Emir Muñoz

Emir Muñoz
National University of Ireland, Galway | NUI Galway · Department of Information Technology

Doctor of Philosophy

About

27
Publications
4,540
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
247
Citations
Additional affiliations
October 2012 - June 2013
National University of Ireland, Galway
Position
  • Research Assistant
August 2011 - December 2011
Universidad Central
Position
  • Lecturer
August 2011 - December 2011
Universidad Central
Position
  • Lecturer
Education
August 2009 - April 2011
University of Santiago, Chile
Field of study
  • Databases
March 2004 - June 2009
March 2004 - April 2011

Publications

Publications (27)
Article
Full-text available
Knowledge graph embedding (KGE) models have become popular means for making discoveries in knowledge graphs (e.g., RDF graphs) in an efficient and scalable manner. The key to success of these models is their ability to learn low-rank vector representations for knowledge graph entities and relations. Despite the rapid development of KGE models, stat...
Article
Full-text available
Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from l...
Preprint
Full-text available
Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is timeconsuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from li...
Conference Paper
Full-text available
Neural link predictors learn distributed representations of entities and relations in a knowledge graph. They are remarkably powerful in the link prediction and knowledge base completion tasks, mainly due to the learned representations that capture important statistical dependencies in the data. Recent works in the area have focused on either desig...
Preprint
Full-text available
Neural link predictors learn distributed representations of entities and relations in a knowledge graph. They are remarkably powerful in the link prediction and knowledge base completion tasks, mainly due to the learned representations that capture important statistical dependencies in the data. Recent works in the area have focused on either desig...
Chapter
Full-text available
There is an increasing number of Semantic Web knowledge bases (KBs) available on the Web, created in academia and industry alike. In this paper, we address the problem of lack of structure in these KBs due to their schema-free nature required for open environments such as the Web. Relation cardinality is an important structural aspect of data that...
Conference Paper
Learning embeddings of entities and relations using neural architectures is an effective method of performing statistical learning on large-scale relational data, such as knowledge graphs. In this paper, we consider the problem of regularizing the training of neural knowledge graph embeddings by leveraging external background knowledge. We propose...
Article
Timely identification of adverse drug reactions (ADRs) is highly important in the domains of public health and pharmacology. Early discovery of potential ADRs can limit their effect on patient lives and also make drug development pipelines more robust and efficient. Reliable in silico prediction of ADRs can be helpful in this context, and thus, it...
Conference Paper
Full-text available
Cardinality is an important structural aspect of data that has not received enough attention in the context of RDF knowledge bases (KBs). Information about cardinalities can be useful for data users and knowledge engineers when writing queries, reusing or engineering KBs. Such cardinalities can be declared using OWL and RDF constraint languages as...
Conference Paper
Full-text available
Relation paths are sequences of relations with inverse that allow for complete exploration of knowledge graphs in a two-way un-constrained manner. They are powerful enough to encode complex relationships between entities and are crucial in several contexts, such as knowledge base verification, rule mining, and link prediction. However, fundamental...
Article
Full-text available
We propose a new computational method for discovery of possible adverse drug reactions. The method consists of two key steps. First we use openly available resources to semi-automatically compile a consolidated data set describing drugs and their features (e.g., chemical structure, related targets, indications or known adverse reaction). The data s...
Conference Paper
Full-text available
RDF is structured, dynamic, and schemaless data, which enables a big deal of flexibility for Linked Data to be available in an open environment such as the Web. However, for RDF data, flexibility turns out to be the source of many data quality and knowledge representation issues. Tasks such as assessing data quality in RDF require a different set o...
Conference Paper
Full-text available
This paper describes our entry for the Linked Data Mining Challenge 2016, which poses the problem of classifying music albums as 'good' or 'bad' by mining Linked Data. The original labels are assigned according to aggregated critic scores published by the Metacritic website. To this end, the challenge provides datasets that contain the DBpedia refe...
Conference Paper
Full-text available
In this paper, we describe our contribution to the 2015 Linked Data Mining Challenge. The proposed task is concerned with the prediction of review of movies as " good " or " bad " , as does Meta-critic website based on critics' reviews. First we describe the sources used to build the training data. Although, several sources provide data about movie...
Conference Paper
Full-text available
This paper describes µRaptor, a DOM-based method to ex-tract hCard microformats from HTML pages stripped of microformat markup. µRaptor extracts DOM sub-trees, converts them into rules, and uses them to extract hCard microformats. Besides, we use co-occurring CSS classes to improve the overall precision. Results on train data show 0.96 precision an...
Conference Paper
Full-text available
Linked Data (LD) datasets (e.g., DBpedia, Freebase) are used in many knowledge extraction tasks due to the high variety of do-mains they cover. Unfortunately, many of these datasets do not provide a description for their properties and classes, reducing the users' freedom to understand, reuse or enrich them. This work attempts to fill part of this...
Conference Paper
Full-text available
The tables embedded in Wikipedia articles contain rich, semi-structured encyclopaedic content. However, the cumulative content of these tables cannot be queried against. We thus propose methods to recover the semantics of Wikipedia tables and, in particular, to extract facts from them in the form of RDF triples. Our core method uses an existing Lin...
Conference Paper
Full-text available
We introduce soft cardinality constraints which need to be satisfied on average only, and thus permit violations in a controlled manner. Starting from a highly expressive but intractable class, we establish a fragment that is maximal with respect to both expressivity and efficiency. More precisely, we characterise the associated implication problem...
Article
The increasing popularity of XML for persistent data storage, processing and exchange has triggered the demand for efficient algorithms to manage XML data. Both industry and academia have long since recognized the importance of keys in XML data management. In this paper we make a theoretical as well as a practical contribution to this area. This en...
Conference Paper
Full-text available
Tables are widely used in Wikipedia articles to display relational information --- they are inherently concise and information rich. However, aside from info-boxe s, there are no automatic methods to exploit the integrated content of these tables. We thus present DRETa: a tool that uses DBpedia as a reference knowledge-base to extract RDF triples f...
Conference Paper
Full-text available
We are currently investigating methods to triplify the content of Wikipedia's tables. We propose that existing knowledge-bases can be leveraged to semi-automatically extract high-quality facts (in the form of RDF triples) from tables embedded in Wikipedia articles (henceforth called \Wikitables"). We present a survey of Wikitables and their content...
Conference Paper
Keys are fundamental for database management, independently of the particular data model used. In particular, several notions of XML keys have been proposed over the last decade, and their expressiveness and computational properties have been analyzed in theory. In practice, however, expressive notions of XML keys with good reasoning capabilities h...
Conference Paper
Full-text available
Keys are fundamental for database management, independently of the particular data model used. In particular, several notions of XML keys have been proposed over the last decade, and their expressiveness and computational properties have been analyzed in theory. In practice, however, expressive notions of XML keys with good reasoning capabilities h...

Network

Cited By