The Experts below are selected from a list of 59991 Experts worldwide ranked by ideXlab platform
Thies Lindenthal - One of the best experts on this subject based on the ideXlab platform.
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Monocentric Cyberspace: The Primary Market for Internet Domain Names
The Journal of Real Estate Finance and Economics, 2016Co-Authors: Thies LindenthalAbstract:Cyberspace is no different from traditional cities, at least in economic terms. Urban economics governs the creation of new space on the Internet and explains location choices and price gradients in virtual space. This study explores registration dynamics in the largest primary market for virtual space: Internet Domain names. After developing a framework for Domain registrations, it empirically tests whether Domain registrations are constrained by the depletion of unregistered high quality Domain names. Estimations based on registrations of COM Domain names suggest that the number of Domains expands substantially slower than the growth in overall demand for Domain space. Supplying alternative Domain extensions can relax the shortage in Domains in the short term.
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AMCIS - Pricing Quality Attributes of Internet Domain Names: A Hedonic Model for Words
2014Co-Authors: Thies Lindenthal, Claudia LoebbeckeAbstract:We investigate price finding of buyers and sellers of Internet Domains. We develop a hedonic model for Domain prices, controlling for differences in Domain quality along multiple dimensions and test the model empirically on a large dataset of Domain transactions observed at secondary Domain markets. We document differences in the implicit prices for selected Domain attributes across different types of buyers: Domain developers, who subsequently launch websites on the purchased Domains, price differently from Domain investors. We find no evidence of sellers being able to discriminate between the different buyer types.
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Valuable words: The price dynamics of Internet Domain names
Journal of the Association for Information Science and Technology, 2014Co-Authors: Thies LindenthalAbstract:This article estimates the first constant quality price index for Internet Domain names. The suggested index provides a benchmark for Domain name traders and investors looking for information on price trends, historical returns, and the fundamental risk of Internet Domain names. The index increases transparency in the market for this newly emerged asset class. A cointegration analysis shows that Domain registrations and resale prices form a long-run equilibrium and indicates supply constraints in Domain space. This study explores a large data set of Domain sales spanning the years 2006 to 2013. Differences in the quality of individual Domain names are controlled for in hedonic repeat sales regressions.
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pricing quality attributes of Internet Domain names a hedonic model for words
Americas Conference on Information Systems, 2014Co-Authors: Thies Lindenthal, Claudia LoebbeckeAbstract:We investigate price finding of buyers and sellers of Internet Domains. We develop a hedonic model for Domain prices, controlling for differences in Domain quality along multiple dimensions and test the model empirically on a large dataset of Domain transactions observed at secondary Domain markets. We document differences in the implicit prices for selected Domain attributes across different types of buyers: Domain developers, who subsequently launch websites on the purchased Domains, price differently from Domain investors. We find no evidence of sellers being able to discriminate between the different buyer types.
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Valuable Words: Pricing Internet Domain Names
SSRN Electronic Journal, 2011Co-Authors: Thies LindenthalAbstract:This paper estimates the first constant quality price index for Internet Domain names. The suggested index provides a benchmark for Domain name traders and investors looking for information on price trends, historical returns and the fundamental risk of Internet Domain names. The index increases transparency in the market for this newly emerged asset class. Furthermore, it can serve as a fever curve capturing the well-being of the Internet economy, as the demand for Domains is linked to online business opportunities in general. This proxy also includes small online enterprises that are excluded by traditional stock-price indices.The heterogeneity of Domain names is controlled for by hedonic repeat sales regressions. The empirical work is based on a previously unexplored dataset of real Domain transactions spanning the years 2006-2012.
Pablo Rodríguez-bocca - One of the best experts on this subject based on the ideXlab platform.
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Learning semantic information from Internet Domain Names using word embeddings
Engineering Applications of Artificial Intelligence, 2020Co-Authors: Waldemar López, Jorge Merlino, Pablo Rodríguez-boccaAbstract:Abstract Word embeddings is a well-known set of techniques widely used in Natural Language Processing (NLP). These techniques are able to learn words’ semantic based on the distributional hypothesis which states that words that are used and occur in the same contexts tend to purport similar meanings. This paper explores the usage of word embeddings in a new scenario to create a Vector Space Model (VSM) for Internet Domain Names (DNS). Our goal is to find semantically similar Domains only using information of DNS queries without any knowledge about the content of those Domains. The results presented here have practical applications in many engineering activities including websites recommendations, identification of fraudulent or risky sites, parental-control systems and anomaly detection in network traffic analysis (among others). We use the distributional hypothesis to learn the semantic of Domain names from users’ web navigation patterns, validating empirically that Domain names that occur in the same web sessions tend to have similar semantic. We also test different word embeddings techniques: word2vec , app2vec (considering time intervals between DNS queries), and fastText (which includes sub-word information). Due to the characteristics of Domain names, we found fastText as the best option for building a VSM for DNS, being 10.5% superior than word2vec with Skip-Gram which was the next best technique considering the Mean Average Precision at k (MAP@k) metric, which compares the most similar Domains in our VSM with the most similar Domains provided by a third party source, namely, similar sites service offered by Alexa Internet, Inc.
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CLEI - Vector representation of Internet Domain names using a word embedding technique
2017 XLIII Latin American Computer Conference (CLEI), 2017Co-Authors: Waldemar López, Jorge Merlino, Pablo Rodríguez-boccaAbstract:Word embeddings is a well known set of techniques widely used in natural language processing (NLP), and word2vec is a computationally-efficient predictive model to learn such embeddings. This paper explores the use of word embeddings in a new scenario. We create a vector representation of Internet Domain Names (DNS) by taking the core ideas from NLP techniques and applying them to real anonymized DNS log queries from a large Internet Service Provider (ISP). Our main objective is to find semantically similar Domains only using information of DNS queries without any other previous knowledge about the content of those Domains. We use the word2vec unsupervised learning algorithm with a Skip-Gram model to create the embeddings. And we validate the quality of our results by expert visual inspection of similarities, and by comparing them with a third party source, namely, similar sites service offered by Alexa Internet, Inc.
Waldemar López - One of the best experts on this subject based on the ideXlab platform.
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Learning semantic information from Internet Domain Names using word embeddings
Engineering Applications of Artificial Intelligence, 2020Co-Authors: Waldemar López, Jorge Merlino, Pablo Rodríguez-boccaAbstract:Abstract Word embeddings is a well-known set of techniques widely used in Natural Language Processing (NLP). These techniques are able to learn words’ semantic based on the distributional hypothesis which states that words that are used and occur in the same contexts tend to purport similar meanings. This paper explores the usage of word embeddings in a new scenario to create a Vector Space Model (VSM) for Internet Domain Names (DNS). Our goal is to find semantically similar Domains only using information of DNS queries without any knowledge about the content of those Domains. The results presented here have practical applications in many engineering activities including websites recommendations, identification of fraudulent or risky sites, parental-control systems and anomaly detection in network traffic analysis (among others). We use the distributional hypothesis to learn the semantic of Domain names from users’ web navigation patterns, validating empirically that Domain names that occur in the same web sessions tend to have similar semantic. We also test different word embeddings techniques: word2vec , app2vec (considering time intervals between DNS queries), and fastText (which includes sub-word information). Due to the characteristics of Domain names, we found fastText as the best option for building a VSM for DNS, being 10.5% superior than word2vec with Skip-Gram which was the next best technique considering the Mean Average Precision at k (MAP@k) metric, which compares the most similar Domains in our VSM with the most similar Domains provided by a third party source, namely, similar sites service offered by Alexa Internet, Inc.
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CLEI - Vector representation of Internet Domain names using a word embedding technique
2017 XLIII Latin American Computer Conference (CLEI), 2017Co-Authors: Waldemar López, Jorge Merlino, Pablo Rodríguez-boccaAbstract:Word embeddings is a well known set of techniques widely used in natural language processing (NLP), and word2vec is a computationally-efficient predictive model to learn such embeddings. This paper explores the use of word embeddings in a new scenario. We create a vector representation of Internet Domain Names (DNS) by taking the core ideas from NLP techniques and applying them to real anonymized DNS log queries from a large Internet Service Provider (ISP). Our main objective is to find semantically similar Domains only using information of DNS queries without any other previous knowledge about the content of those Domains. We use the word2vec unsupervised learning algorithm with a Skip-Gram model to create the embeddings. And we validate the quality of our results by expert visual inspection of similarities, and by comparing them with a third party source, namely, similar sites service offered by Alexa Internet, Inc.
S. Wright - One of the best experts on this subject based on the ideXlab platform.
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Cybersquatting at the Intersection of Internet Domain Names and Trademark Law
IEEE Communications Surveys and Tutorials, 2012Co-Authors: S. WrightAbstract:This is a tutorial about the basic elements of Domain name system and trademark law focussing on the interactions between them and specifically on the concept of cybersquatting. Cybersquatting is the registration (with the intent to profit) of a Domain name that is the trademark of another. The tutorial reviews the structure of the Domain name space and it's associated protocols as well as the legal context for trademarks and the recent advances for adjudication of disputes related to cybersquatting. Intermixed with the technical discussion, the paper also provides four rationales for engineers concerned with the design of communications protocols and related information systems to give consideration to aspects of law, in this case trademark law, that may impact the design or usage of these protocols and information systems. Some potential impacts of recent extensions proposed for the gTLD Domain name space are also considered.
Jorge Merlino - One of the best experts on this subject based on the ideXlab platform.
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Learning semantic information from Internet Domain Names using word embeddings
Engineering Applications of Artificial Intelligence, 2020Co-Authors: Waldemar López, Jorge Merlino, Pablo Rodríguez-boccaAbstract:Abstract Word embeddings is a well-known set of techniques widely used in Natural Language Processing (NLP). These techniques are able to learn words’ semantic based on the distributional hypothesis which states that words that are used and occur in the same contexts tend to purport similar meanings. This paper explores the usage of word embeddings in a new scenario to create a Vector Space Model (VSM) for Internet Domain Names (DNS). Our goal is to find semantically similar Domains only using information of DNS queries without any knowledge about the content of those Domains. The results presented here have practical applications in many engineering activities including websites recommendations, identification of fraudulent or risky sites, parental-control systems and anomaly detection in network traffic analysis (among others). We use the distributional hypothesis to learn the semantic of Domain names from users’ web navigation patterns, validating empirically that Domain names that occur in the same web sessions tend to have similar semantic. We also test different word embeddings techniques: word2vec , app2vec (considering time intervals between DNS queries), and fastText (which includes sub-word information). Due to the characteristics of Domain names, we found fastText as the best option for building a VSM for DNS, being 10.5% superior than word2vec with Skip-Gram which was the next best technique considering the Mean Average Precision at k (MAP@k) metric, which compares the most similar Domains in our VSM with the most similar Domains provided by a third party source, namely, similar sites service offered by Alexa Internet, Inc.
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CLEI - Vector representation of Internet Domain names using a word embedding technique
2017 XLIII Latin American Computer Conference (CLEI), 2017Co-Authors: Waldemar López, Jorge Merlino, Pablo Rodríguez-boccaAbstract:Word embeddings is a well known set of techniques widely used in natural language processing (NLP), and word2vec is a computationally-efficient predictive model to learn such embeddings. This paper explores the use of word embeddings in a new scenario. We create a vector representation of Internet Domain Names (DNS) by taking the core ideas from NLP techniques and applying them to real anonymized DNS log queries from a large Internet Service Provider (ISP). Our main objective is to find semantically similar Domains only using information of DNS queries without any other previous knowledge about the content of those Domains. We use the word2vec unsupervised learning algorithm with a Skip-Gram model to create the embeddings. And we validate the quality of our results by expert visual inspection of similarities, and by comparing them with a third party source, namely, similar sites service offered by Alexa Internet, Inc.
