A Vector Space Model for Semantic Similarity Calculation and OWL Ontology Alignment

2006 ◽  
pp. 307-316 ◽  
Author(s):  
Rubén Tous ◽  
Jaime Delgado
Keyword(s):  
Vector Space ◽  
Semantic Similarity ◽  
Vector Space Model ◽  
Ontology Alignment ◽  
Space Model ◽  
Similarity Calculation ◽  
Owl Ontology

A Novel Method for Text Similarity Calculation

2013 ◽  
Vol 660 ◽  
pp. 202-206
Author(s):  
Cai Rui ◽  
Li Fei ◽  
Chen Bin ◽  
Quan Cong
Keyword(s):  
Vector Space ◽  
Word Frequency ◽  
Word Order ◽  
Vector Space Model ◽  
Longest Common Subsequence ◽  
Text Similarity ◽  
Space Model ◽  
Frequency Information ◽  
Similarity Calculation ◽  
Common Subsequence

In view of the fact that traditional vector space model for text similarity calculation which does not take word order into consideration leads to bias, this paper puts forward a longest common subsequence and the traditional vector space model of combining text similarity calculation. This method takes the word order and word frequency information into account, using the texts of the longest common subsequence and substring of their information from all public records and the use of word order and word frequency in the text. The importance of similarity calculation is acknowledged, and the traditional vector space model in the calculation of the weight is used on the word frequency information. Some of the dataset collected through the web crawler are used in the proposed text similarity calculation method for testing, and the results proved the effectivity of the method.

Modeling Virtual Footprints

2011 ◽  
Vol 3 (2) ◽  
pp. 1-17
Author(s):  
Rajiv Kadaba ◽  
Suratna Budalakoti ◽  
David DeAngelis ◽  
K. Suzanne Barber
Keyword(s):  
Social Network ◽  
Vector Space ◽  
Semantic Similarity ◽  
Vector Space Model ◽  
Mapping Algorithm ◽  
Space Model ◽  
Single Entity ◽  
Characteristic Behavior ◽  
On Line ◽  
The Web

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.

Modeling Virtual Footprints

2012 ◽  
pp. 96-113
Author(s):  
Rajiv Kadaba ◽  
Suratna Budalakoti ◽  
David DeAngelis ◽  
K. Suzanne Barber
Keyword(s):  
Social Network ◽  
Vector Space ◽  
Semantic Similarity ◽  
Vector Space Model ◽  
Mapping Algorithm ◽  
Space Model ◽  
Single Entity ◽  
Characteristic Behavior ◽  
On Line ◽  
The Web

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.

scholarly journals IMPLEMENTATION OF GENERALIZED VECTOR SPACE MODEL METHOD AT AUTOMATIC ASSESSMENT OF ONLINE ESSAY EXAM

2018 ◽  
Vol 1 (2) ◽  
pp. 43
Author(s):  
Muhammad Arafah
Keyword(s):  
Vector Space ◽  
Vector Space Model ◽  
Automatic Testing ◽  
Automatic Assessment ◽  
Literature Study ◽  
Weighting Method ◽  
Space Model ◽  
Model Method ◽  
Similarity Calculation ◽  
Automatic Scoring

The aim of the study was to design and implement automatic testing of online essay examinations using the Generalized Vector Space Model (GVSM) method. This data is obtained through (1) Literature Study (2) Observation (3) Documentation. The results of this study indicate that the automatic scoring system with the GVSM weighting method and the cosine similarity similarity calculation method have the accuracy of the assessment with an average of 66%.

An improved focused crawler based on Semantic Similarity Vector Space Model

2015 ◽  
Vol 36 ◽  
pp. 392-407 ◽  
Author(s):  
Yajun Du ◽  
Wenjun Liu ◽  
Xianjing Lv ◽  
Guoli Peng
Keyword(s):  
Vector Space ◽  
Semantic Similarity ◽  
Vector Space Model ◽  
Space Model

scholarly journals Looking for Semantic Similarity: What a Vector Space Model of Semantics Can Tell Us About Attention in Real-world Scenes

2021 ◽  
Author(s):  
Taylor R. Hayes ◽  
John M. Henderson
Keyword(s):  
Vector Space ◽  
Semantic Similarity ◽  
Real World ◽  
Critical Role ◽  
Vector Space Model ◽  
Semantic Knowledge ◽  
Space Model ◽  
Strong Positive Relationship ◽  
Space Semantic ◽  
The Relationship

The visual world contains more information than we can perceive and understand in any given moment. Therefore, we must prioritize important scene regions for detailed analysis. Semantic knowledge gained through experience is theorized to play a central role in determining attentional priority in real- world scenes but is poorly understood. Here we examined the relationship between object semantics and attention by combining a vector space model of semantics with eye movements in scenes. Within this approach, the vector space semantic model served as the basis for a concept map, an index of the spatial distribution of the semantic similarity of objects across a given scene. The results showed a strong positive relationship between the semantic similarity of a scene region and viewers’ focus of attention, with greater attention to more semantically related scene regions. We conclude that object semantics play a critical role in guiding attention through real-world scenes.

The Detection of Similar Instance Based on Fingerprint

2015 ◽  
Vol 743 ◽  
pp. 711-714
Author(s):  
Y.H. Cai ◽  
D. Xu
Keyword(s):  
Vector Space ◽  
Hot Spot ◽  
Vector Space Model ◽  
Detection Methods ◽  
Retrieval Algorithm ◽  
Replacement Cost ◽  
Space Model ◽  
Similarity Calculation ◽  
Synonym Substitution ◽  
Detection Speed

Detecting similar instance is a research hot spot of Example-Based Machine Translation.The method of Vector Space Model is one of the mainstream detection methods. However, thereare two disadvantages for it: detection speed is very slow and synonym substitution is not accurate.To solve these problems, fingerprint retrieval algorithm is introduced to improve the detectionspeed. A concept of replacement cost is put forward to measure the accuracy of substitutionbetween synonyms. The result shows that this method can not only improve the detection speed butalso produce a certain improvement to the accuracy of the similarity calculation.

scholarly journals Looking for Semantic Similarity: What a Vector-Space Model of Semantics Can Tell Us About Attention in Real-World Scenes

2021 ◽  
pp. 095679762199476
Author(s):  
Taylor R. Hayes ◽  
John M. Henderson
Keyword(s):  
Vector Space ◽  
Semantic Similarity ◽  
Real World ◽  
Critical Role ◽  
Vector Space Model ◽  
Semantic Knowledge ◽  
Space Model ◽  
Strong Positive Relationship ◽  
Space Semantic ◽  
The Relationship

The visual world contains more information than we can perceive and understand in any given moment. Therefore, we must prioritize important scene regions for detailed analysis. Semantic knowledge gained through experience is theorized to play a central role in determining attentional priority in real-world scenes but is poorly understood. Here, we examined the relationship between object semantics and attention by combining a vector-space model of semantics with eye movements in scenes. In this approach, the vector-space semantic model served as the basis for a concept map, an index of the spatial distribution of the semantic similarity of objects across a given scene. The results showed a strong positive relationship between the semantic similarity of a scene region and viewers’ focus of attention; specifically, greater attention was given to more semantically related scene regions. We conclude that object semantics play a critical role in guiding attention through real-world scenes.

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