Query answering over Description Logic (DL) ontologies has become a vibrant field of research. Efficient realizations often exploit database technology and rewrite a given query to an equivalent SQL or Datalog query over a database associated with the ontology. This approach has been intensively studied for conjunctive query answering in the DL-Lite and EL families, but is much less explored for more expressive DLs and queries. We present a rewriting-based algorithm for conjunctive query answering over Horn-SHIQ ontologies, possibly extended with recursive rules under limited recursion as in DL+log. This setting not only subsumes both DL-Lite and EL, but also yields an algorithm for answering (limited) recursive queries over Horn-SHIQ ontologies (an undecidable problem for full recursive queries). A prototype implementation shows its potential for applications, as experiments exhibit efficient query answering over full Horn-SHIQ ontologies and benign downscaling to DL-Lite, where it is competitive with comparable state of the art systems.

Consistent query answering is a standard approach for producing meaningful query answers when data is inconsistent. Recent work on consistent query answering in the presence of ontologies has shown this problem to be intractable in data complexity even for ontologies expressed in lightweight description logics. In order to better understand the source of this intractability, we investigate the complexity of consistent query answering for simple ontologies consisting only of class subsumption and class disjointness axioms. We show that for conjunctive queries with at most one quantified variable, the problem is first-order expressible; for queries with at most two quantified variables, the problem has polynomial data complexity but may not be first-order expressible; and for three quantified variables, the problem may become co-NP-hard in data complexity. For queries having at most two quantified variables, we further identify a necessary and sufficient condition for first-order expressibility. In order to be able to handle arbitrary conjunctive queries, we propose a novel inconsistency-tolerant semantics and show that under this semantics, first-order expressibility is always guaranteed. We conclude by extending our positive results to DL-Lite ontologies without inverse.

Open query answering is the idea of answering queries that are not given using the vocabulary of the queried knowledge base but instead the vocabulary of the inquirer. Many aspects of open query answering can be tackled through the combination of human effort with algorithmic techniques. In this paper we explore its applicability to crowdsourcing, using a framework in which human and computational intelligence can co-exist by augmenting existing Linked Data and Linked Service technology with crowdsourcing functionality. We analyze how the task can be decomposed and translated into Mechanical Turk projects in order to achieve this vision.

Community Question Answering (CQA) services thrive as a result of a small number of highly active users, typically called experts, who provide a large number of high quality useful answers. Understanding the temporal dynamics and interactions between experts can present key insights into how community members evolve over time. In this paper, we present a temporal study of experts in CQA and analyze the changes in their behavioral patterns over time. Further, using unsupervised machine learning methods, we show the interesting evolution patterns that can help us distinguish experts from one another. Using supervised classification methods, we show that the models based on evolutionary data of users can be more effective at expert identification than the models that ignore evolution. We run our experiments on two large online CQA to show the generality of our proposed approach.

Current techniques for query answering over DL-Lite ontologies have severe limitations in practice, since they either produce complex queries that are inefficient during execution, or require expensive data pre-processing. In light of this, we present two complementary sets of results that aim at improving the overall peformance of query answering systems. We show how to create ABox repositories that are complete w.r.t. a significant portion of DL-Lite TBoxes, but where the data is not explicitly expanded. Second, we show how to characterize ABox completeness by means of dependencies, and how to use these and equivalence to optimize DL-Lite TBoxes. These results allow us to reduce the cost of query rewriting, often dramatically, and to generate highly efficient queries. We have implemented a novel system for query answering over DL-Lite ontologies that incorporates these techniques, and we present a series of data-intensive evaluations that show their effectiveness.

In order to meet usability requirements, most logic-based applications provide explanation facilities for reasoning services. This holds also for DLs, where research has focused on the explanation of both TBox reasoning and, more recently, query answering. Besides explaining the presence of a tuple in a query answer, it is important to explain also why a given tuple is missing. We address the latter problem for (conjunctive) query answering over DL-Lite ontologies, by adopting abductive reasoning, that is, we look for additions to the ABox that force a given tuple to be in the result. As reasoning taskswe consider existence and recognition of an explanation, and relevance and necessity of a certain assertion for an explanation. We characterize the computational complexity of these problems for subset minimal and cardinality minimal explanations.

The current study investigates the relationship between children’s curiosity and question asking ability. Generation of two types of questions was assessed: identification (yes/no questions asked to identify a target from an array) and understanding questions, asked to learn more about a topic. The latter was related to children’s curiosity, as was the ability to recognize the effectiveness of questions in solving a mystery. Training on asking identification questions was effective in improving children’s ability to ask that type of question, but did not transfer to the other task. Training on asking understanding questions was not successful. Children’s curiosity did not influence the effectiveness of the training.

This paper shows that automatically generated questions can help classify children’s spoken responses to a reading tutor teaching them to generate their own questions. We use automatic question generation to model and classify children’s prompted spoken questions about stories. On distinguishing complete and incomplete questions from irrelevant speech and silence, a language model built from automatically generated questions out-performs a trigram language model that does not exploit the structure of questions.

Recent studies on question generation identify the need for automatic discourse analysers. We evaluated the feasibility of integrating an available discourse analyser called HILDA for a specific question generation system called CODA; introduce an approach by extracting a discourse corpus from the CODA parallel corpus; and identified future work towards automatic discourse analysis in the domain of question generation.

Automated question generation has been explored for a broad range of tasks. However, an important task for which limited work on question generation has been undertaken is writing support. Writing support systems, particularly for novice writers who are acquiring the fundamentals of writing, can scaffold the complex processes that bear on writing. Novice writers face significant challenges in creative writing. Their stories often lack the expressive prose that characterizes texts produced by their expert writer counterparts. A story that is composed by a novice writer may also lack a compelling plot, may not effectively utilize a story’s setting, characters, and props, and may describe events that play out in an unpredictable or confusing order. We propose an automatic question generation framework that is designed to stimulate the cognitive processes associated with creative writing. The framework utilizes semantic role labeling and discourse parsing applied to the initial drafts of the writer’s passage to generate questions to promote creativity.