Mining and Leveraging Background Knowledge for Improving Named Entity Linking

Abstract

Knowledge-rich Information Extraction (IE) methods aspire towards combining classical IE with background knowledge obtained from third-party resources. Linked Open Data repositories that encode billions of machine readable facts from sources such as Wikipedia play a pivotal role in this development. The recent growth of Linked Data adoption for Information Extraction tasks has shed light on many data quality issues in these data sources that seriously challenge their usefulness such as completeness, timeliness and semantic correctness. Information Extraction methods are, therefore, faced with problems such as name variance and type confusability. If multiple linked data sources are used in parallel, additional concerns regarding link stability and entity mappings emerge. This paper develops methods for integrating Linked Data into Named Entity Linking methods and addresses challenges in regard to mining knowledge from Linked Data, mitigating data quality issues, and adapting algorithms to leverage this knowledge. Finally, we apply these methods to Recognyze, a graph-based Named Entity Linking (NEL) system, and provide a comprehensive evaluation which compares its performance to other well-known NEL systems, demonstrating the impact of the suggested methods on its own entity linking performance.

Named Entity Extraction for Knowledge Graphs: A Literature Overview

“…It tackles the main issue of NEL evaluation and clarifies how two IRIs could be compared to each other and evaluated without being limited to a particular KB. However, GERBIL does not offer any additional explanations (such as error analysis) for each mention [120].…”

“…Recently proposed multi-and cross-lingual approaches such as [112], [119] can train a model on a richly-resourced language and then apply it to a more sparsely-resourced one. Other approaches to multi-and cross-lingual NEL [93], [120]- [122] need to be re-trained for every new language and, compared to the single-language approaches, accuracy is still weak [121]- [123]. One reason may be that environment and structure change from one language to another.…”

“…Hence, it can be challenging to adapt existing systems to build domain-specific applications [21]. According to [120], each tool constructs its KG differently. Consequently, it is challenging to utilize those tools for linking from different KBs.…”

“…However, choosing the best NER-model to use for NEL is still a challenging task due to the hardness of comparing the dataset used to train the system and the dataset that needs to be processed [47]. Unless end-to-end methods are used, obtaining good NEL results requires improving and making use of the most recent NER approaches [120].…”

Verification of Web Videos Through Analysis of Their Online Context

“…Posting a video and claiming that it was captured at a location other than the actual one is a common case of disinformation. In CAA, location extraction from the text metadata of the video are based on Recognyze [46]. Recognyze identifies location-related named entities by searching and aligning them with established knowledge bases such as GeoNames and DBpedia, and refines the results by exploiting structure and context to solve abbreviations and ambiguities, achieving state-of-the-art performance.…”