scholarly journals A Review on WordNet and Vector Space Analysis for Short-text Semantic Similarity

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 166578-166592
Author(s):  
Surender Singh Samant ◽  
N. L. Bhanu Murthy ◽  
Aruna Malapati

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yudong Liu ◽  
Wen Chen

In the field of information science, how to help users quickly and accurately find the information they need from a tremendous amount of short texts has become an urgent problem. The recommendation model is an important way to find such information. However, existing recommendation models have some limitations in case of short text recommendation. To address these issues, this paper proposes a recommendation model based on semantic features and a knowledge graph. More specifically, we first select DBpedia as a knowledge graph to extend short text features of items and get the semantic features of the items based on the extended text. And then, we calculate the item vector and further obtain the semantic similarity degrees of the users. Finally, based on the semantic features of the items and the semantic similarity of the users, we apply the collaborative filtering technology to calculate prediction rating. A series of experiments are conducted, demonstrating the effectiveness of our model in the evaluation metrics of mean absolute error (MAE) and root mean square error (RMSE) compared with those of some recommendation algorithms. The optimal MAE for the model proposed in this paper is 0.6723, and RMSE is 0.8442. The promising results show that the recommendation effect of the model on the movie field is significantly better than those of these existing algorithms.


1989 ◽  
Vol 62 (4) ◽  
pp. 823-833 ◽  
Author(s):  
P. M. Di Lorenzo

1. In the study of the neural code for gustation, it has been suggested that the pattern of responsiveness across fibers or units in the neural pathway for taste may provide the basis for identification and discrimination among taste qualities. Two possible mechanisms of comparison between pairs of stimuli were discussed, as follows: 1) a labeled-line code, where one subset of units responds to one stimulus but not the other, and a second subset of units responds in just the opposite fashion; and 2) a frequency code, where all units always respond well to one stimulus and always respond poorly to the other. 2. Conventional analyses of across unit patterns of response in the taste system have employed the Pearson product-moment correlation and/or the neural mass difference as measures of similarity. These measures consider the relative firing rates for a given pair of stimuli (correlation) or the averaged absolute differences in the firing rates (neural mass difference) evoked by two stimuli. Neither of these metrics considers both the absolute and the relative strengths of response to a given pair of stimuli in the comparison of across unit patterns. 3. A new approach to the analysis of across unit patterns of response, called vector space analysis, was described. With this method, the responses to a given stimulus across units are viewed as a vector in n-dimensional space, where n is the number of units in the sample. The length of each vector provides an index of the overall strength of the response to a particular tastant, and the angle between two vectors is a measure of the similarity of the across unit patterns for a given pair of tastants. 4. A neural discrimination (delta, delta) was derived from this approach as a measure of the similarity of two vectors that incorporates information about both the overall magnitude of response and the distribution of responses across units for a given pair of of stimuli. A labeled line index (lambda, lambda) was also proposed to indicate the extent to which the discrimination between two stimuli may be encoded by the responses in two separate subsets of units. 5. Electrophysiological responses to representatives of the four basic taste qualities (salty, sour, sweet, and bitter) were recorded in 47 single units located in the parabrachial nucleus of the pons (PbN) of the rat. Conventional and vector space analyses were applied to the across unit patterns that were recorded from these units. Multidimensional scaling techniques were used to compare the results of each analysis.(ABSTRACT TRUNCATED AT 400 WORDS)


2011 ◽  
Vol 3 (2) ◽  
pp. 1-17
Author(s):  
Rajiv Kadaba ◽  
Suratna Budalakoti ◽  
David DeAngelis ◽  
K. Suzanne Barber

Entities interacting on the web establish their identity by creating virtual personas. These entities, or agents, can be human users or software-based. This research models identity using the Entity-Persona Model, a semantically annotated social network inferred from the persistent traces of interaction between personas on the web. A Persona Mapping Algorithm is proposed which compares the local views of personas in their social network referred to as their Virtual Signatures, for structural and semantic similarity. The semantics of the Entity-Persona Model are modeled by a vector space model of the text associated with the personas in the network, which allows comparison of their Virtual Signatures. This enables all the publicly accessible personas of an entity to be identified on the scale of the web. This research enables an agent to identify a single entity using multiple personas on different networks, provided that multiple personas exhibit characteristic behavior. The agent is able to increase the trustworthiness of on-line interactions by establishing the identity of entities operating under multiple personas. Consequently, reputation measures based on on-line interactions with multiple personas can be aggregated and resolved to the true singular identity.


Author(s):  
Rajiv Kadaba ◽  
Suratna Budalakoti ◽  
David DeAngelis ◽  
K. Suzanne Barber

Entities interacting on the web establish their identity by creating virtual personas. These entities, or agents, can be human users or software-based. This research models identity using the Entity-Persona Model, a semantically annotated social network inferred from the persistent traces of interaction between personas on the web. A Persona Mapping Algorithm is proposed which compares the local views of personas in their social network referred to as their Virtual Signatures, for structural and semantic similarity. The semantics of the Entity-Persona Model are modeled by a vector space model of the text associated with the personas in the network, which allows comparison of their Virtual Signatures. This enables all the publicly accessible personas of an entity to be identified on the scale of the web. This research enables an agent to identify a single entity using multiple personas on different networks, provided that multiple personas exhibit characteristic behavior. The agent is able to increase the trustworthiness of on-line interactions by establishing the identity of entities operating under multiple personas. Consequently, reputation measures based on on-line interactions with multiple personas can be aggregated and resolved to the true singular identity.


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