The class of shuffle ideals is a fundamental sub-family of regular languages. The shuffle ideal generated by a string set $U$ is the collection of all strings containing some string $u \in U$ as a (not necessarily contiguous) subsequence. In spite of its apparent simplicity, the problem of learning a shuffle ideal from given data is known to be computationally intractable. In this paper, we study the PAC learnability of shuffle ideals and present positive results on this learning problem under element-wise independent and identical distributions and Markovian distributions in the statistical query model. A constrained generalization to learning shuffle ideals under product distributions is also provided. In the empirical direction, we propose a heuristic algorithm for learning shuffle ideals from given labeled strings under general unrestricted distributions. Experiments demonstrate the advantage for both efficiency and accuracy of our algorithm.

We propose a method for automatically answering questions about images by bringing together recent advances from natural language processing and computer vision. We combine discrete reasoning with uncertain predictions by a multi-world approach that represents uncertainty about the perceived world in a bayesian framework. Our approach can handle human questions of high complexity about realistic scenes and replies with range of answer like counts, object classes, instances and lists of them. The system is directly trained from question-answer pairs. We establish a first benchmark for this task that can be seen as a modern attempt at a visual turing test.

The emergence of online crowdsourcing sites, online work platforms, and evenMassive Open Online Courses (MOOCs), has created an increasing need for reliably evaluating the skills of the participating users in a scalable way.Many platforms already allow users to take online tests and verify their skills, but the existing approaches face many problems. First of all, cheating is very common in online testing without supervision, as the test questions often "leak" and become easily available online together with the answers.Second, technical skills, such as programming, require the tests to be frequently updated in order to reflect the current state-of-the-art. Third,there is very limited evaluation of the tests themselves, and how effectively they measure the skill that the users are tested for. In this paper, we present a Scalable Testing and Evaluation Platform (STEP),that allows continuous generation and evaluation of test questions. STEP leverages already available content, on Question Answering sites such as StackOverflow and re-purposes these questions to generate tests. The system utilizes a crowdsourcing component for the editing of the questions, while it uses automated techniques for identifying promising QA threads that can be successfully re-purposed for testing. This continuous question generation decreases the impact of cheating and also creates questions that are closer to the real problems that the skill holder is expected to solve in real life.STEP also leverages the use of Item Response Theory to evaluate the quality of the questions. We also use external signals about the quality of the workers.These identify the questions that have the strongest predictive ability in distinguishing workers that have the potential to succeed in the online job marketplaces. Existing approaches contrast in using only internal consistency metrics to evaluate the questions. Finally, our system employs an automatic "leakage detector" that queries the Internet to identify leaked versions of our questions. We then mark these questions as "practice only," effectively removing them from the pool of questions used for evaluation. Our experimental evaluation shows that our system generates questions of comparable or higher quality compared to existing tests, with a cost of approximately 3-5 dollars per question, which is lower than the cost of licensing questions from existing test banks.

We leverage crowd wisdom for multiple-choice question answering, and employ lightweight machine learning techniques to improve the aggregation accuracy of crowdsourced answers to these questions. In order to develop more effective aggregation methods and evaluate them empirically, we developed and deployed a crowdsourced system for playing the "Who wants to be a millionaire?" quiz show.Analyzing our data (which consist of more than 200,000 answers), we find that by just going with the most selected answer in the aggregation, we can answer over 90% of the questions correctly, but the success rate of this technique plunges to 60% for the later/harder questions in the quiz show. To improve the success rates of these later/harder questions, we investigate novel weighted aggregation schemes for aggregating the answers obtained from the crowd.By using weights optimized for reliability of participants (derived from the participants' confidence), we show that we can pull up the accuracy rate for the harder questions by 15%, and to overall 95% average accuracy.Our results provide a good case for the benefits of applying machine learning techniques for building more accurate crowdsourced question answering systems.

This paper describes USI Answers -- a natural language question answering system for enterprise data. We report on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data sources. Additional complications come from the nature of the data, which comes both as structured and unstructured. The proposed solution allows users to express questions in natural language, makes apparent the system's interpretation of the query, and allows easy query adjustment and reformulation.

This paper describes USI Answers — a natural language question answering system for enterprise data. We report on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data sources. Additional complications come from the nature of the data, which comes both as structured and unstructured. The proposed solution allows users to express questions in natural language, makes apparent the system's interpretation of the query, and allows easy query adjustment and reformulation. The application is in use by more than 1500 users from Siemens Energy. We evaluate our approach on a data set consisting of fleet data.

Inquire Biology is a prototype of a new kind of intelligent textbook — one that answers students’ questions, engages their interest, and improves their understanding. Inquire Biology provides unique capabilities via a knowledge representation that captures conceptual knowledge from the textbook and uses inference procedures to answer students’ questions. Students ask questions by typing free-form natural language queries or by selecting passages of text. The system then attempts to answer the question and also generates suggested questions related to the query or selection. The questions supported by the system were chosen to be educationally useful, for example: what is the structure of X? compare X and Y? how does X relate to Y? In user studies, students found this question-answering capability to be extremely useful while reading and while doing problem solving. In an initial controlled experiment, community college students using the Inquire Biology prototype outperformed students using either a hardcopy or conventional E-book version of the same biology textbook. While additional research is needed to fully develop Inquire Biology, the initial prototype clearly demonstrates the promise of applying knowledge representation and question-answering technology to electronic textbooks.

We study the complexity of conjunctive query answering under (weakly-)(frontier-)guarded disjunctive existential rules, i.e., existential rules extended with disjunction, and their main subclasses, linear rules and inclusion dependencies (IDs). Our main result states that conjunctive query answering under a fixed set of disjunctive IDs is 2EXPTIME-hard. This quite surprising result together with a 2EXPTIME upper bound for weakly-frontier-guarded disjunctive rules, obtained by exploiting recent results on guarded negation first-order logic, gives us a complete picture of the computational complexity of our problem. We also consider a natural subclass of disjunctive IDs, namely frontier-one (only one variable is propagated), for which the combined complexity decreases to EXPTIME. Finally, we show that frontier-guarded rules, combined with negative constraints, are strictly more expressive than DL-Lite H bool , one of the most expressive languages of the DL-Lite family. We also show that query answering under this DL is 2EXPTIME-complete in combined complexity.

So-called combined approaches answer a conjunctive query over a description logic ontology in three steps: first, they materialise certain consequences of the ontology and the data; second, they evaluate the query over the data; and third, they filter the result of the second phase to eliminate unsound answers. Such approaches were developed for various members of the DL-Lite and the EL families of languages, but none of them can handle ontologies containing nominals. In our work, we bridge this gap and present a combined query answering approach for ELHO--a logic that contains all features of the OWL 2 EL standard apart from transitive roles and complex role inclusions. This extension is nontrivial because nominals require equality reasoning, which introduces complexity into the first and the third step. Our empirical evaluation suggests that our technique is suitable for practical application, and so it provides a practical basis for conjunctive query answering in a large fragment of OWL 2 EL.

The paper reports on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data sources. Additional complications come from the nature of the data, which comes both as structured and unstructured. The proposed solution allows users to express questions in natural language, makes apparent the system's interpretation of the query, and allows easy query adjustment and reformulation. The application is in use by more than 1500 users from Siemens Energy. We evaluate our approach on a data set consisting of fleet data.