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"Topic": models, code, and papers

RDF2Vec-based Classification of Ontology Alignment Changes

May 23, 2018
Matthias Jurisch, Bodo Igler

When ontologies cover overlapping topics, the overlap can be represented using ontology alignments. These alignments need to be continuously adapted to changing ontologies. Especially for large ontologies this is a costly task often consisting of manual work. Finding changes that do not lead to an adaption of the alignment can potentially make this process significantly easier. This work presents an approach to finding these changes based on RDF embeddings and common classification techniques. To examine the feasibility of this approach, an evaluation on a real-world dataset is presented. In this evaluation, the best classifiers reached a precision of 0.8.

* 6 pages, accepted at Workshop on Deep Learning for Knowledge Graphs and Semantic Technologies (DL4KGS) 

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Thread Reconstruction in Conversational Data using Neural Coherence Models

Jul 25, 2017
Dat Tien Nguyen, Shafiq Joty, Basma El Amel Boussaha, Maarten de Rijke

Discussion forums are an important source of information. They are often used to answer specific questions a user might have and to discover more about a topic of interest. Discussions in these forums may evolve in intricate ways, making it difficult for users to follow the flow of ideas. We propose a novel approach for automatically identifying the underlying thread structure of a forum discussion. Our approach is based on a neural model that computes coherence scores of possible reconstructions and then selects the highest scoring, i.e., the most coherent one. Preliminary experiments demonstrate promising results outperforming a number of strong baseline methods.

* Neu-IR: Workshop on Neural Information Retrieval 2017 

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How to merge three different methods for information filtering ?

Oct 26, 2015
Jean-Valère Cossu, Ludovic Bonnefoy, Xavier Bost, Marc El Bèze

Twitter is now a gold marketing tool for entities concerned with online reputation. To automatically monitor online reputation of entities , systems have to deal with ambiguous entity names, polarity detection and topic detection. We propose three approaches to tackle the first issue: monitoring Twitter in order to find relevant tweets about a given entity. Evaluated within the framework of the RepLab-2013 Filtering task, each of them has been shown competitive with state-of-the-art approaches. Mainly we investigate on how much merging strategies may impact performances on a filtering task according to the evaluation measure.

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Estimating complex causal effects from incomplete observational data

Jul 02, 2014
Juha Karvanen

Despite the major advances taken in causal modeling, causality is still an unfamiliar topic for many statisticians. In this paper, it is demonstrated from the beginning to the end how causal effects can be estimated from observational data assuming that the causal structure is known. To make the problem more challenging, the causal effects are highly nonlinear and the data are missing at random. The tools used in the estimation include causal models with design, causal calculus, multiple imputation and generalized additive models. The main message is that a trained statistician can estimate causal effects by judiciously combining existing tools.

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Practical Collapsed Stochastic Variational Inference for the HDP

Dec 02, 2013
Arnim Bleier

Recent advances have made it feasible to apply the stochastic variational paradigm to a collapsed representation of latent Dirichlet allocation (LDA). While the stochastic variational paradigm has successfully been applied to an uncollapsed representation of the hierarchical Dirichlet process (HDP), no attempts to apply this type of inference in a collapsed setting of non-parametric topic modeling have been put forward so far. In this paper we explore such a collapsed stochastic variational Bayes inference for the HDP. The proposed online algorithm is easy to implement and accounts for the inference of hyper-parameters. First experiments show a promising improvement in predictive performance.

* NIPS Workshop; Topic Models: Computation, Application, and Evaluation 

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A Brief Introduction to Temporality and Causality

Jul 14, 2010
Kamran Karimi

Causality is a non-obvious concept that is often considered to be related to temporality. In this paper we present a number of past and present approaches to the definition of temporality and causality from philosophical, physical, and computational points of view. We note that time is an important ingredient in many relationships and phenomena. The topic is then divided into the two main areas of temporal discovery, which is concerned with finding relations that are stretched over time, and causal discovery, where a claim is made as to the causal influence of certain events on others. We present a number of computational tools used for attempting to automatically discover temporal and causal relations in data.

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Representing Knowledge about Norms

Jul 18, 2006
Daniel Kayser, Farid Nouioua

Norms are essential to extend inference: inferences based on norms are far richer than those based on logical implications. In the recent decades, much effort has been devoted to reason on a domain, once its norms are represented. How to extract and express those norms has received far less attention. Extraction is difficult: as the readers are supposed to know them, the norms of a domain are seldom made explicit. For one thing, extracting norms requires a language to represent them, and this is the topic of this paper. We apply this language to represent norms in the domain of driving, and show that it is adequate to reason on the causes of accidents, as described by car-crash reports.

* The 16th European Conference on Artificial Intelligence (ECAI'04) (2004) 363-367 

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An Integrated Framework for Learning and Reasoning

Aug 01, 1995
C. G. Giraud-Carrier, T. R. Martinez

Learning and reasoning are both aspects of what is considered to be intelligence. Their studies within AI have been separated historically, learning being the topic of machine learning and neural networks, and reasoning falling under classical (or symbolic) AI. However, learning and reasoning are in many ways interdependent. This paper discusses the nature of some of these interdependencies and proposes a general framework called FLARE, that combines inductive learning using prior knowledge together with reasoning in a propositional setting. Several examples that test the framework are presented, including classical induction, many important reasoning protocols and two simple expert systems.

* Journal of Artificial Intelligence Research, Vol 3, (1995), 147-185 
* See for an online appendix and other files accompanying this article 

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Acquiring Knowledge from Encyclopedic Texts

Nov 04, 1994
Fernando Gomez, Richard Hull, Carlos Segami

A computational model for the acquisition of knowledge from encyclopedic texts is described. The model has been implemented in a program, called SNOWY, that reads unedited texts from {\em The World Book Encyclopedia}, and acquires new concepts and conceptual relations about topics dealing with the dietary habits of animals, their classifications and habitats. The program is also able to answer an ample set of questions about the knowledge that it has acquired. This paper describes the essential components of this model, namely semantic interpretation, inferences and representation, and ends with an evaluation of the performance of the program, a sample of the questions that it is able to answer, and its relation to other programs of similar nature.

* Proceedings of the Fourth ACL Conference on Applied Natural Language Processing, Stuttgart, Germany, October 13-15, 1994 
* 7 pages, 7 Postscript figures, uses aclap.sty 

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Exploratory Methods for Relation Discovery in Archival Data

Feb 23, 2022
Lucia Giagnolini, Marilena Daquino, Francesca Mambelli, Francesca Tomasi

In this article we propose a holistic approach to discover relations in art historical communities and enrich historians' biographies and archival descriptions with graph patterns relevant to art historiographic enquiry. We use exploratory data analysis to detect patterns, we select features, and we use them to evaluate classification models to predict new relations, to be recommended to archivists during the cataloguing phase. Results show that relations based on biographical information can be addressed with higher precision than relations based on research topics or institutional relations. Deterministic and a priori rules present better results than probabilistic methods.

* 25 pages, 4 figures, 7 tables, journal article 

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