In this paper we discuss our approach to the task of Cold-Start Knowledge Base Population and the challenges associated with it. We describe our knowledge base system Lorify and each of the components necessary to populate it from unstructured text. The pivotal component for building a large-scale knowledge base is scalable cross-document coreference. We address this with a novel clustering alg...

In this demo, we showcase a set up wizard designed to bypass the cold start problem that often affects recommendation systems in the event domain. We have developed a mobile application for tourists, RelEVENT, which allows them to quickly and non-intrusively set up preferences and/or interests related to events. This will directly affect the degree to which they can receive personalized recomme...

Combining social network information with collaborative filtering recommendation algorithms has helped to alleviate some drawbacks of collaborative filtering, for example, the cold start problem, and has increased the accuracy of recommendations. However, the user coverage of recommendation for social-based recommendation is low as there is often insufficient data about explicit social relation...

In this work, we apply a clustering technique to integrate the contents of items into the item-based collaborative filtering framework. The group rating information that is obtained from the clustering result provides a way to introduce content information into collaborative recommendation and solves the cold start problem. Extensive experiments have been conducted on MovieLens data to analyze ...

The huge amount of interlinked information referring to different domains, provided by the Linked Open Data (LOD) initiative, could be e↵ectively exploited by recommender systems to deal with the cold-start and sparsity problems. In this paper we investigate the contribution of several features extracted from the Linked Open Data cloud to the accuracy of di↵erent recommendation algorithms. We f...

Online debate forums are important social media for people to voice their opinions and debate with each other. Mining user stances or viewpoints from these forums has been a popular research topic. However, most current work does not address an important problem: for a specific issue, there may not be many users participating and expressing their opinions. Despite the sparsity of user stances, ...

We present results on combining supervised and unsupervised methods to ensemble multiple systems for two popular Knowledge Base Population (KBP) tasks, Cold Start Slot Filling (CSSF) and Tri-lingual Entity Discovery and Linking (TEDL). We demonstrate that our combined system along with auxiliary features outperforms the best performing system for both tasks in the 2015 competition, several ense...

We present KELVIN, an automated system for processing a large text corpus and distilling a knowledge base about persons, organizations, and locations. We have tested the KELVIN system on several corpora, including: (a) the TAC KBP 2012 Cold Start corpus which consists of public Web pages from the University of Pennsylvania, and (b) a subset of 26k news articles taken from English Gigaword 5th e...

Recommender systems help users to find information that best fits their preferences and needs in an overloaded search space. Most recommender systems research has been focused on the accuracy improvement of recommendation algorithms. Despite this, recently new trends in recommender systems have become important research topics such as, cold start, group recommendations, context-aware recommenda...

The paper describes an ontology-based approach for integrating interactive user modeling and learning content management to deal with typical adaptation problems, such as cold start and dynamics of the user’s knowledge, in the context of the Semantic Web. An integrated OntoAIMS system is presented and its viability discussed based on user studies. The work demonstrates some novel aspects, such ...