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The second contribution of this paper is an analysis of the proposed representations categories with respect to a novel image mining application, the collection of individual household census data from satellite imagery, more specifically Google earth satellite imagery. The representations are considered both in terms of generating census prediction models and in terms of applying such models for larger scale census prediction. The research presented in this paper basis on the premise that segmenting textual content into successive situations according to four components - space, time, actors and motion — can help depicting a storyline in a way that facilitates comparative analyses across texts, and ultimately fostering knowledge discovery.

The paper presents the original aim of the project and sums up the knowledge modelling choices made in order to formalise the segmentation procedure through which sequences of situations are extracted. We then present several proof of concept visualisations that facilitate visual reasoning on the structure, rhythm, patterns and variations of heterogeneous texts, and summarise how the space, time, actors and motion components are organised inside a given narrative. The approach was tested across various types of text, in three languages, and the paper details some of the potential benefits of the resulting visualisations on the specific case of R.

Similarity plays a central role in language understanding process. However, it is always difficult to precisely define on which type of data and what similarity metrics we can apply in order to assess the similarity of two texts.

22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), San Francisco 2016

Previously, we proposed a four-layer system [ 69 ] that takes into account not only string and semantic word similarities, but also word alignment and sentence structure. Our system achieved new state of the art or competitive result to state of the art on different test corpora for the Semantic Textual Similarity STS task from to The multi-layer architecture helps to deal with heterogeneous corpora which may not have been generated by the same distribution nor same domain. In this extended paper, we looked into the correlation between the two semantic processing tasks Semantic Relatedness a more broad task of STS and Recognizing Textual Entailment RTE to construct a co-learning model where we integrated our multi-layer architecture and Corpus Patterns technique to ultimately improve the performances of both tasks.

Spreadsheets compose a notably large and valuable dataset of documents within the enterprise settings and on the Web. Although spreadsheets are intuitive to use and equipped with powerful functionalities, extracting and reusing data from them remains a cumbersome and mostly manual task.

Their greatest strength, the large degree of freedom they provide to the user, is at the same time also their greatest weakness, since data can be arbitrarily structured. Therefore, in this paper we propose a supervised learning approach for layout recognition in spreadsheets. We work on the cell level, aiming at predicting their correct layout role, out of five predefined alternatives. For this task we have considered a large number of features not covered before by related work.

Moreover, we gather a considerably large dataset of annotated cells, from spreadsheets exhibiting variability in format and content. Our experiments, with five different classification algorithms, show that we can predict cell layout roles with high accuracy. Subsequently, in this paper we focus on revising the classification results, with the aim of repairing misclassifications.

We propose a sophisticated approach, composed of three steps, which effectively corrects a reasonable number of inaccurate predictions. We present and validate a method and underlying set of technologies, data structures and algorithms to calculate, categorize and visualize component dependencies, data lineage and business semantics from the database structures and queries, independently of actual data in the data warehouse.

Chosen approach based on semantic techniques, probabilistic weight calculation and estimation of the impact of data in queries and implemented rule system supports the calculation of the dependency graph from these estimates. We demonstrate a method for business semantics integration and ontology learning from data structures and schemas with a combination of query semantics captured by dependency graph.

Annotation of technical assets using a business ontology provides meaning and governance view for human and machine agents to address various planning, automation and decision support problems. Data processing performance and business ontology integration is evaluated and analyzed over several real-life datasets. Many authoritative studies report how in these last years the consumer credit was up year on year, making it necessary to develop instruments able to assist the financial operators in some crucial tasks. The most important of them is to classify the loan applications as reliable or unreliable, on the basis of the customer information at their disposal.

Such instruments of credit scoring allow the operators to reduce the financial losses, and for this reason they play a very important role. However, the design of effective credit scoring models is not an easy task, since it must face some problems, first among them the data imbalance in the model training.

This problem arises because the number of default cases is usually much smaller than that of the non-default ones and this kind of distribution worsens the effectiveness of the state-of-the-art approaches used to define these models. This paper proposes a novel Linear Dependence Based LDB approach able to build a credit scoring model by using only the past non-default cases, overcoming both the imbalanced class distribution and the cold-start issues.

Knowledge Discovery from Sensor Data - CRC Press Book

It relies on the concept of linear dependence between the vector representations of the past and new loan applications, evaluating it in the context of a matrix. The experiments, performed by using two real-world datasets with a strong unbalanced distribution of data, show that the proposed approach achieves performance closer or better than that of one of the best state-of-the-art approaches of credit scoring such as random forests, even using only past non-default cases.

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This work presents a novel approach for automatically generating a sentiment lexicon. We employ an unsupervised learning approach using several probabilistic and information theoretic models. While most of the unsupervised approaches require a set of seed words to begin their work, our methods differ from these by using no a priori knowledge.

In addition, our models are effective with a diverse corpus rather than requiring a corpus for a limited domain. We demonstrate the effectiveness of our approaches by performing sentiment analysis on Amazon products reviews, comparing the various automatically-generated lexicons. Based on our cross validation results, we show that our lexicons outperform a widely-used sentiment lexicon on both balanced and unbalanced datasets. The field of quality control has seen over the last decades a variety of studies and innovations turned towards the improvement of the perceptions rendered through manufactured products.

Thus, quality checks do not only rely on technical control, but on a diversity of controls which correspond to the senses involved when interacting with a product. However, the quality specifications and in particular the vocabulary used for their description are still very specific to each product or industrial domain. With the perspective of simplifying and standardizing perceived quality control, this study aims at providing a Smart System based on knowledge modelling methods which is capable of guiding manufacturers in the process of structuring, generalizing and eventually automatizing the control process related to perceived quality and touch in particular.

This paper presents a general framework for the Smart System as well as an ontological structure for the representation of perceived quality knowledge. The specificities of the sense of touch are detailed and led to the proposition of novel formalized description and conceptual model of haptic perceptions. Mereology, the formal theory of parts and wholes, has a played a prominent role within applied ontology.

As a fundamental set of concepts for commonsense reasoning, it also appears in a number of upper level ontologies. Furthermore, such upper-level ontologies provide an account of the most basic, domain-independent, existing entities, such as time, space, objects, and processes. We show that the existing axiomatization of SUMO omits some of the intended models of classical mereology, and we propose the correction and addition of axioms to address this issue.

In addition, we show the formal relationship between the axiomatization of mereology in both upper-level ontologies. The development of domain-specific ontologies requires joint efforts among different groups of stakeholders, such as knowledge engineers and domain experts. During the development processes, ontology changes need to be tracked and propagated across developers.

Version Control Systems VCSs collect metadata describing changes and allow for the synchronization of different versions of the same ontology. Commonly, VCSs follow optimistic approaches to enable the concurrent modification of ontology artifacts, as well as conflict detection and resolution. For conflict detection, VCSs usually apply techniques where files are compared line by line. However, ontology changes can be serialized in different ways during the development process.

As a consequence, existing VCSs may detect a large number of false-positive conflicts, i. We developed SerVCS in order to enhance VCSs to cope with different serializations of the same ontology, following the principle of prevention is better than cure. SerVCS resorts on unique ontology serializations and minimizes the number of false-positive conflicts.

September 24-25, 2020 in Istanbul, Turkey

All submitted conference papers will be blind peer reviewed by three competent reviewers. Impact Factor Indicators. A number of selected high-impact full text papers will also be considered for the special journal issues. All submitted papers will have the opportunity to be considered for this Special Journal Issue. The paper selection will be carried out during the peer review process as well as at the conference presentation stage.

KDD 2016 - San Francisco

Submitted papers must not be under consideration by any other journal or publication. The final decision for paper selection will be made based on peer review reports by the Guest Editors and the Editor-in-Chief jointly. Selected full-text papers will be published online free of charge. The Conference offers the opportunity to become a conference sponsor or exhibitor.

To participate as a sponsor or exhibitor, please download and complete the Conference Sponsorship Request Form. All conference materials and services will be delivered digitally to the participant with the online conference management system. Conference registration includes the following digital materials and services:.

Online payment option available for author and listener delegates. Conference participants can make online credit card payments for conference registration fees. Gaspar, Maria P. Aghili, S. Tabarestani, C. Freytes, M. Shojaie, M. Cabrerizo, A. Barreto, N.

Rishe, R. Curiel, D.