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Abstract

Why we organize our personal digital data the way we do and how design of new PIM systems can help us manage our information more efficiently. Each of us has an ever-growing collection of personal digital data: documents, photographs, PowerPoint presentations, videos, music, emails and texts sent and received. To access any of this, we have to find it. The ease (or difficulty) of finding something depends on how we organize our digital stuff. In this book, personal information management (PIM) experts Ofer Bergman and Steve Whittaker explain why we organize our personal digital data the way we do and how the design of new PIM systems can help us manage our collections more efficiently.

Content

Bergman and Whittaker report that many of us use hierarchical folders for our personal digital organizing. Critics of this method point out that information is hidden from sight in folders that are often within other folders so that we have to remember the exact location of information to access it. Because of this, information scientists suggest other methods: search, more flexible than navigating folders; tags, which allow multiple categorizations; and group information management. Yet Bergman and Whittaker have found in their pioneering PIM research that these other methods that work best for public information management don't work as well for personal information management. Bergman and Whittaker describe personal information collection as curation: we preserve and organize this data to ensure our future access to it. Unlike other information management fields, in PIM the same user organizes and retrieves the information. After explaining the cognitive and psychological reasons that so many prefer folders, Bergman and Whittaker propose the user-subjective approach to PIM, which does not replace folder hierarchies but exploits these unique characteristics of PIM.

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Abstract

An ontology is a knowledge representation structure made up of concepts and their interrelations. It represents shared understanding delineated by some domain. The building of an ontology can be addressed from the perspective of natural language processing. This thesis discusses the validity and theoretical background of knowledge acquisition from natural language. It also presents the theoretical and experimental framework for NLP-driven ontology building and evaluation tasks.

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LCSH

Text processing (Computer science)

Subject

Text processing (Computer science)

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Abstract

In this title, a leading philosopher of the internet explains how knowledge and expertise can still work - and even grow stronger - in an age when the internet has made topics simply Too Big to Know. Knowing used to be so straightforward. If we wanted to know something we looked it up, asked an expert, gathered the facts, weighted the possibilities, and honed in on the best answer ourselves. But, ironically, with the advent of the internet and the limitless information it contains, we're less sure about what we know, who knows what, or even what it means to know at all. Knowledge, it would appear, is in crisis. And yet, while its very foundations seem to be collapsing, human knowledge has grown in previously unimaginable ways, and in inconceivable directions, in the Internet age. We fact-check the news media more closely and publicly than ever before. Science is advancing at an unheard of pace thanks to new collaborative techniques and new ways to find patterns in vast amounts of data. Businesses are finding expertise in every corner of their organization, and across the broad swath of their stakeholders. We are in a crisis of knowledge at the same time that we are in an epochal exaltation of knowledge. In "Too Big to Know", Internet philosopher David Weinberger explains that, rather than a systemic collapse, the Internet era represents a fundamental change in the methods we have for understanding the world around us. Weinberger argues that our notions of expertise - what it is, how it works, and how it is cultivated - are out of date, rooted in our pre-networked culture and assumptions. For thousands of years, we've relied upon a reductionist process of filtering, winnowing, and otherwise reducing the complex world to something more manageable in order to understand it. Back then, an expert was someone who had mastered a particular, well-defined domain. Now, we live in an age when topics are blown apart and stitched together by momentary interests, diverse points of view, and connections ranging from the insightful to the perverse. Weinberger shows that, while the limits of our own paper-based tools have historically prevented us from achieving our full capacity of knowledge, we can now be as smart as our new medium allows - but we will be smart differently. For the new medium is a network, and that network changes our oldest, most basic strategy of knowing. Rather than knowing-by-reducing, we are now knowing-by-including. Indeed, knowledge now is best thought of not as the content of books or even of minds, but as the way the network works. Knowledge will never be the same - not for science, not for business, not for education, not for government, not for any of us. As Weinberger makes clear, to make sense of this new system of knowledge, we need - and smart companies are developing - networks that are themselves experts. Full of rich and sometimes surprising examples from history, politics, business, philosophy, and science, "Too Big to Know" describes how the very foundations of knowledge have been overturned, and what this revolution means for our future.