You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A SURVEY ON ONTOLOGY BASED WEB PERSONALIZATION 
Chapter Title:  SURVEYS 
1. Abstract of Cited Paper (BIB001):  The Internet consists of web sites that employ different kinds of structures as the backbone of their build-up. However, users are browsing the web according to its content, regardless of the structure. In this paper we discuss the possibilities of applying ontologies in exploring the web sites' structures and usage for producing viewing recommendations for the visitors. A special log system for capturing access data is introduced as well as techniques applied for data mining. Ontology of user profiles is constructed by exploiting the user locality model. 
2. Abstract of Cited Paper (BIB002):  Semantic search has been one of the motivations of the semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based knowledge bases to improve search over large document repositories. In our view of information retrieval on the semantic Web, a search engine returns documents rather than, or in addition to, exact values in response to user queries. For this purpose, our approach includes an ontology-based scheme for the semiautomatic annotation of documents and a retrieval system. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with conventional keyword-based retrieval to achieve tolerance to knowledge base incompleteness. Experiments are shown where our approach is tested on corpora of significant scale, showing clear improvements with respect to keyword-based search 
3. Abstract of Cited Paper (BIB003):  Given the rapid growth of participatory media content such as blogs, there is a need to design personalized recommender systems to recommend only useful content to users. We believe that in addition to producing useful recommendations, certain insights from media research such as simplification and opinion diversity in recommendations should form the foundations of such recommender systems, so that the behavior of the systems can be understood more closely, and modified if necessary. We propose and evaluate such a system based on a Bayesian user-model. We use the underlying social network of blog authors and readers to model the preference features for individual users. The initial results of our proposed solution are encouraging, and set the agenda for fu-

Literature Review:
Recently the need for adaptive systems has been recognized and the research has been geared up on user profiling and context. More sophisticated personalised web experiences have been provided with web mining techniques combined with Ontology. Robal and Kalja BIB002 , discussed the possibilities of applying ontology's in exploring the web sites' structures and usage for producing various recommendations for the visitors. Content based system analyses item descriptions to identify items that are of particular interest to the user. Pazzani and Billsus  Discussed Content-based recommendation systems Kearney, Anand, & Shapcott  , investigates how web visitor usage data may be combined with semantic domain knowledge to provide a deeper understanding of user behaviour. Seth and Zhang BIB004 , focus on news associated content and suggest the design of a social network based recommender system for this rationale Staab & Studer [16] ,formally defined an ontology as a 4-tuple of a set of concepts ,a set of relations, a set of instances and a set of axioms. A context-aware system has to infer which context the user is in a given moment in time, and consequently adapt the system to that context  . The profiles are constructed using a variety of learning techniques including the vector space model Genetic algorithms, the probabilistic model or clustering. CASTELLS, Miriam FERNÁNDEZ, and David VALLET, Discussed about using Vector -space model for Ontology based information retrieval. BIB003 . C´elia da Costa Pereira1 and Andrea G. B. Tettamanzi, Discussed user profiling using genetic algorithms BIB001 Chen, Q. proposed a neural network approach to user modeling in the context of information retrieval  .