Ontology Based Personalized Search Engine

An ontology is a representation of knowledge as hierarchies of concepts within domain, using a shared vocabulary to denote the types, properties and inter-relationships of those concepts [1][2]. Ontologies are often equated with classification of hierarchies of classes, class definitions, and the relations, but ontologies need not be limited to these forms. Ontologies are also not limited to conservative definitions, i.e., in the traditional logic sense that only introduce terminology and do not add any knowledge about the world (Enderton, 1972). To specify a conceptualization, axioms need to be proposed that constrain interpretation of defined terms [3]. Ontologies are frameworks for organizing information and are collections of URIs. It is a systematic arrangement of all important categories of objects and concepts within a particular field and relationship between them. Search engines are commonly used for information retrieval from web. The ontology based personalized search engine (OPSE) captures the user’s priorities in the form of concepts by mining through the data which has been previously clicked by them. Search results need to be provided according to user profile and user interest so that highly relevant search data is provided to the user. In order to do this, user profiles need to be maintained. Location information is important for searching data; OPSE needs to classify concepts into content concepts and location concepts. User locations (gathered during user registration) are used to supplement the location concepts in OPSE. Ontology based user profiles are used to organize user preferences and adapt personalized ranking function in order for relevant documents to be retrieved according to a suitable ranking. A client-server architecture is used for design of ontology based personalized search engine. The design involves in collecting and storing client clickthrough data. Functionalities such as re-ranking and concept extraction can be performed at the server side of personalized search engine. As an additional requirement, we can address the privacy issue by restricting the information in the user profile exposed to the personalized mobile search engine server with some privacy parameters. The Prototype of OPSE will be developed on the web platform. Ontology based personalized search engines can significantly improve the precision of results

An Ontology-based Approach for Personalized Itinerary Search

Personalization plays an important role in information retrieval systems. In the field of transportation, and more specifically multimodal transportation, personalization represents an efficient way for travelers to find appropriate routes. Providing travelers with the relevant information to their needs and preferences is challenging for transportation systems. In this paper, we propose an ontology-based approach for personalized itinerary search. Our proposal is based on modeling each user using an ontological fuzzy modular profile that incorporates a set of fuzzy modules representing several aspects of the user’s description. The approach is applied in the transportation domain and integrates a new method of matching between the profile ontology and the domain ontology to obtain personalized responses for individual user profiles. Our proposal was implemented and evaluated. Obtained results show that personalization coupled with ontology matching enables an improvement of query reformulation

Validation and Evaluation

In this technical report, we present prototypical implementations of innovative tools and methods for personalized and contextualized (multimedia) search, collaborative ontology evolution, ontology evaluation and cost models, and dynamic access and trends in distributed (semantic) knowledge, developed according to the working plan outlined in Technical Report TR-B-12-04. The prototypes complete the next milestone on the path to an integral Corporate Semantic Web architecture based on the three pillars Corporate Ontology Engineering, Corporate Semantic Collaboration, and Corporate Semantic Search, as envisioned in TR-B-08-09

prototypical implementations ; working packages in project phase II

In this technical report, we present the concepts and first prototypical imple- mentations of innovative tools and methods for personalized and contextualized (multimedia) search, collaborative ontology evolution, ontology evaluation and cost models, and dynamic access and trends in distributed (semantic) knowledge. The concepts and prototypes are based on the state of art analysis and identified requirements in the CSW report IV

Intelligent personalized approaches for semantic search and query expansion

University of Technology Sydney. Faculty of Engineering and Information Technology.In today’s highly advanced technological world, the Internet has taken over all aspects of human life. Many services are advertised and provided to the users through online channels. The user looks for services and obtains them through different search engines. To obtain the best results that meet the needs and requirements of the users, researchers have extensively studied methods such as different personalization methods by which to improve the performance and efficiency of the retrieval process. A key part of the personalization process is the generation of user models. The most commonly used user models are still rather simplistic, representing the user as a vector of ratings or using a set of keywords. Recently, semantic techniques have had a significant importance in the field of personalized querying and personalized web search engines. This thesis focuses on both processes of personalized web search engines, first the reformulation of queries and second ranking query results. The importance of personalized web search lies in its ability to identify users' interests based on their personal profiles. This work contributes to personalized web search services in three aspects. These contributions can be summarized as follows: First, it creates user profiles based on a user’s browsing behaviour, as well as the semantic knowledge of a domain ontology, aiming to improve the quality of the search results. However, it is not easy to acquire personalized web search results, hence one of the problems that is encountered in this approach is how to get a precise representation of the user interests, as well as how to use it to find search results. The second contribution builds on the first contribution. A personalized web search approach is introduced by integrating user context history into the information retrieval process. This integration process aims to provide search results that meet the user’s needs. It also aims to create contextual profiles for the user based on several basic factors: user temporal behaviour during browsing, semantic knowledge of a specific domain ontology, as well as an algorithm based on re-ranking the search results. The previous solutions were related to the re-ranking of the returned search results to match the user’s requirements. The third contribution includes a comparison of three-term weight methods in personalized query expansion. This model has been built to incorporate both latent semantics and weighting terms. Experiments conducted in the real world to evaluate the proposed personalized web search approach; show promising results in the quality of reformulation and re-ranking processes compared to Google engine techniques

prototypical implementations

In this technical report, we present prototypical implementations of innovative tools and methods developed according to the working plan outlined in Technical Report TR-B-09-05 [23]. We present an ontology modularization and integration framework and the SVoNt server, the server-side end of an SVN- based versioning system for ontologies in the Corporate Ontology Engineering pillar. For the Corporate Semantic Collaboration pillar, we present the prototypical implementation of a light-weight ontology editor for non-experts and an ontology based expert finder system. For the Corporate Semantic Search pillar, we present a prototype for algorithmic extraction of relations in folksonomies, a tool for trend detection using a semantic analyzer, a tool for automatic classification of web documents using Hidden Markov models, a personalized semantic recommender for multimedia content, and a semantic search assistant developed in co-operation with the Museumsportal Berlin. The prototypes complete the next milestone on the path to an integral Cor- porate Semantic Web architecture based on the three pillars Corporate Ontol- ogy Engineering, Corporate Semantic Collaboration, and Corporate Semantic Search, as envisioned in [23]

Personalized content retrieval in context using ontological knowledge

Personalized content retrieval aims at improving the retrieval process by taking into account the particular interests of individual users. However, not all user preferences are relevant in all situations. It is well known that human preferences are complex, multiple, heterogeneous, changing, even contradictory, and should be understood in context with the user goals and tasks at hand. In this paper, we propose a method to build a dynamic representation of the semantic context of ongoing retrieval tasks, which is used to activate different subsets of user interests at runtime, in a way that out-of-context preferences are discarded. Our approach is based on an ontology-driven representation of the domain of discourse, providing enriched descriptions of the semantics involved in retrieval actions and preferences, and enabling the definition of effective means to relate preferences and context