A Model for Ranking Entities and Its Application to Wikipedia

Lecture Notes in Computer Science, 2008

The Knowledge Media Institute of the Open University participated in the entity ranking and entity list completion tasks of the Entity Ranking Track in INEX 2007. In both the entity ranking and entity list completion tasks, we have considered document features in addition to a basic document content based relevance model. These document features include categorizations of documents, relevance of category names to the query, and hierarchical relations between categories. Furthermore, based on our TREC2006 and 2007 expert search approach, we applied a co-occurrence based entity association discovery model to the two tasks based on the assumption that relevant entities often cooccur with query terms or given relevant entities in documents. Our initial experimental results show that, by considering the predefined category, its children and grandchildren in the document content based relevance model, the performance of our entity ranking approach can be significantly improved. Consideration of the predefined category's parents, a category name based relevance model, and the co-occurrence model is not shown to be helpful in entity ranking and list completion, respectively.

Proceedings of the 2009 …, 2009

TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2015

In this paper we propose a novel framework using features extracted from Wikipedia for the task of matching named entities. We present how the employed features are extracted from Wikipedia and how its structure is utilized. We describe how a term-concepts table constructed from Wikipedia and the redirect links are integrated in the framework. In addition, the internal links within Wikipedia along with the categories structure are also used to compute the relatedness between concepts. We evaluate the built framework and report its performance in the applications of word sense disambiguation and named entities matching. The system performance is compared against other learning-based stateof-the-art systems and its reported results are found to be competitive. We also present a method in this paper for named entities matching that is context-independent and compare its results with those of other systems.

Findings of the Association for Computational Linguistics: ACL 2022

There is little work on entity linking (EL) over 001 Wikidata, even though it is the most extensive 002 crowdsourced knowledge base. The scale of 003 Wikidata can open up many new real-world ap-004 plications, but its massive number of entities 005 also makes EL challenging. To effectively nar-006 row down the search space, we propose a novel 007 candidate retrieval paradigm based on entity 008 profiling. Wikidata entities and their textual 009 fields are first indexed into a text search engine 010 (e.g., Elasticsearch). During inference, given 011 a mention and its context, we use a sequence-012 to-sequence (seq2seq) model to generate the 013 profile of the target entity, which consists of 014 its title and description. We use the profile 015 to query the indexed search engine to retrieve 016 candidate entities. Our approach complements 017 the traditional approach of using a Wikipedia 018 anchor-text dictionary, enabling us to further 019 design a highly effective hybrid method for 020 candidate retrieval. Combined with a sim-021 ple cross-attention reranker, our complete EL 022 framework achieves state-of-the-art results on 023 three Wikidata-based datasets and strong per-024 formance on TACKBP-2010 1. 025 1 Introduction 026 Entity linking (EL) is the task of mapping entity 027 mentions in a document to standard referent enti-028 ties in a target knowledge base (KB) (Dill et al.,

Lecture Notes in Computer Science, 2008

Many realistic user tasks involve the retrieval of specific entities instead of just any type of documents. Examples of information needs include 'Countries where one can pay with the euro' or 'Impressionist art museums in The Netherlands'. The Initiative for Evaluation of XML Retrieval (INEX) started the XML Entity Ranking track (INEX-XER) to create a test collection for entity retrieval in Wikipedia. Entities are assumed to correspond to Wikipedia entries. The goal of the track is to evaluate how well systems can rank entities in response to a query; the set of entities to be ranked is assumed to be loosely defined either by a generic category (entity ranking) or by some example entities (list completion). This track overview introduces the track setup, and discusses the implications of the new relevance notion for entity ranking in comparison to ad hoc retrieval.

international journal of engineering trends and technology, 2014

Measuring relationships between pairs of data objects in Wikipedia is challenging task in real world data. For the Wikipedia graph, consisting of the articles together with the hyperlinks between them, the preferential attachment rule explains portion of the constitution, but instinct says that the themes of each article also performs a crucial position. This proposed system concentrate on small datasets extracted from the Wikipedia database. The matter of researching individual search space intents has attracted intensive consideration from both enterprise and academia. However, state-of-the-art intent researching techniques go through from different drawbacks when only utilizing a unmarried variety of statistics supply. For instance, query textual content has issue in distinguishing ambiguous queries; search space log is bias for a order of seek outcome and users noisy click on behaviors. In this proposed system, we'll use three kinds of similar objects, namely queries, websit...

Lecture Notes in Computer Science, 2009

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.

2008

Building an expert finding system is very important for many applications especially in the academic environment. Previous work uses e-mails or web pages as corpus to analyze the expertise for each expert. In this paper, we present an Expert Finding System, abbreviated as EFS to build experts' profiles by using their journal publications. For a given proposal, the EFS first looks up the Wikipedia web site to get relative link information, and then list and rank all associated experts by using those information. In our experiments, we use a real-world dataset which comprises of 882 people and 13,654 papers, and are categorized into 9 expertise domains. Our experimental results show that the EFS works well on several expertise domains like "Artificial Intelligence" and "Image & Pattern Recognition" etc.

Lecture Notes in Computer Science, 2008

The INEX 2007 evaluation was based on the Wikipedia collection in XML format. In this paper we describe some modifications to the GPX search engine and the approach taken in the Ad-hoc and the Link-the-Wiki tracks. The GPX retrieval strategy is based on the construction of a collection sub-tree, consisting of all nodes that contain one or more of the search terms. Nodes containing search terms are assigned a score using the GPX ranking scheme which incorporates an extended TF-IDF variant. In earlier version of GPX scores were recursively propagated from text containing nodes, through ancestors, all the way to the document root of the XML tree. In this paper we describe a simplification whereby the score of each node is computed directly, doing away with the score propagation mechanism. Preliminary results indicate improved performance. The GPX search engine was used in the Link-the-Wiki track to identify prospective incoming links to new Wikipedia pages. We also describe a simple and efficient approach to the identification of prospective outgoing links in new Wikipedia pages. We present preliminary evaluation results.

Information Reuse and …, 2010

Searching for information about individual entities such as persons, locations, events, is an important activity in Internet search today, and is in its core a very semantic-oriented task. Several ways for accessing such information exist, but for locating entity-specific information, search engines are the most commonly used approach. In this context, keyword queries are the primary means of retrieving information about a specific entity. We believe that an important first step of performing such a task is to understand what type of entity the user is looking for. We call this process Entity Type Disambiguation. In this paper we present a Naive Bayesian Model for entity type disambiguation that explores our assumption that an entity type can be inferred from the attributes a user specifies in a search query. The model has been applied to queries provided by a large sample of participants in an experiment performing an entity search task. The beneficial impact of this approach for the development of new search systems is discussed. ⋆ This work is partially supported by the by the FP7 EU Large-scale Integrating Project OKKAM -Enabling a Web of Entities (contract no. ICT-215032). For more details, visit http://www.okkam.org.