A relational model of semantic similarity between words using automatically extracted lexical pattern clusters from the web

Bollegala, Danushka; Matsuo, Yutaka; Ishizuka, Mitsuru

doi:10.3115/1699571.1699617

Cited by 33 publications

(28 citation statements)

References 26 publications

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“…The research of Bollegala et al [7] was focused on the importance of the types of search engines that support social work from the point of view of technology and information. The research by NetMarket [8] explained Ecosia's social business model by studying global pollution changes compared to the trees planted by the search engine.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Internet Searches on Afforestation: The Case of a Green Search Engine

Palos-Sánchez

Saura

2018

Forests

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Abstract:Ecosia is an Internet search engine that plants trees with the income obtained from advertising. This study explored the factors that affect the adoption of Ecosia.org from the perspective of technology adoption and trust. This was done by using the Unified Theory of Acceptance and Use of Technology (UTAUT2) and then analyzing the results with PLS-SEM (Partial Least Squares-Structural Equation Modeling). Subsequently, a survey was conducted with a structured questionnaire on search engines, which yielded the following results: (1) the idea of a company helping to mitigate the effects of climate change by planting trees is well received by Internet users. However, few people accept the idea of changing their habits from using traditional search engines; (2) Ecosia is a search engine believed to have higher compatibility rates, and needing less hardware resources, and (3) ecological marketing is an appropriate and future strategy that can increase the intention to use a technological product. Based on the results obtained, this study shows that a search engine or other service provided by the Internet, which can be audited (visits, searches, files, etc.), can also contribute to curb the effects of deforestation and climate change. In addition, companies, and especially technological start-ups, are advised to take into account that users feel better using these tools. Finally, this study urges foundations and non-governmental organizations to fight against the effects of deforestation by supporting these initiatives. The study also urges companies to support technological services, and follow the behavior of Ecosia.org in order to positively influence user satisfaction by using ecological marketing strategies.

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Section: Introductionmentioning

confidence: 99%

The Effect of Internet Searches on Afforestation: The Case of a Green Search Engine

Palos-Sánchez

Saura

2018

Forests

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“…Co-occurrence methods (known for questionable results [3]) are also external. Here, similarity is calculated assuming that entities that appear together in high number of contexts (e.g.…”

Section: External Similarity Dimensionsmentioning

confidence: 99%

Dimensions of Ontological Similarity

Szmeja

Ganzha

Paprzycki

et al. 2016

2016 IEEE Tenth International Conference on Semantic Computing (ICSC)

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Thus far, many methods for calculating semantic similarity have been proposed. Most of them produce a single value that is to "represent all aspects of similarity." In opposition to this approach we argue that semantic entities can be similar in different ways, each with a separate score (and meaning / interpretation). We propose conceptualization of semantic similarity through separate dimensions that denote / capture different "aspects of similarity."

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“…The NGD is evaluated in a word classification task (i.e. words are grouped based on their similarities according to the model referred to in [10]). This also means that the words in question usually display the same formal properties, especially their inflections and distribution.…”

Section: Related Workmentioning

confidence: 99%

Measuring semantic similarity between digital forensics terminologies using web search engines

Karie

Venter

2012

2012 Information Security for South Africa

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Semantic similarity between different terminologies is becoming a generic problem that extends across numerous domains, touching applications developed for computational linguistics, artificial intelligence, cognitive science and, in the case of this paper, digital forensics. Despite the usefulness of semantic similarity measures in different domains, accurately measuring semantic similarity between any two terms remains a challenging task. The main difficulty lies in developing a computational method with the ability to generate satisfactory results close to how human beings perceive these terminologies, especially when used in their domain of expertise.This paper presents a novel approach of using the Web to measure semantic similarity between two terms x and y in the digital forensics domain. The proposed approach is based on the Euclidean distance, a mathematical concept used to calculate the distance between two points. This paper also shows how computing the absolute value of the difference of the logarithms of the hit count percentages of any given terms x and y relates to the computed Euclidean distance of x and y. Percentages are computed from the total number of hit counts reported by any Web search engine for the search terms x, y and the logical x AND y together. Finally, these concepts are used to deduce a formula to automatically calculate a semantic similarity measure coined as the Digital Forensic Absolute Semantic Similarity Value of the terms x and y, denoted as DFASSV(x, y).Experiments conducted using the proposed DFASSV method focuses on the digital forensics domain. However, a comparison of the DFASSV approach with previously proposed Web-based semantic similarity measures shows that this approach is well suited for digital forensics domain terminologies. In the authors' opinion however, the DFASSV approach can be applied in other domains as well because it does not require any humanannotated knowledge. DFASSV is a novel approach to semantic similarity measure and constitutes the main contribution of this paper.

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A relational model of semantic similarity between words using automatically extracted lexical pattern clusters from the web

Cited by 33 publications

References 26 publications

The Effect of Internet Searches on Afforestation: The Case of a Green Search Engine

The Effect of Internet Searches on Afforestation: The Case of a Green Search Engine

Dimensions of Ontological Similarity

Measuring semantic similarity between digital forensics terminologies using web search engines

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