Personalized Exploratory Search in the Semantic Web

Effective access to information on the Web, which has become vital to many users and to the whole society, is being hampered by information overload, unavailability of information, navigation issues and user diversity. We aim to facilitate the slow adoption of the Semantic Web by devising an enhanced faceted semantic browser with support for multi-paradigm exploration, personalized recommendation and adaptive view generation. We employ facet and restriction selection, ordering and annotation to address information overload and user guidance, and adaptive view generation with incremental graph visualization to enable end-user grade exploration of semantic web content. We present the highly promising results of two user studies performed with our browser prototypes in the job offers and digital image domains, which confirm the viability and practicality of our approach in terms of improved task times and user understanding of the explored information space. 1. Problem & Motivation The Web has become a global ubiquitous socioeconomic space providing information, services and facilitating both private and business communication with an estimated 1.8 billion users (survey by Miniwatts Marketing Group, 2010). Many use the Web daily as an integral part of their work, ultimately making the human society as we know it today dependent on the storage, availability and exchange of information on the Web. Due to the constant growth, complexity and size of the Web, several issues hamper online user experience: information overload (i.e., too much information being available), the lack of information availability (i.e., the required information is available somewhere in the Web, but unavailable to users who need it), the navigation problem (i.e., users losing track of their position in the information space effectively “getting lost in hyperspace”), ignorance of user diversity (i.e., the fact that sites are created to suit the “average user”). The Semantic Web aims to provide better search and browsing capabilities by enabling machine readability of information on the Web taking advantage of ontologies [8]. Adaptive Web approaches aim to learn about user preferences and adapt the user experience to the specific needs of individuals [3], while the Social Web aims to facilitate communication and collaboration on the Web by exploiting social wisdom to improve individual user experience [9]. The Exploratory search initiative aims to provide users with better tools for advanced information seeking tasks such as learning, investigation and analysis [6]. We believe that Semantic Web approaches would effectively address several of the aforementioned issues. However, despite continuous progress in semantic technologies, the original promise of the Semantic Web still remains unrealized mainly because there are few real-world applications that allow end-users to access, view and process Semantic Web information [8]. Since there has been little cross-fertilization between the aforementioned initiatives, our aim is to facilitate Semantic Web adoption by providing an enduser grade exploratory browser for the Semantic Web taking advantage of adaptive social and exploratory approaches. Neither BrowserRDF nor mSpace can be effectively used for complex interactive exploration of Semantic Web content, which in addition to faceted querying needs to support interactive information visualization and exploration of graphs (Semantic Web being a graph). The VisGets interface supports interactive/exploratory web search by querying it in three dimensions – time, location and topic, while also providing advanced visualization of search results [4]. However, VisGets is limited to the three predefined dimensions and does not account for information space changes and semantic content. These issues have been addressed by the generic semantic browser Tabulator [2], which allows the exploration of linked data, but again lacks the capability to perform complex search queries and adapt to individual users’ needs, while being too complex to use by end-users due to its interface and installation issues. Consequently, in our opinion, no existing approach has so far provided an effective end-user grade visualization, querying and exploration solution for (dynamic) semantic web content. 3. Uniqueness of the Approach We devised a comprehensive faceted exploration browser for the Semantic Web, which acts as an integrated tool for search when it acts like a client-side semantic search engine 2. Background & Related Work Due to the strong multidisciplinary character of our work and the novelty of exploratory search, there are, to the best of our knowledge, no other approaches our work could be directly compared to. Thus we focus on partial related approaches from individual disciplines with respect to current challenges in visualization (raw data, no default visualization), querying (complexity of semantic queries) and exploration (complex highly interlinked graph of resources) of the Semantic Web. We chose faceted browsers as the fundamental part of our approach as they have already been explored in various fields including HCI, Semantic Web and exploratory search as they integrate search and browsing, while providing support for query construction. Wilson and schraefel compared three prominent faceted browsers – Flamenco, mSpace and RelationBrowser++ [12], while Oren described the faceted browser BrowseRDF [7]. From these only mSpace and BrowseRDF use RDF data, but do not exploit the more expressive RDFS/OWL variants. Additionally, BrowseRDF provides elementary facet generation capability over simple RDF as it automatically identifies facets based on several statistical measures, but offers only very limited interaction options. None of these solutions support adaptation and thus user modeling and personalization in any way, except for manual reordering of columns in mSpace. Figure 1. Request handling of the enhanced faceted browser, extensions shown in gray. Page 1 of 5 Personalized Exploratory Search in the Semantic Web 17. 4. 2010 file://H:\Users\acm-web\acm-grand-finals-2010-tvarozek.html front-end, and for navigation when it supports navigation across a collection of “pure” information artifacts accessed via a semantic endpoint. Our browser is based on these principles: Semantic information space representation – we use a domain ontology (e.g., in RDFS or OWL as defined by W3C) to represent both the data and the metadata describing the structure of the information space. We also employ a user ontology which stores user models describing individual users’ characteristics, and an event ontology which describes the events that occur in the browser and its states during user interaction so that they can be used for subsequent automated user characteristics acquisition. Multi-paradigm exploration, which integrates view-based faceted search with content-based (query-by-example) search and traditional keyword-based search to provide users with the most suitable means to create queries or navigate the information space. This also includes visualization and navigation options for the browsing of search results such as result lists, result attribute tables and attribute/thumbnail matrices, incremental graph visualization and history visualization for revisitation and orientation support. Visual query construction, which provides support during the construction and modification of complex semantic queries in an intuitive user friendly way. Personalized recommendation to address information overload, provide guidance during complex information seeking sessions and compute relevance for interface generation. Adaptive view generation, which facilitates the generation of user interfaces necessary for exploration including facets, result overviews and exploration views, accommodates for the dynamics of the information space and preferences of individual users. Collaborative content/meta-data creation to harness the power of social wisdom. Our enhanced faceted semantic browser extends the typical request handling of faceted browsers with additional steps that perform specific tasks as outlined in Figure 1. Due to space constraints we focus on personalized recommendation and provide only a brief description of the remaining aspects of our solution. 3.1 Model for relevance evaluation Our browser logs events that occurred as results of user interaction with the current state of the browser via a specialized semantic logging service which preserves the semantics of events as opposed to traditional web server logs, which store them only implicitly in request URLs (Figure 1, bottom right). The acquired events are processed by a separate user modelling back-end [1] and in turn retrieved as an updated user model, which drives our personalization engine (Figure 1, top left). Each logged event uses our event ontology to specify the semantics of the respective user action and also references the domain and user ontologies as required. The user modelling back-end provides us with several sources of adaptation, which we employ with different weights depending on how closely related they are to the current user task: 1. In-session user behaviour – user navigation, facet and restriction selection during the current user session (i.e., user clicks). Frequent use of specific items indicates higher relevance to the current task and/or user interest in the corresponding domain concepts. 2. Short/long term user model – user characteristics acquired during multiple sessions described by their relevance to the user and the confidence in their estimation in the range <0,1>. 3. Similar/related user models are assumed to belong to users with similar needs and are thus used for relevance evaluation if user specific data is unavailable or has low confidence. Social user context can be exploited by assigning custom weights to specific relations between users resulting in social recommendation. Moreover, i