayal, instituted normalized record, is significant for both front-end and back-end applications. In this paper, we formalize the record normalization issue, present top to bottom examination of normalization granularity levels (e.g., record, field, and worth segment) and of normalization structures (e.g., common versus complete). We propose an exhaustive structure for registering the normalized record. The proposed system incorporates a suit of record normalization techniques, from guileless ones, which utilize just the data accumulated from records themselves, to complex methodologies, which all around mine a gathering of copy records before choosing an incentive for a quality of a normalized record

Space exploration: The interstellar goal and Titan demonstration

Automated interstellar space exploration is reviewed. The Titan demonstration mission is discussed. Remote sensing and automated modeling are considered. Nuclear electric propulsion, main orbiting spacecraft, lander/rover, subsatellites, atmospheric probes, powered air vehicles, and a surface science network comprise mission component concepts. Machine, intelligence in space exploration is discussed