13,853 research outputs found
Semantic user profiling techniques for personalised multimedia recommendation
Due to the explosion of news materials available through broadcast and other channels, there is an increasing need for personalised news video retrieval. In this work, we introduce a semantic-based user modelling technique to capture users’ evolving information needs. Our approach exploits implicit user interaction to capture long-term user interests in a profile. The organised interests are used to retrieve and recommend news stories to the users. In this paper, we exploit the Linked Open Data Cloud to identify similar news stories that match the users’ interest. We evaluate various recommendation parameters by introducing a simulation-based evaluation scheme
Use of Wikipedia Categories in Entity Ranking
Wikipedia is a useful source of knowledge that has many applications in
language processing and knowledge representation. The Wikipedia category graph
can be compared with the class hierarchy in an ontology; it has some
characteristics in common as well as some differences. In this paper, we
present our approach for answering entity ranking queries from the Wikipedia.
In particular, we explore how to make use of Wikipedia categories to improve
entity ranking effectiveness. Our experiments show that using categories of
example entities works significantly better than using loosely defined target
categories
Enriching Existing Test Collections with OXPath
Extending TREC-style test collections by incorporating external resources is
a time consuming and challenging task. Making use of freely available web data
requires technical skills to work with APIs or to create a web scraping program
specifically tailored to the task at hand. We present a light-weight
alternative that employs the web data extraction language OXPath to harvest
data to be added to an existing test collection from web resources. We
demonstrate this by creating an extended version of GIRT4 called GIRT4-XT with
additional metadata fields harvested via OXPath from the social sciences portal
Sowiport. This allows the re-use of this collection for other evaluation
purposes like bibliometrics-enhanced retrieval. The demonstrated method can be
applied to a variety of similar scenarios and is not limited to extending
existing collections but can also be used to create completely new ones with
little effort.Comment: Experimental IR Meets Multilinguality, Multimodality, and Interaction
- 8th International Conference of the CLEF Association, CLEF 2017, Dublin,
Ireland, September 11-14, 201
Keyword based categorisation of diary entries to support personal Internet content pre-caching on mobile devices
This paper presents a study into the effectiveness of our algorithm for automatic categorisation of real users' diary entries, as a first step towards personal Internet content pre-caching on mobile devices. The study reports an experiment comparing trial subjects allocations of 99 diary entries to those predicted by a keyword-based algorithm. While leaving considerable grounds for improvement, results are positive and show pave the way for supporting mobile services based on categorising users' diary entries
Two-phased knowledge formalisation for hydrometallurgical gold ore process recommendation and validation
This paper describes an approach to externalising and formalising expert knowledge involved in the design and evaluation of hydrometallurgical process chains for gold ore treatment. The objective was to create a case-based reasoning application for recommending and validating a treatment process of gold ores. We describe a twofold approach. Formalising human expert knowledge about gold mining situations enables the retrieval of similar mining contexts and respective process chains, based on prospection data gathered from a potential gold mining site. Secondly, empirical knowledge on hydrometallurgical treatments is formalised. This enabled us to evaluate and, where needed, redesign the process chain that was recommended by the first aspect of our approach. The main problems with formalisation of knowledge in the domain of gold ore refinement are the diversity and the amount of parameters used in literature and by experts to describe a mining context. We demonstrate how similarity knowledge was used to formalise literature knowledge. The evaluation of data gathered from experiments with an initial prototype workflow recommender, Auric Adviser, provides promising results
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