Semantic Modeling of Analytic-based Relationships with Direct Qualification

Successfully modeling state and analytics-based semantic relationships of documents enhances representation, importance, relevancy, provenience, and priority of the document. These attributes are the core elements that form the machine-based knowledge representation for documents. However, modeling document relationships that can change over time can be inelegant, limited, complex or overly burdensome for semantic technologies. In this paper, we present Direct Qualification (DQ), an approach for modeling any semantically referenced document, concept, or named graph with results from associated applied analytics. The proposed approach supplements the traditional subject-object relationships by providing a third leg to the relationship; the qualification of how and why the relationship exists. To illustrate, we show a prototype of an event-based system with a realistic use case for applying DQ to relevancy analytics of PageRank and Hyperlink-Induced Topic Search (HITS).Comment: Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015

Topical word importance for fast keyphrase extraction

We propose an improvement on a state-of-the-art keyphrase extraction algorithm, Topical PageRank (TPR), incorporating topical information from topic models. While the original algorithm requires a random walk for each topic in the topic model being used, ours is independent of the topic model, computing but a single PageRank for each text regardless of the amount of topics in the model. This increases the speed drastically and enables it for use on large collections of text using vast topic models, while not altering performance of the original algorithm