1,721,178 research outputs found
Entity linking on philosophical documents
No full textEntity Linking consists in automatically enriching a document by detecting the text fragments mentioning a given entity in an external knowledge base, e.g., Wikipedia. This problem is a hot research topic due to its impact in several text-understanding related tasks. However, its application to some specfiic, restricted topic domains has not received much attention. In this work we study how we can improve entity linking performance by exploiting a domain-oriented knowledge base, obtained by filtering out from Wikipedia the entities that are not relevant for the target domain. We focus on the philosophical domain, and we experiment a combination of three different entity filtering approaches: one based on the Philosophy" category of Wikipedia, and two based on similarity metrics between philosophical documents and the textual description of the entities in the knowledge base, namely cosine similarity and Kullback-Leibler divergence. We apply traditional entity linking strategies to the domainoriented knowledge base obtained with these filtering techniques. Finally, we use the resulting enriched documents to conduct a preliminary user study with an expert in the area
Dataset Popularity Prediction for Caching of CMS Big Data
No full textThe Compact Muon Solenoid (CMS) expe- riment at the European Organization for Nuclear Research (CERN) deploys its data collections, simula- tion and analysis activities on a distributed computing infrastructure involving more than 70 sites worldwide. The historical usage data recorded by this large infras- tructure is a rich source of information for system tun- ing and capacity planning. In this paper we investigate how to leverage machine learning on this huge amount of data in order to discover patterns and correlations useful to enhance the overall efficiency of the dis- tributed infrastructure in terms of CPU utilization and task completion time. In particular we propose a scal- able pipeline of components built on top of the Spark engine for large-scale data processing, whose goal is collecting from different sites the dataset access logs, organizing them into weekly snapshots, and training, on these snapshots, predictive models able to fore- cast which datasets will become popular over time. The high accuracy achieved indicates the ability of the learned model to correctly separate popular datasets from unpopular ones. Dataset popularity predictions are then exploited within a novel data caching policy, called PPC (Popularity Prediction Caching). We eval- uate the performance of PPC against popular caching policy baselines like LRU (Least Recently Used). The experiments conducted on large traces of real dataset accesses show that PPC outperforms LRU reducing the number of cache misses up to 20% in some sites
Compressed indexes for fast search of semantic data (extended abstract)
Full text linkThe sheer increase in volume of RDF data demands efficient solutions for the triple indexing problem, that is devising a compressed data structure to compactly represent RDF triples by guaranteeing, at the same time, fast pattern matching operations. This problem lies at the heart of delivering good practical performance for the resolution of complex SPARQL queries on large RDF datasets. We propose a trie-based index layout to solve the problem and introduce two novel techniques to reduce its space of representation for improved effectiveness. The extensive experimental analysis reveals that our best space/time trade-off configuration substantially outperforms existing solutions at the state-of-the-art, by taking 30-60% less space and speeding up query execution by a factor of 2-81 times
Learning to Rank for Non Independent and Identically Distributed Datasets
No full textWith the growing data privacy concerns, federated machine learning algorithms capable of preserving the confidentiality of sensitive information while enabling collaborative model training across decentralized data sources are attracting increasing interest. In this paper, we address the problem of collaboratively learning effective ranking models from non-independently and identically distributed (non-IID) training data owned by distinct search clients. We assume that the learning agents cannot access each other's data, and that the models learned from local datasets might be biased or underperforming due to a skewed distribution of certain document features or query topics in the learning-to-rank training data. Thus, we aim to instill in the local ranking model learned from local data the knowledge from other models to obtain a more robust ranker capable of effectively handling documents and queries underrepresented in the local collection. To achieve this, we explore different methods for merging the ranking models, thus obtaining in each client a model that excels in ranking documents from the local data distribution but also performs well on queries retrieving documents having distributions typical of a partner's node. In particular, our findings suggest that by relying on a linear combination of the local models, we can improve IR models effectiveness by up to +17.92% in NDCG@10 (moving from 0.619 to 0.730), and by up to +19.64% in MAP (moving from 0.713 to 0.853)
Representing document lengths with identifiers
No full textThe length of each indexed document is needed by most common text retrieval scoring functions to rank it with respect to the current query. For efficiency purposes information retrieval systems maintain this information in the main memory. This paper proposes a novel strategy to encode the length of each document directly in the document identifier, thus reducing main memory demand. The technique is based on a simple document identifier assignment method and a function allowing the approximate length of each indexed document to be computed analytically
Popularity-Based Caching of CMS Datasets
No full textThe distributed monitoring infrastructure of the Compact Muon Solenoid (CMS) experiment at the European Organization for Nuclear Research (CERN) records on a Hadoop infrastructures a broad variety of computing and storage logs. They represent a valuable source of information for system tuning and capacity planning. In this paper we analyze machine learning (ML) techniques on large amount of traces to discover patterns and correlations useful to classify the popularity of experiment-related datasets. We implement a scalable pipeline of Spark components which collect the dataset access logs from heterogeneous monitoring sources and group them into weekly snapshots organized by CMS sites. Predictive models are trained on these snapshots and forecast which dataset will become popular over time. Dataset popularity predictions are then used to experiment a novel strategy of data caching, called Popularity Prediction Caching (PPC). We compare the hit rates of PPC with those produced by well known caching policies. We demonstrate how the performance improvement is as high as 20% in some sites
Compressed Indexes for Fast Search of Semantic Data
Full text linkThe sheer increase in volume of RDF data demands efficient solutions for the triple indexing problem, that is to devise a compressed data structure to compactly represent RDF triples by guaranteeing, at the same time, fast pattern matching operations. This problem lies at the heart of delivering good practical performance for the resolution of complex SPARQL queries on large RDF datasets. In this work, we propose a trie-based index layout to solve the problem and introduce two novel techniques to reduce its space of representation for improved effectiveness. The extensive experimental analysis, conducted over a wide range of publicly available real-world datasets, reveals that our best space/time trade-off configuration substantially outperforms existing solutions at the state-of-the-art, by taking 30-60 percent less space and speeding up query execution by a factor of 2 - 81×
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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