Preliminary results on Ontology-based Open Data Publishing

Despite the current interest in Open Data publishing, a formal and comprehensive methodology supporting an organization in deciding which data to publish and carrying out precise procedures for publishing high-quality data, is still missing. In this paper we argue that the Ontology-based Data Management paradigm can provide a formal basis for a principled approach to publish high quality, semantically annotated Open Data. We describe two main approaches to using an ontology for this endeavor, and then we present some technical results on one of the approaches, called bottom-up, where the specification of the data to be published is given in terms of the sources, and specific techniques allow deriving suitable annotations for interpreting the published data under the light of the ontology

Knowledge-preserving Certain Answers for SQL-like Queries

International audienceAnswering queries over incomplete data is based on finding answers that are certainly true, independently of how missing values are interpreted. This informal description has given rise to several different mathematical definitions of certainty. To unify them, a framework based on "explanations", or extra information about incomplete data, was recently proposed. It partly succeeded in justifying query answering methods for relational databases under set semantics, but had two major limitations. First, it was firmly tied to the set data model, and a fixed way of comparing incomplete databases with respect to their information content. These assumptions fail for reallife database queries in languages such as SQL that use bag semantics instead. Second, it was restricted to queries that only manipulate data, while in practice most analytical SQL queries invent new values, typically via arithmetic operations and aggregation. To leverage our understanding of the notion of certainty for queries in SQL-like languages, we consider incomplete databases whose information content may be enriched by additional knowledge. The knowledge order among them is derived from their semantics, rather than being fixed a priori. The resulting framework allows us to capture and justify existing notions of certainty, and extend these concepts to other data models and query languages. As natural applications, we provide for the first time a well-founded definition of certain answers for the relational bag data model and for valueinventing queries on incomplete databases, addressing the key shortcomings of previous approaches

Students’ Perspectives on Concepts, Factors, and Models Related to the Attainment of Achievement

This study aimed to explore concepts, factors affecting, and achievement models, from the perspective of tertiary students in Yogyakarta. Respondents (N = 533) were students of a private university in Yogyakarta. Data was collected through an open-ended questionnaire for all respondents, and in-depth interviews with 23 of these. Data were analyzed using content analysis techniques for responses to the answers provided. The results show that the concept of achievement, according to the perspective of the students, is differentiated between into definitions of achievement, and the criteria of what may to be considered to be achievements. The definition of achievement, according to the students, is something which is unique to this finding, with the emergence of non-academic achievement and excellence in competition with others. The criteria discovered, for a person to be considered to be an achiever, include compliance with goals (both personal and social) and the presence of the element of development. The presence of the suitability of social goals, is another unique thing found in this study. Factors which influence achievement include ‘input’ (personal capacities), and ‘process’ (the learning process)’. The external conditions which emerged in this finding took the form of other unique matters, found in the local culture. The dynamics of reaching achievement begin with ‘input’ (cognitive capacity, personal skills, motivation), and external conditions (which give rise to academic learning behaviors with the support of self-efficacy), for future learning achievements which are more optimal, with the attainment of personal and social goals

Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension

This study considers the task of machine reading at scale (MRS) wherein, given a question, a system first performs the information retrieval (IR) task of finding relevant passages in a knowledge source and then carries out the reading comprehension (RC) task of extracting an answer span from the passages. Previous MRS studies, in which the IR component was trained without considering answer spans, struggled to accurately find a small number of relevant passages from a large set of passages. In this paper, we propose a simple and effective approach that incorporates the IR and RC tasks by using supervised multi-task learning in order that the IR component can be trained by considering answer spans. Experimental results on the standard benchmark, answering SQuAD questions using the full Wikipedia as the knowledge source, showed that our model achieved state-of-the-art performance. Moreover, we thoroughly evaluated the individual contributions of our model components with our new Japanese dataset and SQuAD. The results showed significant improvements in the IR task and provided a new perspective on IR for RC: it is effective to teach which part of the passage answers the question rather than to give only a relevance score to the whole passage.Comment: 10 pages, 6 figure. Accepted as a full paper at CIKM 201