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Ontologies for Knowledge Representation
Published in Archana Patel, Narayan C. Debnath, Bharat Bhushan, Semantic Web Technologies, 2023
These ontologies are not domain restricted. WordNet [14], developed by Princeton University, is a general-purpose ontology that includes nouns, verbs, adverbs, and adjectives that are organized as synonyms set called synsets. It resembles a thesaurus where the words are grouped based on their meaning. Wordnet is a natural language terms ontology that can be utilized for similarity score computations. The current version of WordNet v.3.1 includes 155,287 distinct terms and 117,659 synsets, arranged as taxonomic hierarchies. The synsets are also arranged into hierarchies corresponding to several synonyms of the same concept. Multiple kinds of relationships can also be generated between these synsets. The most popular relationships in WordNet are the Hyponym/Hypernym relationship (i.e., Is-A relationship), and the Meronym/Holonym relationship (i.e., Part-Of relationship). WordNet can be considered as both a dictionary and a thesaurus. Many researchers working on Natural Language Processing and Linguistics use WordNet as their dataset.
Functional Architecture for Knowledge Semantics
Published in Denise Bedford, Knowledge Architectures, 2020
It is an essential semantic method because it can reveal the most commonly occurring verb associations between concepts. These revelations may fall into the three predefined types of relationships we described earlier. But, it is more likely that these revelations will help us to understand better that last category of ‘associative relationships,’ i.e., the ‘other’ or ‘everything else.’ Semantic network graphs derived from intentionally developed collections can help us to understand semantics at a level we can use to teach machines. They can help us to expose our tacit knowledge of a context or domain. We leverage a semantic network when we understand it as a set of concepts that are related to one another. Most semantic networks are cognitively based. Semantic networks and graphs are essential tools for the field of computational linguistics. While semantic networks have been the focus of research since the 1950s, the increased computing capacity of the past 20 years has led to an explosion of applications and new insights. One notable example of this research is WordNet, a lexical database of the English language. Wordnet organizes English into sets of synonyms called synsets, provides short, general definitions, and records the various semantic relations between these synonym sets and some degree of hierarchy. It provides a first primitive understanding of common relationships that mean nodes are equal or have some inherent structure.
Track
Published in Walter R. Paczkowski, Deep Data Analytics for New Product Development, 2020
WordNet is a lexical database that classifies words into a hierarchy of more abstract words and concepts. It maps words to their synonyms thus allowing a grouping of words. In this regard, some have equated WordNet to a combination dictionary and thesaurus. The important use for this application is the hierarchy of concepts that is formed. This hierarchy can be visualized as an inverted tree with the root, representing the most general abstract concept, at the top of a graph with branches flowing down from the root. Each branch can be split into new branches, the split points, called nodes, being a next lower (and less abstract) concept or word. This split continues until the original word is reached. At this point, the branches terminate at a final node. There will be as many final branches and terminal nodes as there are words originally submitted to the WordNet program. In our case, the tokenized words from a single sentence are used so there are as many terminal nodes as there are words in that sentence.
Semantic-Based Integrated Plagiarism Detection Approach for English Documents
Published in IETE Journal of Research, 2021
Manpreet Kaur, Vishal Gupta, Ravreet Kaur
Word-to-word similarity sometimes poses the problem of word ambiguity in English. For example, the word “bat” can be used in a different sense to represent either the mammal or the sports equipment. Hence, the similarity between two words depends on the context of a word in which it is used in the sentence. Several semantic databases such as WordNet [42], Gene Ontology [47], and Transfer Standard [48] are available in English to extract the semantics of the word. We adopt the WordNet hierarchy to clarify the context of two words and to compute the semantic relatedness between them. WordNet is a lexical English database that includes thousands of words, including nouns, verbs, adverbs, and adjectives which are building blocks of it, mapped into a set of cognitive synonyms called “synsets”. A short and precise description (“gloss”) is associated with each word or concept in the WordNet for a better representation of its semantics. Two words are considered to be more related if they are closer to each other with fewer edges than those located far away with more edges between them. Common information is shared between two words provided by the most specific word that subsumes them called “least common subsumer (LCS)”. That is to say, the least common subsumer is the most specific common ancestral node of two words.
Perceived Usefulness Factors of Online Reviews: A Study of Amazon.com
Published in Journal of Computer Information Systems, 2018
Sang-Gun Lee, Silvana Trimi, Chang-Gyu Yang
WordNet is a semantically structured lexical database started in 1985 by linguists and computer scientists at Princeton University. In WordNet, words are arranged by semantic relations, synonym sets or synsets, and not in an alphabetical order [24, 38]. WordNet defines words into classes (such as nouns, verbs, adjectives, and adverbs) and groups words into synsets, where each synset may have different synsets in accordance with semantic relations (i.e., hypernym, hyponym, meronym, and antonym). After drawing attention from linguists and computer scientists around the world, new versions of WordNet have been introduced [8, 37, 46] and even expanded beyond the English language. Many researchers began creating independent language processing systems for their own languages, resulting in the development of WordNets for more than 50 languages [47]. In this study, we apply WordNet to qualitatively assess online postings for their tendency, either positive or negative, and the word class used.
Web service discovery with incorporation of web services clustering
Published in International Journal of Computers and Applications, 2023
Sunita Jalal, Dharmendra Kumar Yadav, Chetan Singh Negi
WordNet is semantically oriented English lexical database developed at Princeton University under the direction of Miller [21]. The content of WordNet consists of a set of synsets and semantic relationship between these synsets. A synset is a set of synonyms. For example, in WordNet database car word has five synsets: , , , and . The synsets are interconnected via different semantic relationships such as synonymy/antonymy, hyponymy/hypernymy, and holonymy/meronymy. Two concepts have synonymy relationship if they have similar meaning. Two concepts have antonymy relationship if they are opposite in meaning. A concept is called hypernym in hypernymy relationship if its meaning denotes a super-ordinate. For example, is a hypernym of bicycle. A concept is called hyponym if its meaning reprsents a subordinate. For example, car is hyponym of . A holonymy/meronymy relationship exists between two concepts if one concept is the part of another concept. For example snowflake is a meronym of snow and body is holonym of heart, lungs, arms and legs. The WordNet can be viewed as the hierarchical structure among these semantic relations. The WordNet lexicon presents a good semantic structure for calculating semantic similarity between concepts. Figure 5 shows an example of hyponymy/hypernymy semantic relationship between concepts.