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Recently, a large number of dataset repositories, catalogs and portals are emerging in the science and government realms. Once a large number of datasets are published on such data portals, the question arises how to retrieve datasets satisfying an information need. In this paper, we present an approach for retrieving datasets according to user queries. We define dataset retrieval as a specialization...
We propose and study novel text representation features created from parse tree structures. Unlike the traditional parse tree features which include all the attached syntactic categories to capture linguistic properties of text, the new features are solely or primarily defined based on the tree structure, and thus better reflect the pure structural properties of parse trees. We hypothesize that these...
In recent years, many linked open datasets have been published, enabling data access and interoperability at a new scale. However, reusing rules, queries and processes is still difficult: applications are usually developed from the ground up, reinventing queries, inferences and operations that others might have created before. To address this issue, we introduce reusable inference modules, created...
This paper describes the development of an emoticon recommendation system based on emoticons numerically categorized by emotion. The emoticon recommendation system aims to help users express their feelings in computer-mediated communication by recommending emoticons appropriate to user input. In order to develop this system, the original emoticon database, a table of emoticons with the points expressed...
We present an approach to diversify entity search by utilizing semantics present and inferred from the initial entity search results. Our approach makes use of ontologies and independent component analysis of the entity descriptions to reveal direct and latent semantic connections between the entities present in the initial search results. The semantic connections are then used to sample a set of...
Despite the widespread diffusion of structured data sources and the public acclaim of the Linked Open Data initiative, a preponderant amount of information remains nowadays available only in unstructured form, both on the Web and within organizations. While different in form, structured and unstructured contents speak about the very same entities of the world, their properties and relations, still,...
We present computational models capable of understanding and conveying concepts based on word associations. We discover word associations automatically using corpus-based semantic models with Wikipedia as the corpus. The best model effectively combines corpus-based models with preexisting databases of free association norms gathered from human volunteers. We use this model to play human-directed and...
In this paper, we investigate the use of Frame Semantics for improving the performance of linking of non-PLO (Person, Location, Organization) entities to Wikipedia articles. We propose an architecture for a system using Frame Semantics and Select ional Preferences (SP) to improve precision of linking non-PLO entities. We present performance results, which suggest that our system provides a mechanism...
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