Semantic-based Analysis

Nowadays, satisfying user needs has become the main challenge in a variety of web applications. Recommender systems play a major role in that direction. However, as most of the information is present in a textual form, recommender systems face the challenge of efficiently analyzing huge amounts of text. The usage of semantic-based analysis has gained much interest in recent years. The emergence of ontologies has yet facilitated semantic interpretation of text. However, relying on an ontology for performing the semantic analysis requires too much effort to construct and maintain the used ontologies. Besides, the currently known ontologies cover a small number of the world's concepts especially when a nondomain-specific concepts are needed. This paper proposes the use of Wikipedia as ontology to solve the problems of using traditional ontologies for the text analysis in text-based recommendation systems. A full system model that unifies semantic-based analysis with a collaborative via content recommendation system is presented.

Nowadays, satisfying user needs has become the main challenge in a variety of web applications. Recommender systems play a major role in that direction. However, as most of the information is present in a textual form, recommender systems face the challenge of efficiently analyzing huge amounts of text. The usage of semantic-based analysis has gained much interest in recent years. The emergence of ontologies has yet facilitated semantic interpretation of text. However, relying on an ontology for performing the semantic analysis requires too much effort to construct and maintain the used ontologies. Besides, the currently known ontologies cover a small number of the world's concepts especially when a nondomain-specific concepts are needed. This paper proposes the use of Wikipedia as ontology to solve the problems of using traditional ontologies for the text analysis in text-based recommendation systems. A full system model that unifies semantic-based analysis with a collaborative via content recommendation system is presented.

Nowadays, satisfying user needs has become the main challenge in a variety of web applications. Recommender systems play a major role in that direction. However, as most of the information is present in a textual form, recommender systems face the challenge of efficiently analyzing huge amounts of text. The usage of semantic-based analysis has gained much interest in recent years. The emergence of ontologies has yet facilitated semantic interpretation of text. However, relying on an ontology for performing the semantic analysis requires too much effort to construct and maintain the used ontologies. Besides, the currently known ontologies cover a small number of the world's concepts especially when a nondomain-specific concepts are needed. This paper proposes the use of Wikipedia as ontology to solve the problems of using traditional ontologies for the text analysis in text-based recommendation systems. A full system model that unifies semantic-based analysis with a collaborative via content recommendation system is presented.

Semantic annotation is an enabling technology. It links content of documents to ontology concepts that unambiguously describe them to enable information retrieval applications. The annotation process itself is a challenging task due to the presence of polysemies and lack of sufficient contextual text in some ontologies. This paper proposes a novel approach to semantic annotation of document segments by first generating contextual texts for ontology concepts using documents sourced on the Web. Document segments are subsequently annotated with concepts based on similarity of contexts.

Nowadays, satisfying user needs has become the main challenge in a variety of web applications. Recommender systems play a major role in that direction. However, as most of the information is present in a textual form, recommender systems face the challenge of efficiently analyzing huge amounts of text. The usage of semantic-based analysis has gained much interest in recent years. The emergence of ontologies has yet facilitated semantic interpretation of text. However, relying on an ontology for performing the semantic analysis requires too much effort to construct and maintain the used ontologies. Besides, the currently known ontologies cover a small number of the world's concepts especially when a nondomain-specific concepts are needed. This paper proposes the use of Wikipedia as ontology to solve the problems of using traditional ontologies for the text analysis in text-based recommendation systems. A full system model that unifies semantic-based analysis with a collaborative via content recommendation system is presented.

In this paper we introduce a hybrid method for classifying HTML documents. In this method the statistical, semantic and writing style features of text are used to categorize documents. Categorization can be done in both supervised and unsupervised modes and categories may be predefined or be created dynamically (clustering). The classification system exploits an ontology of interesting topics. The ontology which contains categories and their hierarchical relations can be updated automatically during the system’s lifetime. Newly defined categories can be added to the ontology and existing categories can be changed according to the documents received. The statistical part of the method is based on the Rocchio algorithm. The algorithm has been changed to cover the special conditions for dynamic category building, for categorizing with and without training data and for variable length feature vectors. The semantic part of the algorithm exploits Wordnet to substitute words with their cor...

Nowadays, satisfying user needs has become the main challenge in a variety of web applications. Recommender systems play a major role in that direction. However, as most of the information is present in a textual form, recommender systems face the challenge of efficiently analyzing huge amounts of text. The usage of semantic-based analysis has gained much interest in recent years. The emergence of ontologies has yet facilitated semantic interpretation of text. However, relying on an ontology for performing the ...

Identifying topics and concepts associated with a set of documents is a task common to many applications. It can help in the annotation and categorization of documents and be used to model a person's current interests for improving search results, business intelligence or selecting appropriate advertisements. One approach is to associate a document with a set of topics selected from a fixed ontology or vocabulary of terms. We have investigated using Wikipedia's articles and associated pages as a topic ontology for this ...

Nowadays, satisfying user needs has become the main challenge in a variety of web applications. Recommender systems play a major role in that direction. However, as most of the information is present in a textual form, recommender systems face the challenge of efficiently analyzing huge amounts of text. The usage of semantic-based analysis has gained much interest in recent years. The emergence of ontologies has yet facilitated semantic interpretation of text. However, relying on an ontology for performing the semantic analysis requires too much effort to construct and maintain the used ontologies. Besides, the currently known ontologies cover a small number of the world's concepts especially when a nondomain-specific concepts are needed. This paper proposes the use of Wikipedia as ontology to solve the problems of using traditional ontologies for the text analysis in text-based recommendation systems. A full system model that unifies semantic-based analysis with a collaborative via content recommendation system is presented.

Nowadays, satisfying user needs has become the main challenge in a variety of web applications. Recommender systems play a major role in that direction. However, as most of the information is present in a textual form, recommender systems face the challenge of efficiently analyzing huge amounts of text. The usage of semantic-based analysis has gained much interest in recent years. The emergence of ontologies has yet facilitated semantic interpretation of text. However, relying on an ontology for performing the semantic analysis requires too much effort to construct and maintain the used ontologies. Besides, the currently known ontologies cover a small number of the world's concepts especially when a nondomain-specific concepts are needed. This paper proposes the use of Wikipedia as ontology to solve the problems of using traditional ontologies for the text analysis in text-based recommendation systems. A full system model that unifies semantic-based analysis with a collaborative via content recommendation system is presented.

Nowadays, satisfying user needs has become the main challenge in a variety of web applications. Recommender systems play a major role in that direction. However, as most of the information is present in a textual form, recommender systems face the challenge of efficiently analyzing huge amounts of text. The usage of semantic-based analysis has gained much interest in recent years. The emergence of ontologies has yet facilitated semantic interpretation of text. However, relying on an ontology for performing the semantic analysis requires too much effort to construct and maintain the used ontologies. Besides, the currently known ontologies cover a small number of the world's concepts especially when a non-domain-specific concepts are needed. This paper proposes the use of Wikipedia as ontology to solve the problems of using traditional ontologies for the text analysis in text-based recommendation systems. A full system model that unifies semantic-based analysis with a collaborative via content recommendation system is presented.

Recommender systems apply statistical and knowledge discovery techniques to the problem of making recommendations during live user interaction. This paper describes a novel approach of building recommender systems for the Web with the aid of usergenerated content.