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294
The Google similarity distance
, 2005
"... Words and phrases acquire meaning from the way they are used in society, from their relative semantics to other words and phrases. For computers the equivalent of ‘society ’ is ‘database, ’ and the equivalent of ‘use ’ is ‘way to search the database. ’ We present a new theory of similarity between ..."
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Cited by 314 (9 self)
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Words and phrases acquire meaning from the way they are used in society, from their relative semantics to other words and phrases. For computers the equivalent of ‘society ’ is ‘database, ’ and the equivalent of ‘use ’ is ‘way to search the database. ’ We present a new theory of similarity between words and phrases based on information distance and Kolmogorov complexity. To fix thoughts we use the world-wide-web as database, and Google as search engine. The method is also applicable to other search engines and databases. This theory is then applied to construct a method to automatically extract similarity, the Google similarity distance, of words and phrases from the world-wideweb using Google page counts. The world-wide-web is the largest database on earth, and the context information entered by millions of independent users averages out to provide
A Survey of Modern Authorship Attribution Methods
- JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
"... Authorship attribution supported by statistical or computational methods has a long history starting from 19th century and marked by the seminal study of Mosteller and Wallace (1964) on the authorship of the disputed Federalist Papers. During the last decade, this scientific field has been developed ..."
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Cited by 155 (9 self)
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Authorship attribution supported by statistical or computational methods has a long history starting from 19th century and marked by the seminal study of Mosteller and Wallace (1964) on the authorship of the disputed Federalist Papers. During the last decade, this scientific field has been developed substantially taking advantage of research advances in areas such as machine learning, information retrieval, and natural language processing. The plethora of available electronic texts (e.g., e-mail messages, online forum messages, blogs, source code, etc.) indicates a wide variety of applications of this technology provided it is able to handle short and noisy text from multiple candidate authors. In this paper, a survey of recent advances of the automated approaches to attributing authorship is presented examining their characteristics for both text representation and text classification. The focus of this survey is on computational requirements and settings rather than linguistic or literary issues. We also discuss evaluation methodologies and criteria for authorship attribution studies and list open questions that will attract future work in this area.
Automated classification and analysis of internet malware
- In Proceedings of Recent Advances in Intrusion Detection (RAID’07
, 2007
"... Abstract. Numerous attacks, such as worms, phishing, and botnets, threaten the availability of the Internet, the integrity of its hosts, and the privacy of its users. A core element of defense against these attacks is anti-virus (AV) software—a service that detects, removes, and characterizes these ..."
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Cited by 104 (5 self)
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Abstract. Numerous attacks, such as worms, phishing, and botnets, threaten the availability of the Internet, the integrity of its hosts, and the privacy of its users. A core element of defense against these attacks is anti-virus (AV) software—a service that detects, removes, and characterizes these threats. The ability of these products to successfully characterize these threats has far-reaching effects—from facilitating sharing across organizations, to detecting the emergence of new threats, and assessing risk in quarantine and cleanup. In this paper, we examine the ability of existing host-based anti-virus products to provide semantically meaningful information about the malicious software and tools (or malware) used by attackers. Using a large, recent collection of malware that spans a variety of attack vectors (e.g., spyware, worms, spam), we show that different AV products characterize malware in ways that are inconsistent across AV products, incomplete across malware, and that fail to be concise in their semantics. To address these limitations, we propose a new classification technique that describes malware behavior in terms of system state changes (e.g., files written, processes created) rather than in sequences or patterns of system calls. To address the sheer volume of malware and diversity of its behavior, we provide a method for automatically categorizing these profiles of malware into groups that reflect similar classes of behaviors and demonstrate how behavior-based clustering provides a more direct and effective way of classifying and analyzing Internet malware. 1
Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990-2010)
"... The simple but general formal theory of fun & intrinsic motivation & creativity (1990-) is based on the concept of maximizing intrinsic reward for the active creation or discovery of novel, surprising patterns allowing for improved prediction or data compression. It generalizes the traditio ..."
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Cited by 75 (15 self)
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The simple but general formal theory of fun & intrinsic motivation & creativity (1990-) is based on the concept of maximizing intrinsic reward for the active creation or discovery of novel, surprising patterns allowing for improved prediction or data compression. It generalizes the traditional field of active learning, and is related to old but less formal ideas in aesthetics theory and developmental psychology. It has been argued that the theory explains many essential aspects of intelligence including autonomous development, science, art, music, humor. This overview first describes theoretically optimal (but not necessarily practical) ways of implementing the basic computational principles on exploratory, intrinsically motivated agents or robots, encouraging them to provoke event sequences exhibiting previously unknown but learnable algorithmic regularities. Emphasis is put on the importance of limited computational resources for online prediction and compression. Discrete and continuous time formulations are given. Previous practical but non-optimal implementations (1991, 1995, 1997-2002) are reviewed, as well as several recent variants by others (2005-). A simplified typology addresses current confusion concerning the precise nature of intrinsic motivation.
A comparison of personal name matching: Techniques and practical issues
- IN ‘WORKSHOP ON MINING COMPLEX DATA’ (MCD’06), HELD AT IEEE ICDM’06, HONG KONG
, 2006
"... or send email to: Technical-DOT-Reports-AT-cs-DOT-anu.edu.au A list of technical reports, including some abstracts and copies of some full reports may be found at: ..."
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Cited by 55 (12 self)
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or send email to: Technical-DOT-Reports-AT-cs-DOT-anu.edu.au A list of technical reports, including some abstracts and copies of some full reports may be found at:
P.: Automatic meaning discovery using google
- Centrum Wiskunde & Informatica (CWI
, 2004
"... We have found a method to automatically extract the meaning of words and phrases from the world-wide-web using Google page counts. The approach is novel in its unrestricted problem domain, simplicity of implementation, and manifestly ontological underpinnings. The world-wide-web is the largest datab ..."
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Cited by 40 (3 self)
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We have found a method to automatically extract the meaning of words and phrases from the world-wide-web using Google page counts. The approach is novel in its unrestricted problem domain, simplicity of implementation, and manifestly ontological underpinnings. The world-wide-web is the largest database on earth, and the latent semantic context information entered by millions of independent users averages out to provide automatic meaning of useful quality. We demonstrate positive correlations, evidencing an underlying semantic structure, in both numerical symbol notations and number-name words in a variety of natural languages and contexts. Next, we demonstrate the ability to distinguish between colors and numbers, and to distinguish between 17th century Dutch painters; the ability to understand electrical terms, religious terms, emergency incidents, and we conduct a massive experiment in understanding WordNet categories; the ability to do a simple automatic English-Spanish translation. 1
LIMES -- A Time-Efficient Approach for Large-Scale Link Discovery on the Web of Data
, 2011
"... The Linked Data paradigm has evolved into a powerful enabler for the transition from the document-oriented Web into the Semantic Web. While the amount of data published as Linked Data grows steadily and has surpassed 25 billion triples, less than 5 % of these triples are links between knowledge base ..."
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Cited by 40 (7 self)
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The Linked Data paradigm has evolved into a powerful enabler for the transition from the document-oriented Web into the Semantic Web. While the amount of data published as Linked Data grows steadily and has surpassed 25 billion triples, less than 5 % of these triples are links between knowledge bases. Link discovery frameworks provide the functionality necessary to discover missing links between knowledge bases in a semi-automatic fashion. Yet, the task of linking knowledge bases requires a significant amount of time, especially when it is carried out on large data sets. This paper presents and evaluates LIMES- a novel timeefficient approach for link discovery in metric spaces. Our approach utilizes the mathematical characteristics of metric spaces to compute estimates of the similarity between instances. These estimates are then used to filter out a large amount of those instance pairs that do not suffice the mapping conditions. Thus, LIMES can reduce the number of comparisons needed during the mapping process by several orders of magnitude. We present the mathematical foundation and the core algorithms employed in the implementation. We evaluate LIMES with synthetic data to elucidate its behavior on small and large data sets with different configurations and show that our approach can significantly reduce the time complexity of a mapping task. In addition, we compare the runtime of our framework with a state-ofthe-art link discovery tool. We show that LIMES is more than 60 times faster when mapping large knowledge bases.
On Universal Prediction and Bayesian Confirmation
- Theoretical Computer Science
, 2007
"... The Bayesian framework is a well-studied and successful framework for inductive reasoning, which includes hypothesis testing and confirmation, parameter estimation, sequence prediction, classification, and regression. But standard statistical guidelines for choosing the model class and prior are not ..."
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Cited by 30 (14 self)
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The Bayesian framework is a well-studied and successful framework for inductive reasoning, which includes hypothesis testing and confirmation, parameter estimation, sequence prediction, classification, and regression. But standard statistical guidelines for choosing the model class and prior are not always available or can fail, in particular in complex situations. Solomonoff completed the Bayesian framework by providing a rigorous, unique, formal, and universal choice for the model class and the prior. I discuss in breadth how and in which sense universal (non-i.i.d.) sequence prediction solves various (philosophical) problems of traditional Bayesian sequence prediction. I show that Solomonoff’s model possesses many desirable properties: Strong total and future bounds, and weak instantaneous bounds, and in contrast to most classical continuous prior densities has no zero p(oste)rior problem, i.e. can confirm universal hypotheses, is reparametrization and regrouping invariant, and avoids the old-evidence and updating problem. It even performs well
Application of Kolmogorov complexity and universal codes to identity testing and nonparametric testing of serial independence for time series
, 2006
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Least square projection: A fast high-precision multidimensional projection technique and its application to document mapping
- IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
, 2008
"... The problem of projecting multidimensional data into lower dimensions has been pursued by many researchers due to its potential application to data analysis of various kinds. This paper presents a novel multidimensional projection technique based on least square approximations. The approximations c ..."
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Cited by 24 (8 self)
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The problem of projecting multidimensional data into lower dimensions has been pursued by many researchers due to its potential application to data analysis of various kinds. This paper presents a novel multidimensional projection technique based on least square approximations. The approximations compute the coordinates of a set of projected points based on the coordinates of a reduced number of control points with defined geometry. We name the technique Least Square Projections (LSP). From an initial projection of the control points, LSP defines the positioning of their neighboring points through a numerical solution that aims at preserving a similarity relationship between the points given by a metric in mD. In order to perform the projection, a small number of distance calculations are necessary, and no repositioning of the points is required to obtain a final solution with satisfactory precision. The results show the capability of the technique to form groups of points by degree of similarity in 2D. We illustrate that capability through its application to mapping collections of textual documents from varied sources, a strategic yet difficult application. LSP is faster and more accurate than other existing high-quality methods, particularly where it was mostly tested, that is, for mapping text sets.
