"Things and Strings" accepted at EKAW 2016

Tags: 
Place name disambiguation / natural language processing / lda / wikipedia / dbpedia / linked data

Our paper titled "Things and Strings: Improving Place Name Disambiguation from Short Texts by Combining Entity Co-Occurrence with Topic Modeling" by Yiting Ju, Benjamin Adams, Krzysztof Janowicz, Yingjie Hu, Bo Yan and Grant McKenzie was accepted at the 20th International Conference on Knowledge Engineering and Knowledge Management (EKAW). This year EKAW will take place in Bologna, Italy. More on the paper below:


Place name disambiguation is the task of correctly identifying a place from a set of places sharing a common name. It contributes to tasks such as knowledge extraction, query answering, geographic information retrieval, and automatic tagging. Disambiguation quality relies on the ability to correctly identify and interpret contextual clues, complicating the task for short texts. Here we propose a novel approach to the disambiguation of place names from short texts that integrates two models: entity co-occurrence and topic modeling. The first model uses Linked Data to identify related entities to improve disambiguation quality. The second model uses topic modeling to differentiate places based on the terms used to describe them. We evaluate our approach using a corpus of short texts, determine the suitable weight between models, and demonstrate that a combined model outperforms benchmark systems such as DBpedia Spotlight and Open Calais in terms of F1-score and Mean Reciprocal Rank.


Full paper online soon.