Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs

Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA. This problem is most pronounced when answers can be found only by joining evidence from multiple documents. Curated knowledge graphs (KGs) may yield good answers, but are limited by their inherent incompleteness and potential staleness. This paper presents QUEST, a method that can answer complex questions directly from textual sources on-the-fly, by computing similarity joins over partial results from different documents. Our method is completely unsupervised, avoiding training-data bottlenecks and being able to cope with rapidly evolving ad hoc topics and formulation style in user questions. QUEST builds a noisy quasi KG with node and edge weights, consisting of dynamically retrieved entity names and relational phrases. It augments this graph with types and semantic alignments, and computes the best answers by an algorithm for Group Steiner Trees. We evaluate QUEST on benchmarks of complex questions, and show that it substantially outperforms state-of-the-art baselines

COMPLIANCE TO QUALITY CRITERIA OF EXISTING REQUIREMENTS ELICITATION METHODS

In this article we define a requirements elicitation method based on natural language modelling. We argue that our method complies with synthesized quality criteria for RE methods, and compare this with the compliance of traditional RE methods (EER, ORM, UML). We show limited empirical evidence to support our theoretical argument.computer science applications;

The use of data-mining for the automatic formation of tactics

This paper discusses the usse of data-mining for the automatic formation of tactics. It was presented at the Workshop on Computer-Supported Mathematical Theory Development held at IJCAR in 2004. The aim of this project is to evaluate the applicability of data-mining techniques to the automatic formation of tactics from large corpuses of proofs. We data-mine information from large proof corpuses to find commonly occurring patterns. These patterns are then evolved into tactics using genetic programming techniques

A Model-driven Approach for Empowering Advance Web Augmentation From Client-side to Server-side Support

Websites augmentations have been adopted as a mean for improving the User Experience of applications that often are not owned by the user. The augmentations alter the page in order to add, modify and even remove its content pursuing the satisfaction of a user’s need. However, these augmentations are limited to page modification or transcluding content from another site on Internet. Moreover, advance server-side based augmentations have been released only by developers because of the required technical skill for the task. In this work, we have presented a novel approach for designing Web Augmentation coping client-side and server side using a Model-Driven Web Engineering approach. The approach rises the abstraction level for server side developments allowing end-users to design, and even implement the new functionalities. Additionally, the approach uses advance separation of concern principles thus we provide a set of tools for designing the composition of the core application and the augmentation. We show as running example an augmentation that introduces a site community’s review support upon an agriculture e-commerce site.European Union Horizon 2020 No.62149Ministerio de Ciencia e Innovación 2016-76956-C3-2-R (POLOLAS

Digital Forensics Event Graph Reconstruction

Ontological data representation and data normalization can provide a structured way to correlate digital artifacts. This can reduce the amount of data that a forensics examiner needs to process in order to understand the sequence of events that happened on the system. However, ontology processing suffers from large disk consumption and a high computational cost. This paper presents Property Graph Event Reconstruction (PGER), a novel data normalization and event correlation system that leverages a native graph database to improve the speed of queries common in ontological data. PGER reduces the processing time of event correlation grammars and maintains accuracy over a relational database storage format