72,903 research outputs found

    Enhancing Recommendations in Specialist Search Through Semantic-based Techniques and Multiple Resources

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    Information resources abound on the Internet, but mining these resources is a non-trivial task. Such abundance has raised the need to enhance services provided to users, such as recommendations. The purpose of this work is to explore how better recommendations can be provided to specialists in specific domains such as bioinformatics by introducing semantic techniques that reason through different resources and using specialist search techniques. Such techniques exploit semantic relations and hidden associations that occur as a result of the information overlapping among various concepts in multiple bioinformatics resources such as ontologies, websites and corpora. Thus, this work introduces a new method that reasons over different bioinformatics resources and then discovers and exploits different relations and information that may not exist in the original resources. Such relations may be discovered as a consequence of the information overlapping, such as the sibling and semantic similarity relations, to enhance the accuracy of the recommendations provided on bioinformatics content (e.g. articles). In addition, this research introduces a set of semantic rules that are able to extract different semantic information and relations inferred among various bioinformatics resources. This project introduces these semantic-based methods as part of a recommendation service within a content-based system. Moreover, it uses specialists' interests to enhance the provided recommendations by employing a method that is collecting user data implicitly. Then, it represents the data as adaptive ontological user profiles for each user based on his/her preferences, which contributes to more accurate recommendations provided to each specialist in the field of bioinformatics

    Semantically-enhanced recommendations in cultural heritage

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    In the Web 2.0 environment, institutes and organizations are starting to open up their previously isolated and heterogeneous collections in order to provide visitors with maximal access. Semantic Web technologies act as instrumental in integrating these rich collections of metadata by defining ontologies which accommodate different representation schemata and inconsistent naming conventions over the various vocabularies. Facing the large amount of metadata with complex semantic structures, it is becoming more and more important to support visitors with a proper selection and presentation of information. In this context, the Dutch Science Foundation (NWO) funded the Cultural Heritage Information Personalization (CHIP) project in early 2005, as part of the Continuous Access to Cultural Heritage (CATCH) program in the Netherlands. It is a collaborative project between the Rijksmuseum Amsterdam, the Eindhoven University of Technology and the Telematica Instituut. The problem statement that guides the research of this thesis is as follows: Can we support visitors with personalized access to semantically-enriched collections? To study this question, we chose cultural heritage (museums) as an application domain, and the semantically rich background knowledge about the museum collection provides a basis to our research. On top of it, we deployed user modeling and recommendation technologies in order to provide personalized services for museum visitors. Our main contributions are: (i) we developed an interactive rating dialog of artworks and art concepts for a quick instantiation of the CHIP user model, which is built as a specialization of FOAF and mapped to an existing event model ontology SEM; (ii) we proposed a hybrid recommendation algorithm, combining both explicit and implicit relations from the semantic structure of the collection. On the presentation level, we developed three tools for end-users: Art Recommender, Tour Wizard and Mobile Tour Guide. Following a user-centered design cycle, we performed a series of evaluations with museum visitors to test the effectiveness of recommendations using the rating dialog, different ways to build an optimal user model and the prediction accuracy of the hybrid algorithm. Chapter 1 introduces the research questions, our approaches and the outline of this thesis. Chapter 2 gives an overview of our work at the first stage. It includes (i) the semantic enrichment of the Rijksmuseum collection, which is mapped to three Getty vocabularies (ULAN, AAT, TGN) and the Iconclass thesaurus; (ii) the minimal user model ontology defined as a specialization of FOAF, which only stores user ratings at that time, (iii) the first implementation of the content-based recommendation algorithm in our first tool, the CHIP Art Recommender. Chapter 3 presents two other tools: Tour Wizard and Mobile Tour Guide. Based on the user's ratings, the Web-based Tour Wizard recommends museum tours consisting of recommended artworks that are currently available for museum exhibitions. The Mobile Tour Guide converts recommended tours to mobile devices (e.g. PDA) that can be used in the physical museum space. To connect users' various interactions with these tools, we made a conversion of the online user model stored in RDF into XML format which the mobile guide can parse, and in this way we keep the online and on-site user models dynamically synchronized. Chapter 4 presents the second generation of the Mobile Tour Guide with a real time routing system on different mobile devices (e.g. iPod). Compared with the first generation, it can adapt museum tours based on the user's ratings artworks and concepts, her/his current location in the physical museum and the coordinates of the artworks and rooms in the museum. In addition, we mapped the CHIP user model to an existing event model ontology SEM. Besides ratings, it can store additional user activities, such as following a tour and viewing artworks. Chapter 5 identifies a number of semantic relations within one vocabulary (e.g. a concept has a broader/narrower concept) and across multiple vocabularies (e.g. an artist is associated to an art style). We applied all these relations as well as the basic artwork features in content-based recommendations and compared all of them in terms of usefulness. This investigation also enables us to look at the combined use of artwork features and semantic relations in sequence and derive user navigation patterns. Chapter 6 defines the task of personalized recommendations and decomposes the task into a number of inference steps for ontology-based recommender systems, from a perspective of knowledge engineering. We proposed a hybrid approach combining both explicit and implicit recommendations. The explicit relations include artworks features and semantic relations with preliminary weights which are derived from the evaluation in Chapter 5. The implicit relations are built between art concepts based on instance-based ontology matching. Chapter 7 gives an example of reusing user interaction data generated by one application into another one for providing cross-application recommendations. In this example, user tagging about cultural events, gathered by iCITY, is used to enrich the user model for generating content-based recommendations in the CHIP Art Recommender. To realize full tagging interoperability, we investigated the problems that arise in mapping user tags to domain ontologies, and proposed additional mechanisms, such as the use of SKOS matching operators to deal with the possible mis-alignment of tags and domain-specific ontologies. We summarized to what extent the problem statement and each of the research questions are answered in Chapter 8. We also discussed a number of limitations in our research and looked ahead at what may follow as future work

    Enriching ontological user profiles with tagging history for multi-domain recommendations

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    Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals' tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites

    Semantic data mining and linked data for a recommender system in the AEC industry

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    Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations

    Semantic Grounding Strategies for Tagbased Recommender Systems

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    Recommender systems usually operate on similarities between recommended items or users. Tag based recommender systems utilize similarities on tags. The tags are however mostly free user entered phrases. Therefore, similarities computed without their semantic groundings might lead to less relevant recommendations. In this paper, we study a semantic grounding used for tag similarity calculus. We show a comprehensive analysis of semantic grounding given by 20 ontologies from different domains. The study besides other things reveals that currently available OWL ontologies are very narrow and the percentage of the similarity expansions is rather small. WordNet scores slightly better as it is broader but not much as it does not support several semantic relationships. Furthermore, the study reveals that even with such number of expansions, the recommendations change considerably.Comment: 13 pages, 5 figure

    An event distribution platform for recommending cultural activities

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    Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

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    Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation
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