In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/. To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -- the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.

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The anatomy of a large-scale hypertextual Web search engine

Imagined communities: Reflections on the origin and spread of nationalism

The Anatomy of a Large-Scale Hypertextual Web Search Engine.

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The development of the Internet in recent years has made it possible and useful to access many different information systems anywhere in the world to obtain information. While there is much research on the integration of heterogeneous information systems, most commercial systems stop short of the actual integration of available data. Data fusion is the process of fusing multiple records representing the same real-world object into a single, consistent, and clean representation.This article places data fusion into the greater context of data integration, precisely defines the goals of data fusion, namely, complete, concise, and consistent data, and highlights the challenges of data fusion, namely, uncertain and conflicting data values. We give an overview and classification of different ways of fusing data and present several techniques based on standard and advanced operators of the relational algebra and SQL. Finally, the article features a comprehensive survey of data integration systems from academia and industry, showing if and how data fusion is performed in each.

Data fusion

Crowd-sourcing social computing systems represent a new material for HCI designers. Crowd sourcing has been successfully applied to many areas, but these systems are difficult to reliably design and prototype. One source of uncertainty during design is the behavior of the crowd at different levels of scale. A second source of uncertainty during design is the crowd’s response to differing incentive functions. Using a research through design methodology we have constructed two crowd-sourcing systems where these two sources of uncertainty are central research problems to the design of both systems. This paper discusses these issues with respect to our implementations and discusses a research agenda designed to reduce the uncertainty associated with these design choices.

Design Uncertainty in Crowd-Sourcing Systems

Question answering (QA) provides answers to a wide range of questions but is still limited in the complexity of reasoning and the breadth of accessible data sources. In this paper, we describe a dataset and baseline results for a question answering system that utilizes web tables. The dataset is derived from commonly asked questions on the web, and their corresponding answers found in tables on websites. Our dataset is novel in that every question is paired with a table of a different signature, so learning must automatically generalize across domains. Each QA training instance comprises a table, a natural language question, and a corresponding structured SQL query. We build our model by dividing question answering into a sequence of tasks, including table retrieval and question element classification, and conduct experiments to measure the performance of each task. In a traditional machine learning design manner, we extract various features specific to each task, apply a neural model, and then compose a full pipeline which constructs the SQL query from its parts. Our work provides quantitative results and error analysis for each task, and identifies in detail the reasoning required to generate SQL expressions from natural language questions. This analysis of reasoning informs future models based on neural machine learning.

Question answering via web extracted tables

We discuss the problem of unavailable data sources in the context of two mediator based applications We discuss the limitations of existing system with respect to this problem and describe a novel evaluation model that overcomes these shortcomings

Anthony Tomasic

Papers

Design Uncertainty in Crowd-Sourcing Systems

Question answering via web extracted tables

Unavailable Data Sources in Mediator Based Applications

A task-oriented dialogue architecture via transformer neural language models and symbolic injection

Contingent Responsiveness in Digital Storybooks: Effects on Children's Comprehension and the Role of Individual Differences in Attention.