Using a Domain Ontology to Mediate between a User Model an dD omain Applications (original) (raw)
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Ontology Based User Modeling for Personalized Information Access
International Journal of Computer Science & Applications, 2010
User modeling is an integral part of any personalized information retrieval system. The user model should be adaptable in order to capture the change in information needs of the users. In this paper, we present an ontology based user modeling strategy in the context of personalized information access. We have adopted a hybrid approach by capitalizing on the features of static and dynamic user profiling strategies. Static user profile specifies the user's interest in a very focused manner and dynamic user profiling adds the feature of adaptability into it. The dynamic user profiling strategy make use of the data sources like usage log and mouse operations that are performed by the users during the browsing sessions. Experiments have performed to evaluate the proposed method for user profiling.
Ontology Based User Modelling for Personalized Information Access
2005
User modeling is an integral part of any personalized information retrieval system. The user model should be adaptable in order to capture the change in information needs of the users. In this paper, we present an ontology based user modeling strategy in the context of personalized information access. We have adopted a hybrid approach by capitalizing on the features of static and dynamic user profiling strategies. Static user profile specifies the user's interest in a very focused manner and dynamic user profiling adds the feature of adaptability into it. The dynamic user profiling strategy make use of the data sources like usage log and mouse operations that are performed by the users during the browsing sessions. Experiments have performed to evaluate the proposed method for user profiling.
WordNet-based user profiles for semantic personalization
… of Workshop on …, 2005
Information access is one of the hottest topics in creating the future information society and it has become even more important since the advent of the Web. On one hand, our society relies more and more on information, both for professional and personal goals. Information is nowadays considered as one of the most valuable and strategic goods: knowing the right information, at the right moment, as soon as it is available is a "must" for all of us. On the other hand, the amount of available information, especially on the Web and in modern Digital Libraries, is increasing tremendously over time.
Proceedings of the 1st Workshop on Semantic Personalized Information Management
2010
Search engines have become an essential tool for the majority of users for finding information in the huge amount of documents contained in the Web. Even though, for most ad-hoc search tasks, they already provide a satisfying performance, certain fundamental properties still leave room for improvement. For example, if users perform general questions, they get frequently lost in navigating the huge amount of documents returned and typically stop their search after scanning a couple of result pages. Basically, results are ranked based on word frequencies and link structures, but other factors, such as sponsored links and ranking algorithms, are also taken into account. Standard search engines do not consider semantic information that can help in recognizing the relevance of a document with respect to the meaning of a query, so that users have to analyze every document and decide which documents are relevant with respect to the meaning implied in their search. Therefore, they also struggle for matching the individualized information needs of a user. Since users are different, and want to access information according to their experience and knowledge, different techniques for constructing user models, analyzing user profiles and deriving information about a user for the adaptation of content have been proposed. An emerging approach is to use Semantic Web and Web 2.0 technologies to model information about users.
Ontology-based personalized search and browsing
Web Intelligence and Agent Systems, 2003
As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. Users must either browse through a large hierarchy of concepts to find the information for which they are looking or submit a query to a publicly available search engine and wade through hundreds of results, most of them irrelevant. The core of the problem is that whether the user is browsing or searching, whether they are an eighth grade student or a Nobel prize winner, the identical information is selected and it is presented the same way. In this paper, we report on research that adapts information navigation based on a user profile structured as a weighted concept hierarchy. A user may create his or her own concept hierarchy and use them for browsing Web sites. Or, the user profile may be created from a reference ontology by 'watching over the user's shoulder' while they browse. We show that these automatically created profiles reflect the user's interests quite well and they are able to produce moderate improvements when applied to search results.
Ontology-based user modeling for web-based information systems
2007
The Web represents an information space where the amount of information grows exponentially. This calls for personalized interaction between users and web-based information systems providing information. Current systems provide a certain level of personalization, which allows the user to set up her preferences manually. Improved efficiency of information acquisition can be achieved by personalization based on a user's particularities used for the adaptation of content or navigation in the information space.
IJERT-Personalised Information Access Based on Ontology and Collaborative Filtering
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/personalised-information-access-based-on-ontology-and-collaborative-filtering https://www.ijert.org/research/personalised-information-access-based-on-ontology-and-collaborative-filtering-IJERTV1IS6484.pdf It is now very easy to access information from internet via World Wide Web. If we access anything via search engine they do not deliver the relevant information because they are programmed as one size fits all. Therefore it is very logical and important to personalize the retrieval system according to preference of user. a retrieval system based on user interests andpreferences play an important role to enhance effectiveness of information retrieval. These systems can also distinct short term and long term preferences of user on the basis of frequency of user interest. The aim of this thesis is to refine access of information in the web information retrieval towards personalization by using dynamic user profile,ontology based query expansion and collaborative filtering technique. This thesis work contribute in improving the accuracy of information retrieval to personalize the user profile by combining the dynamic user profile with ontology and dynamic user profile with collaborative filtering .
Modeling ontology-driven personalization of Web contents
2008
Abstract Personalization of contents accessed on the Web can be improved by proper exploitation of user preferences and navigation behavior. Such profile information may be provided by metadata and should be managed through state-of-the-art Web engineering methodologies and notations.
2010
The Semantic Web dream of a real world-wide graph of interconnected resources is – slowly but steadily – becoming a concrete reality. Still, the whole range of models and technologies which will change forever the way we interact with the web, seems to be missing from every-day technologies available on our personal computers. Ontologies, annotation facilities and semantic querying could (and should) bring new life to Personal Information Management, supporting users in contrasting the ever-growing information overload they are facing in these years, overwhelmed by plethora of communication channels and media. In this paper we present our attempt in bringing the Semantic Web Knowledge Management paradigm at the availability of diverse personal desktop tools (Web Browser, Mail clients, Agenda etc...), by evolving Web Browser Semantic extension Semantic Turkey to an extensible framework providing RDF data access at different levels: java access through OSGi extensions, HTTP access or ...
User Profiles for Personalized Information Access
Lecture Notes in Computer Science, 2007
The amount of information available online is increasing exponentially. While this information is a valuable resource, its sheer volume limits its value. Many research projects and companies are exploring the use of personalized applications that manage this deluge by tailoring the information presented to individual users. These applications all need to gather, and exploit, some information about individuals in order to be effective. This area is broadly called user profiling. This chapter surveys some of the most popular techniques for collecting information about users, representing, and building user profiles. In particular, explicit information techniques are contrasted with implicitly collected user information using browser caches, proxy servers, browser agents, desktop agents, and search logs. We discuss in detail user profiles represented as weighted keywords, semantic networks, and weighted concepts. We review how each of these profiles is constructed and give examples of projects that employ each of these techniques. Finally, a brief discussion of the importance of privacy protection in profiling is presented. This chapter discusses user profiles specifically designed for providing personalized information access. Other types of profiles, build using different construction techniques, are described elsewhere in this book. In particular, Chapter 4 [40] discusses generic user modeling systems that are broader in scope, not necessarily focused on Internet applications. Related research on collaborative recommender systems, discussed in Chapter 9 of this book [81], combines information from multiple users in order to provide improved information services. Concern over privacy protection is growing in parallel with the demand for personalized features. These two trends seem to be in direct opposition to each other, so privacy protection must be a crucial component of every personalization system. A detailed discussion can be found in Chapter 21 of this book [39]. There are a wide variety of applications to which personalization can be applied and a wide variety of different devices available on which to deliver the personalized information. Early personalization research focused on personalized filtering and/or rating systems for e-mail [49], electronic newspapers [14, 16], Usenet newsgroups [41, 58, 86, 91, 106], and Web documents [4]. More recently, personalization efforts have focused on improving navigation effectiveness by providing browsing assistants [9, 13], and adaptive Web sites [69]. Because search is one of the most common activities performed today, many projects are now focusing on personalized Web search [46, 88, 92] and more details on the subject can be found in Chapter 6 of this book [52]. However, personalized approaches to searching other types of collections, e.g., short stories [76], Java source code [100], and images [14] have also been explored. Commercial products are also adopting personalized features, for example, Yahoo!'s personalized Web portals [110] and Google Lab's personalized search [30]. The aforementioned systems are just a few examples that illustrate the breadth of applications to which personalized approaches are being investigated. Nichols [63] and Oard and Marchionini [64] provide a general overview of some the issues and approaches to personalized rating and filtering and Pretschner [71] describes approximately 45 personalization systems. Most personalization systems are based on some type of user profile, a data instance of a user model that is applied to adaptive interactive systems. User profiles may include demographic information, e.g., name, age, country, education level, etc, and may also represent the interests or preferences of either a group of users or a single person. Personalization of Web portals, for example, may focus on individual users, for example, displaying news about specifically chosen topics or the market summary of specifically selected stocks, or a groups of users for whom distinctive characteristics where identified, for example, displaying targeted advertising on ecommerce sites. In order to construct an individual user's profile, information may be collected explicitly, through direct user intervention, or implicitly, through agents that monitor user activity. Although profiles are typically built only from topics of interest to the user, some projects have explored including information about non-relevant topics in the profile [35, 104]. In these approaches, the system is able to use both kinds of topics to identify relevant documents and discard non-relevant documents at the same time. Profiles that can be modified or augmented are considered dynamic, in contrast to static profiles that maintain the same information over time. Dynamic profiles that Explicit info Data Collection Technology Or Application Profile Constructor User Implicit info Keyword profile Semantic Net profile Concept profile Personalized Services As shown in Figure 2.1, the user profiling process generally consists of three main phases. First, an information collection process is used to gather raw information about the user. As described in Section 2.2, depending on the information collection process selected, different types of user data can be extracted. The second phase focuses on user profile construction from the user data. Section 2.3 summarizes a variety of ways in which profiles may be represented and Section 2.4 some of the ways a profile may be constructed. The final phase, in which a technology or application exploits information in the user profile in order to provide personalized services, is discussed in Parts II and III of this book. 2.2 Collecting Information About Users The first phase of a profiling technique collects information about individual users. A basic requirement of such a system is that it must be able to uniquely identify users. This task is described in more detail in Section 2.2.1. The information collected may be explicitly input by the user or implicitly gathered by a software agent. It may be collected on the user's client machine or gathered by the application server itself. Depending on how the information is collected, different data about the users may be extracted. Several options, and their impacts, are discussed in Section 2.2.2. In