Improving Graph-to-Text Generation Using Cycle Training

Natural Language Generation (NLG) from graph structured data is an important step for a number of tasks, including e.g. generating explanations, automated reporting, and conversational interfaces. Large generative language models are currently the state of the art for open ended NLG for graph data. However, these models can produce erroneous text (termed hallucinations). In this paper, we investigate the application of {\em cycle training} in order to reduce these errors. Cycle training involves alternating the generation of text from an input graph with the extraction of a knowledge graph where the model should ensure consistency between the extracted graph and the input graph. Our results show that cycle training improves performance on evaluation metrics (e.g., METEOR, DAE) that consider syntactic and semantic relations, and more in generally, that cycle training is useful to reduce erroneous output when generating text from graphs

Conversational interfaces for search as learning

Searching the web to learn new things or gain knowledge has become a common activity. Recent advances in conversational user interfaces have led to a new research opportunity - that of analyzing the potential of conversational interfaces in improving the effectiveness of search as learning (SAL). Addressing this knowledge gap, in this position paper we present conversational interfaces to support search as learning and novel methods to measure user performance and learning. Our experimental results reveal that conversational interfaces can improve user engagement, augment user long-term memorability, and alleviate user cognitive load. These findings have important implications on designing effective SAL systems.</p

Machine Learning on Linked Data, a Position Paper

The combination of linked data and machine learning is emerging as an interesting area of research. However, while both fields have seen an exponential growth in popularity in the past decade, their union has received relatively little attention. We suggest that the field is currently too complex and divergent to allow collaboration and to attract new researchers. What is needed is a simple perspective, based on unifying principles. Focusing solely on RDF, with all other semantic web technology as optional additions is an important first step. We hope that this view will provide a low-complexity outline of the field to entice new contributions, and to unify existing ones

(Semi-) Automatic Construction of Knowledge Graph Metadata

Recently a huge number of knowledge graphs (KGs) has been generated, but there has not been enough attention to generate high-quality metadata to enable users to reuse the KGs for their own purposes. The main challenge is to generate standardized and high quality descriptive metadata which helps users understand the content of the large KGs. Some existing solutions make use of a combination of schema-level patterns derived from graph summarization with instance-level snippets. I will follow this trend and develop a method based on a combination of content-based patterns with user activity data such as SPARQL query logs to make generated metadata more informative and useful than other developed approaches. The problem of current models is generating complex, long or insufficient metadata which I plan to tackle by proposing a guideline to generate standard metadata during my Ph.D

Using neural networks to aggregate Linked Data rules

Two typical problems are encountered after obtaining a set of rules from a data mining process: (i) their number can be extremely large and (ii) not all of them are interesting to be considered. Both manual and automatic strategies trying to overcome those problems have to deal with technical issues such as time costs and computational complexity. This work is an attempt to address the quantity and quality issues through using a Neural Network model for predicting the quality of Linked Data rules. Our motivation comes from our previous work, in which we obtained large sets of atomic rules through an inductive logic inspired process traversing Linked Data. Assuming a limited amount of resources, and therefore the impossibility of trying every possible combination to obtain a better rule representing a subset of items, the major issue becomes detecting the combinations that will produce the best rule in the shortest time. Therefore, we propose to use a Neural Network to learn directly from the rules how to recognise a promising aggregation. Our experiments show that including a Neural Network-based prediction model in a rule aggregation process significantly reduces the amount of resources (time and space) required to produce high-quality rules

Discovering Research Hypotheses in Social Science Using Knowledge Graph Embeddings

In an era of ever-increasing scientific publications available, scientists struggle to keep pace with the literature, interpret research results and identify new research hypotheses to falsify. This is particularly in fields such as the social sciences, where automated support for scientific discovery is still widely unavailable and unimplemented. In this work, we introduce an automated system that supports social scientists in identifying new research hypotheses. With the idea that knowledge graphs help modeling domain-specific information, and that machine learning can be used to identify the most relevant facts therein, we frame the problem of hypothesis discovery as a link prediction task, where the ComplEx model is used to predict new relationships between entities of a knowledge graph representing scientific papers and their experimental details. The final output consists in fully formulated hypotheses including the newly discovered triples (hypothesis statement), along with supporting statements from the knowledge graph (hypothesis evidence and hypothesis history). A quantitative and qualitative evaluation is carried using experts in the field. Encouraging results show that a simple combination of machine learning and knowledge graph methods can serve as a basis for automated scientific discovery