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Institut
SemRes
(2016)
Documentation is very time consuming; conservators have to categorize photos, give detailed description of the procedures and justify decisions about materials. Every conservator delivers his/her own documentation to the respective monument administrations that archive it in their shelves, without any possibility to access them digitally. In this case, the access to already conducted procedures, as well as the provision of the knowledge about the methods and materials used are very difficult often almost impossible. The SemRes System provides conservators with semantic technologies in order to better structure and retrieve their knowledge and supports them in decision-making for conservation and conservation procedures.
A lot of information that is already available on the Web, or retrieved from local information sys-tems and social networks, is structured in data silos that are not semantically related. Semantic technologies make it apparent that the use of typed links that directly express their relations are an advantage for every application that can reuse the incorporated knowledge about the data. For this reason, data integration, through reengineering (e.g., triplify) or querying (e.g., D2R), is an important task in order to make information available for everyone. Thus, in order to build a semantic map of the data, we need knowledge about data items itself and the relation between heterogeneous data items. Here we present our work of providing Lexical Linked Data (LLD) through a meta-model that contains all the resources and gives the possibility to retrieve and navigate them from different per-spectives. After giving the definition of Lexical Linked Data, we describe the existing datasets we collected and the new datasets we included. Here we describe their format and show some use cases where we link lexical data, and show how to reuse and inference semantic data de-rived from lexical data. Different lexical resources (MultiWordNet, EuroWordNet, MEMODATA Lexicon, the Hamburg Methaphor Data-base) are connected to each other towards an Integrated Vocabulary for LLD that we evaluate and present.
CARS 2012 builds upon the success of the three previous editions held in conjunction with the 3rd to 5th ACM Conferences on Recommender Systems from 2009 to 2011. The 1st CARS Workshop was held in New York, NY, USA, whereas Barcelona, Spain, was home of the 2nd CARS Workshop in 2010. In 2011, the 3rd CARS workshop was held in Chicago, IL, USA.
Current search engines are used for retrieving relevant documents from the huge amount of data available and have become an essential tool for the majority of Web users. 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. In this paper, we present our system architecture and a first user study, where we show that the use of semantics can help users in finding relevant information, filtering it ad facilitating quicker access to data.
Context-aware information is widely available in various ways and is becoming more and more important for enhancing retrieval performance and recommendation results. A primary challenge is not only recommending or retrieving the most relevant items and content, but defining them ad hoc. Other relevant issues include personalizing and adapting the information and the way it is displayed to the user’s current situation and interests. Ubiquitous computing provides new means for capturing user feedback on items and offers information. This year we are particularly interested in contributions investigating how context can influence decision making in contexts such as health, finance, food, education etc. and how systems can exploit context to assert positive behavioral change.
The Reason Why
(2014)
Recommender Systems refer to those applications that offer contents or items to the users, based on their previous activity. These systems are broadly used in several fields and applications, being common that an user interact with several recommender systems during his daily activities. However, most of these systems are black boxes which users really don’t understand how to work. This lack of transparency often causes the distrust of the users. A suitable solution is to offer explanations to the user about why the system is offering such recommendations. This work deals with the problem of retrieving and evaluating explanations based on hybrid recommenders. These explanations are meant to improve the perceived recommendation quality from the user’s perspective. Along with recommended items, explanations are presented to the user to underline the quality of the recommendation. Hybrid recommenders should express relevance by providing reasons speaking for a recommended item. In this work we present an attribute explanation retrieval approach to provide these reasons and show how to evaluate such approaches. Therefore, we set up an online user study where users were asked to provide movie feedback. For each rated movie we additionally retrieved feedback about the reasons this movie was liked or disliked. With this data, explanation retrieval can be studied in general, but it can also be used to evaluate such explanations.
Context-aware information is widely available in various ways and is becoming more and more important for enhancing retrieval performance and recommendation results. The current main issue to cope with is not only recommending or retrieving the most relevant items and content, but defining them ad hoc. Other relevant issues include personalizing and adapting the information and the way it is displayed to the user's current situation and interests.
The workshop on Context-awareness in Retrieval and Recommendation is a forum for research on context-awareness in information retrieval, recommender systems and human computer interaction. The fourth iteration of the workshop was organized in conjunction to the 36th European Conference on Information Retrieval in Amsterdam, The Netherlands.
Context-aware information is widely available in various ways and is becoming more and more important for enhancing retrieval performance and recommendation results. The current main issue to cope with is not only recommending or retrieving the most relevant items and content, but defining them ad hoc. Other relevant issues include personalizing and adapting the information and the way it is displayed to the user’s current situation and interests. Ubiquitous computing further provides new means for capturing user feedback on items and providing information.
Context-aware information is widely available in various ways and is becoming more and more important for enhancing retrieval performance and recommendation results. The current main issue to cope with is not only recommending or retrieving the most relevant items and content, but defining them ad hoc. Other relevant issues include personalizing and adapting the information and the way it is displayed to the user's current situation and interests. Ubiquitous computing further provides new means for capturing user feedback on items and providing information.