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Tomasz Tylenda

Bio: Tomasz Tylenda is an academic researcher from Max Planck Society. The author has contributed to research in topics: Ontology & Analytics. The author has an hindex of 6, co-authored 8 publications receiving 435 citations.

Papers
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Proceedings ArticleDOI
28 Jun 2009
TL;DR: In this paper, the authors investigate the value of incorporating the history information available on the interactions (or links) of the current social network state and show that time-stamps of past interactions significantly improve the prediction accuracy of new and recurrent links over rather sophisticated methods proposed recently.
Abstract: Prediction of links - both new as well as recurring - in a social network representing interactions between individuals is an important problem. In the recent years, there is significant interest in methods that use only the graph structure to make predictions. However, most of them consider a single snapshot of the network as the input, neglecting an important aspect of these social networks viz., their evolution over time.In this work, we investigate the value of incorporating the history information available on the interactions (or links) of the current social network state. Our results unequivocally show that time-stamps of past interactions significantly improve the prediction accuracy of new and recurrent links over rather sophisticated methods proposed recently. Furthermore, we introduce a novel testing method which reflects the application of link prediction better than previous approaches.

227 citations

Proceedings Article
01 Jan 2013
TL;DR: This paper presents a method for discovering and semantically typing newly emerging out-ofKB entities, thus improving the freshness and recall of ontology-based IE and improving the precision and semantic rigor of open IE.
Abstract: Methods for information extraction (IE) and knowledge base (KB) construction have been intensively studied. However, a largely under-explored case is tapping into highly dynamic sources like news streams and social media, where new entities are continuously emerging. In this paper, we present a method for discovering and semantically typing newly emerging out-ofKB entities, thus improving the freshness and recall of ontology-based IE and improving the precision and semantic rigor of open IE. Our method is based on a probabilistic model that feeds weights into integer linear programs that leverage type signatures of relational phrases and type correlation or disjointness constraints. Our experimental evaluation, based on crowdsourced user studies, show our method performing significantly better than prior work.

114 citations

Proceedings ArticleDOI
11 Apr 2016
TL;DR: A linear programming based algorithm is developed to solve the attribute synonymity problem as an optimization problem on a graph, with the attribute names being the vertices and the positive and negative evidences from query logs and web table schemas as weighted edges.
Abstract: Attribute synonyms are important ingredients for keyword-based search systems. For instance, web search engines recognize queries that seek the value of an entity on a specific attribute (referred to as e+a queries) and provide direct answers for them using a combination of knowledge bases, web tables and documents. However, users often refer to an attribute in their e+a query differently from how it is referred in the web table or text passage. In such cases, search engines may fail to return relevant answers. To address that problem, we propose to automatically discover all the alternate ways of referring to the attributes of a given class of entities (referred to as attribute synonyms) in order to improve search quality. The state-of-the-art approach that relies on attribute name co-occurrence in web tables suffers from low precision. Our main insight is to combine positive evidence of attribute synonymity from query click logs, with negative evidence from web table attribute name co-occurrences. We formalize the problem as an optimization problem on a graph, with the attribute names being the vertices and the positive and negative evidences from query logs and web table schemas as weighted edges. We develop a linear programming based algorithm to solve the problem that has bi-criteria approximation guarantees. Our experiments on real-life datasets show that our approach has significantly higher precision and recall compared with the state-of-the-art.

36 citations

01 Jan 2009
TL;DR: The results unequivocally show that time-stamps of past interactions significantly improve the prediction accuracy of new and recurrent links over rather sophisticated methods proposed recently.
Abstract: Prediction of links - both new as well as recurring - in a social network representing interactions between individuals is an important problem. In the recent years, there is significant interest in methods that use only the graph structure to make predictions. However, most of them consider a single snapshot of the network as the input, neglecting an important aspect of these social networks viz., their evolution over time. In this work, we investigate the value of incorporating the history information available on the interactions (or links) of the current social network state. Our results unequivocally show that time-stamps of past interactions significantly improve the prediction accuracy of new and recurrent links over rather sophisticated methods proposed recently. Furthermore, we introduce a novel testing method which reflects the application of link prediction better than previous approaches.

29 citations

Proceedings ArticleDOI
24 Oct 2011
TL;DR: This work has extracted, disambiguated, canonicalized, and connected named entities with the YAGO ontology, and developed a visual analytics system to analyze the evolution of named entities.
Abstract: The constantly evolving Web reflects the evolution of society. Knowledge about entities (people, companies, political parties, etc.) evolves over time. Facts add up (e.g., awards, lawsuits, divorces), change (e.g., spouses, CEOs, political positions), and even cease to exist (e.g., countries split into smaller or join into bigger ones). Analytics of the evolution of the entities poses many challenges including extraction, disambiguation, and canonization of entities from large text collections as well as introduction of specific analysis and interactivity methods for the evolving entity data.In this demonstration proposal, we consider a novel problem of the evolution of named entities. To this end, we have extracted, disambiguated, canonicalized, and connected named entities with the YAGO ontology. To analyze the evolution we have developed a visual analytics system. Careful preprocessing and ranking of the ontological data allowed us to propose wide range of effective interactions and data analysis techniques including advanced filtering, contrasting timeliness of entities and drill down/roll up evolving data.

25 citations


Cited by
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Journal ArticleDOI
TL;DR: Recent progress about link prediction algorithms is summarized, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods.
Abstract: Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labeled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.

2,530 citations

Journal ArticleDOI
TL;DR: A thorough overview and analysis of the main approaches to entity linking is presented, and various applications, the evaluation of entity linking systems, and future directions are discussed.
Abstract: The large number of potential applications from bridging web data with knowledge bases have led to an increase in the entity linking research. Entity linking is the task to link entity mentions in text with their corresponding entities in a knowledge base. Potential applications include information extraction, information retrieval, and knowledge base population. However, this task is challenging due to name variations and entity ambiguity. In this survey, we present a thorough overview and analysis of the main approaches to entity linking, and discuss various applications, the evaluation of entity linking systems, and future directions.

702 citations

Book ChapterDOI
17 Mar 2011
TL;DR: This article surveys some representative link prediction methods by categorizing them by the type of models, largely considering three types of models: first, the traditional (non-Bayesian) models which extract a set of features to train a binary classification model, and second, the probabilistic approaches which model the joint-probability among the entities in a network by Bayesian graphical models.
Abstract: Link prediction is an important task for analying social networks which also has applications in other domains like, information retrieval, bioinformatics and e-commerce There exist a variety of techniques for link prediction, ranging from feature-based classification and kernel-based method to matrix factorization and probabilistic graphical models These methods differ from each other with respect to model complexity, prediction performance, scalability, and generalization ability In this article, we survey some representative link prediction methods by categorizing them by the type of the models We largely consider three types of models: first, the traditional (non-Bayesian) models which extract a set of features to train a binary classification model Second, the probabilistic approaches which model the joint-probability among the entities in a network by Bayesian graphical models And, finally the linear algebraic approach which computes the similarity between the nodes in a network by rank-reduced similarity matrices We discuss various existing link prediction models that fall in these broad categories and analyze their strength and weakness We conclude the survey with a discussion on recent developments and future research direction

566 citations

Journal ArticleDOI
TL;DR: The historical events that lead to the interweaving of data and knowledge are tracked to help improve knowledge and understanding of the world around us.
Abstract: In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.

560 citations

Journal ArticleDOI
TL;DR: This survey will review the general-purpose techniques at the heart of the link prediction problem, which can be complemented by domain-specific heuristic methods in practice.
Abstract: Networks have become increasingly important to model complex systems composed of interacting elements. Network data mining has a large number of applications in many disciplines including protein-protein interaction networks, social networks, transportation networks, and telecommunication networks. Different empirical studies have shown that it is possible to predict new relationships between elements attending to the topology of the network and the properties of its elements. The problem of predicting new relationships in networks is called link prediction. Link prediction aims to infer the behavior of the network link formation process by predicting missed or future relationships based on currently observed connections. It has become an attractive area of study since it allows us to predict how networks will evolve. In this survey, we will review the general-purpose techniques at the heart of the link prediction problem, which can be complemented by domain-specific heuristic methods in practice.

521 citations