AKSW Blog

On the 13th of February at 3 PM, Michael Röder will present the two papers “Evaluating Entity Linking: An Analysis of Current Benchmark Datasets and a Roadmap for Doing a Better Job” of van Erp et al. and “Moving away from semantic overfitting in disambiguation datasets” of Postma et al. in P702.

Abstract 1

Entity linking has become a popular task in both natural language processing and semantic web communities. However, we find that the benchmark datasets for entity linking tasks do not accurately evaluate entity linking systems. In this paper, we aim to chart the strengths and weaknesses of current benchmark datasets and sketch a roadmap for the community to devise better benchmark datasets.

Abstract 2

Entities and events in the world have no frequency, but our communication about them and the expressions we use to refer to them do have a strong frequency profile. Language expressions and their meanings follow a Zipfian distribution, featuring a small amount of very frequent observations and a very long tail of low frequent observations. Since our NLP datasets sample texts but do not sample the world, they are no exception to Zipf’s law. This causes a lack of representativeness in our NLP tasks, leading to models that can capture the head phenomena in language, but fail when dealing with the long tail. We therefore propose a referential challenge for semantic NLP that reflects a higher degree of ambiguity and variance and captures a large range of small real-world phenomena. To perform well, systems would have to show deep understanding on the linguistic tail.

The papers are available at lrec-conf.org and aclweb.org.

About the AKSW Colloquium

This event is part of a series of events about Semantic Web technology. Please see http://wiki.aksw.org/Colloquium for further information about previous and future events. As always, Bachelor and Master students are able to get points for attendance and there is complimentary coffee and cake after the session.

In the upcoming Colloquium, November the 28th at 3 PM, two papers will be presented:

Probabilistic Bag-Of-Hyperlinks Model for Entity Linking

Diego Moussallem will discuss the paper “Probabilistic Bag-Of-Hyperlinks Model for Entity Linking” by Octavian-Eugen Ganea et. al. which was accepted at WWW 2016.

Abstract:  Many fundamental problems in natural language processing rely on determining what entities appear in a given text. Commonly referenced as entity linking, this step is a fundamental component of many NLP tasks such as text understanding, automatic summarization, semantic search or machine translation. Name ambiguity, word polysemy, context dependencies and a heavy-tailed distribution of entities contribute to the complexity of this problem. We here propose a probabilistic approach that makes use of an effective graphical model to perform collective entity disambiguation. Input mentions (i.e., linkable token spans) are disambiguated jointly across an entire document by combining a document-level prior of entity co-occurrences with local information captured from mentions and their surrounding context. The model is based on simple sufficient statistics extracted from data, thus relying on few parameters to be learned. Our method does not require extensive feature engineering, nor an expensive training procedure. We use loopy belief propagation to perform approximate inference. The low complexity of our model makes this step sufficiently fast for real-time usage. We demonstrate the accuracy of our approach on a wide range of benchmark datasets, showing that it matches, and in many cases outperforms, existing state-of-the-art methods

Large-Scale Learning of Relation-Extraction Rules with Distant Supervision from the Web

Afterward, René Speck will present the paper “Large-Scale Learning of Relation-Extraction Rules with
Distant Supervision from the Web” by Sebastian Krause et. al. which was accepted at ISWC 2012.

Abstract: We present a large-scale relation extraction (RE) system which learns grammar-based RE rules from the Web by utilizing large numbers of relation instances as seed. Our goal is to obtain rule sets large enough to cover the actual range of linguistic variation, thus tackling the long-tail problem of real-world applications. A variant of distant supervision learns several relations in parallel, enabling a new method of rule filtering. The system detects both binary and n-ary relations. We target 39 relations from Freebase, for which 3M sentences extracted from 20M web pages serve as the basis for learning an average of 40K distinctive rules per relation. Employing an efficient dependency parser, the average run time for each relation is only 19 hours. We compare these rules with ones learned from local corpora of different sizes and demonstrate that the Web is indeed needed for a good coverage of linguistic variation

About the AKSW Colloquium

This event is part of a series of events about Semantic Web technology. Please see http://wiki.aksw.org/Colloquium for further information about previous and future events. As always, Bachelor and Master students are able to get points for attendance and there is complimentary coffee and cake after the session.

Hello Community! We are very pleased to announce that our paper “Radon– Rapid Discovery of Topological Relations” was accepted for presentation at the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), which will be held in February 4–9 at the Hilton San Francisco, San Francisco, California, USA.

In more detail, we will present the following paper: “Radon– Rapid Discovery of Topological Relations” Mohamed Ahmed Sherif, Kevin Dreßler, Panayiotis Smeros, and Axel-Cyrille Ngonga Ngomo

Abstract. Datasets containing geo-spatial resources are increasingly being represented according to the Linked Data principles. Several time-efficient approaches for discovering links between RDF resources have been developed over the last years. However, the time-efficient discovery of topological relations between geospatial resources has been paid little attention to. We address this research gap by presenting Radon, a novel approach for the rapid computation of topological relations between geo-spatial resources. Our approach uses a sparse tiling index in combination with minimum bounding boxes to reduce the computation time of topological relations. Our evaluation of Radon’s runtime on 45 datasets and in more than 800 experiments shows that it outperforms the state of the art by up to 3 orders of magnitude while maintaining an F-measure of 100%. Moreover, our experiments suggest that Radon scales up well when implemented in parallel.

Acknowledgments
This work is implemented in the link discovery framework LIMES and has been supported by the European Union’s H2020 research and innovation action HOBBIT (GA no. 688227) as well as the BMWI Project GEISER (project no. 01MD16014E).