# Capturing Knowledge of Emerging Entities From Extended Search Snippets

arXiv: https://arxiv.org/abs/2104.01105 · HTML: https://mlsanity.com/paper/2104.01105/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 17 Mar 2021.

## Builds on (most cited first, 9 of 9)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Ultra-Fine Entity Typing](https://mlsanity.com/paper/1807.04905/) (2018)
- [AceKG: A Large-scale Knowledge Graph for Academic Data Mining](https://mlsanity.com/paper/1807.08484/) (2018)
- [Noise Mitigation for Neural Entity Typing and Relation Extraction](https://mlsanity.com/paper/1612.07495/) (2016)
- [Corpus-level Fine-grained Entity Typing](https://mlsanity.com/paper/1708.02275/) (2017)
- [Concept2vec: Metrics for Evaluating Quality of Embeddings for Ontological Concepts](https://mlsanity.com/paper/1803.04488/) (2018)
- [Errata: Distant Supervision for Relation Extraction with Matrix Completion](https://mlsanity.com/paper/1411.4455/) (2014)
- [Parallax: Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae](https://mlsanity.com/paper/1905.12099/) (2019)

## Abstract (arXiv)

Google and other search engines feature the entity search by representing a knowledge card summarizing related facts about the user-supplied entity. However, the knowledge card is limited to certain entities that have a Wiki page or an entry in encyclopedias such as Freebase. The current encyclopedias are limited to highly popular entities, which are far fewer compared with the emerging entities. Despite the availability of knowledge about the emerging entities on the search results, yet there are no approaches to capture, abstract, summerize, fuse, and validate fragmented pieces of knowledge about them. Thus, in this paper, we develop approaches to capture two types of knowledge about the emerging entities from a corpus extended from top-n search snippets of a given emerging entity. The first kind of knowledge identifies the role(s) of the emerging entity as, e.g., who is s/he? The second kind captures the entities closely associated with the emerging entity. As the testbed, we considered a collection of 20 emerging entities and 20 popular entities as the ground truth. Our approach is an unsupervised approach based on text analysis and entity embeddings. Our experimental studies show promising results as the accuracy of more than 87% for recognizing entities and 75% for ranking them. Besides 87% of the entailed types were recognizable. Our testbed and source code is available on Github https://github.com/sunnyUD/researchsourcecode.