17,335 research outputs found
Use of Wikipedia Categories in Entity Ranking
Wikipedia is a useful source of knowledge that has many applications in
language processing and knowledge representation. The Wikipedia category graph
can be compared with the class hierarchy in an ontology; it has some
characteristics in common as well as some differences. In this paper, we
present our approach for answering entity ranking queries from the Wikipedia.
In particular, we explore how to make use of Wikipedia categories to improve
entity ranking effectiveness. Our experiments show that using categories of
example entities works significantly better than using loosely defined target
categories
Entity Ranking in Wikipedia
The traditional entity extraction problem lies in the ability of extracting
named entities from plain text using natural language processing techniques and
intensive training from large document collections. Examples of named entities
include organisations, people, locations, or dates. There are many research
activities involving named entities; we are interested in entity ranking in the
field of information retrieval. In this paper, we describe our approach to
identifying and ranking entities from the INEX Wikipedia document collection.
Wikipedia offers a number of interesting features for entity identification and
ranking that we first introduce. We then describe the principles and the
architecture of our entity ranking system, and introduce our methodology for
evaluation. Our preliminary results show that the use of categories and the
link structure of Wikipedia, together with entity examples, can significantly
improve retrieval effectiveness.Comment: to appea
Topic modeling for entity linking using keyphrase
This paper proposes an Entity Linking system that applies a topic modeling ranking. We apply a novel approach in order to provide new relevant elements to the model. These elements are keyphrases related to the queries and gathered from a huge Wikipedia-based knowledge resourcePeer ReviewedPostprint (author’s final draft
Using Wikipedia Categories and Links in Entity Ranking
This paper describes the participation of the INRIA group in the INEX 2007 XML entity ranking and ad hoc tracks. We developed a system for ranking Wikipedia entities in answer to a query. Our approach utilises the known categories, the link structure of Wikipedia, as well as the link co-occurrences with the examples (when provided) to improve the effectiveness of entity ranking. Our experiments on the training data set demonstrate that the use of categories and the link structure of Wikipedia, together with entity examples, can significantly improve entity retrieval effectiveness. We also use our system for the ad hoc tasks by inferring target categories from the title of the query. The results were worse than when using a full-text search engine, which confirms our hypothesis that ad hoc retrieval and entity retrieval are two different tasks
Overview of the INEX 2009 Entity Ranking Track
In some situations search engine users would prefer to retrieve entities instead of just documents. Example queries include “Italian Nobel prize winners”, “Formula 1 drivers that won the Monaco Grand Prix”, or “German spoken Swiss cantons”. The XML Entity Ranking (XER) track at INEX creates a discussion forum aimed at standardizing evaluation procedures for entity retrieval. This paper describes the XER tasks and the evaluation procedure used at the XER track in 2009, where a new version of Wikipedia was used as underlying collection; and summarizes the approaches adopted by the participants
Entity Linking for Queries by Searching Wikipedia Sentences
We present a simple yet effective approach for linking entities in queries.
The key idea is to search sentences similar to a query from Wikipedia articles
and directly use the human-annotated entities in the similar sentences as
candidate entities for the query. Then, we employ a rich set of features, such
as link-probability, context-matching, word embeddings, and relatedness among
candidate entities as well as their related entities, to rank the candidates
under a regression based framework. The advantages of our approach lie in two
aspects, which contribute to the ranking process and final linking result.
First, it can greatly reduce the number of candidate entities by filtering out
irrelevant entities with the words in the query. Second, we can obtain the
query sensitive prior probability in addition to the static link-probability
derived from all Wikipedia articles. We conduct experiments on two benchmark
datasets on entity linking for queries, namely the ERD14 dataset and the GERDAQ
dataset. Experimental results show that our method outperforms state-of-the-art
systems and yields 75.0% in F1 on the ERD14 dataset and 56.9% on the GERDAQ
dataset
On Type-Aware Entity Retrieval
Today, the practice of returning entities from a knowledge base in response
to search queries has become widespread. One of the distinctive characteristics
of entities is that they are typed, i.e., assigned to some hierarchically
organized type system (type taxonomy). The primary objective of this paper is
to gain a better understanding of how entity type information can be utilized
in entity retrieval. We perform this investigation in an idealized "oracle"
setting, assuming that we know the distribution of target types of the relevant
entities for a given query. We perform a thorough analysis of three main
aspects: (i) the choice of type taxonomy, (ii) the representation of
hierarchical type information, and (iii) the combination of type-based and
term-based similarity in the retrieval model. Using a standard entity search
test collection based on DBpedia, we find that type information proves most
useful when using large type taxonomies that provide very specific types. We
provide further insights on the extensional coverage of entities and on the
utility of target types.Comment: Proceedings of the 3rd ACM International Conference on the Theory of
Information Retrieval (ICTIR '17), 201
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