This special issue issue of AI Magazine presents six articles on some of the most interesting question answering systems in development today. Included are articles on Project, the Semantic Research, Watson, True Knowledge, and TextRunner (University of Washington's clever use of statistical NL techniques to answer questions across the open web).

This special issue issue of AI Magazine presents six articles on some of the most interesting question answering systems in development today. Included are articles on Project, the Semantic Research, Watson, True Knowledge, and TextRunner (University of Washington’s clever use of statistical NL techniques to answer questions across the open web).

The motivation for the project was to tackle what might be regarded as the "holy grail" of Internet search, replacing larger and larger numbers of keyword-based lists of links with perfect, direct answers to naturally phrased queries on any subject. The platform was also designed to scale, with the primary mechanism for answering more and more questions being the addition of knowledge to the platform rather than writing more program code. Additional knowledge areas are typically included by adding "knowledge about knowledge." The system is live and answers millions of questions per month, asked by real Internet users. Questions can be tried at (and API access obtained from) www.trueknowledge.com. All the intellectual External computer systems can connect to the property was subsequently transferred in 2006 platform at two points through an API.

IBM Research undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV Quiz show, Jeopardy! The extent of the challenge includes fielding a real-time automatic contestant on the show, not merely a laboratory exercise. The Jeopardy! Challenge helped us address requirements that led to the design of the DeepQA architecture and the implementation of Watson. After 3 years of intense research and development by a core team of about 20 researches, Watson is performing at human expert-levels in terms of precision, confidence and speed at the Jeopardy! Quiz show. Our results strongly suggest that DeepQA is an effective and extensible architecture that may be used as a foundation for combining, deploying, evaluating and advancing a wide range of algorithmic techniques to rapidly advance the field of QA.

In the winter, 2004 issue of AI Magazine, we reported Vulcan Inc.'s first step toward creating a question-answering system called "Digital Aristotle." The goal of that first step was to assess the state of the art in applied Knowledge Representation and Reasoning (KRR) by asking AI experts to represent 70 pages from the advanced placement (AP) chemistry syllabus and to deliver knowledge-based systems capable of answering questions from that syllabus. This paper reports the next step toward realizing a Digital Aristotle: we present the design and evaluation results for a system called AURA, which enables domain experts in physics, chemistry, and biology to author a knowledge base and that then allows a different set of users to ask novel questions against that knowledge base. These results represent a substantial advance over what we reported in 2004, both in the breadth of covered subjects and in the provision of sophisticated technologies in knowledge representation and reasoning, natural language processing, and question answering to domain experts and novice users.

Conjunctive query answering is a key reasoning service for many ontology-based applications. In order to improve scalability, many Semantic Web query answering systems give up completeness (i.e., they do not guarantee to return all query answers). It may be useful or even critical to the designers and users of such systems to understand how much and what kind of information is (potentially) being lost. We present a method for generating test data that can be used to provide at least partial answers to these questions, a purpose for which existing benchmarks are not well suited. In addition to developing a general framework that formalises the problem, we describe practical data generation algorithms for some popular ontology languages, and present some very encouraging results from our preliminary evaluation.

We propose a new method of recommending questions to answerers so as to suit the answerers’ knowledge and interests in User-Interactive Question Answering (QA) communities. A question recommender can help answerers select the questions that interest them. This increases the number of answers, which will activate QA communities. An effective question recommender should satisfy the following three requirements: First, its accuracy should be higher than the existing category-based approach; more than 50% of answerers select the questions to answer according a fixed system of categories. Second, it should be able to recommend unanswered questions because more than 2,000 questions are posted every day. Third, it should be able to support even those people who have never answered a question previously, because more than 50% of users in current QA communities have never given any answer. To achieve an effective question recommender, we use question histories as well as the answer histories of each user by combining collaborative filtering schemes and content-base filtering schemes. Experiments on real log data sets of a famous Japanese QA community, Oshiete goo, show that our recommender satisfies the three requirements.

The DL-Lite family of Description Logics has been designed with the specific goal of allowing for answering complex queries (in particular, conjunctive queries) over ontologies with very large instance sets (ABoxes). So far, in DL-Lite systems, this goal has been actually achieved only for relatively simple (short) conjunctive queries. In this paper we present Presto, a new query answering technique for DL-Lite ontologies, and an experimental comparison of Presto with the main previous approaches to query answering in DL-Lite. In practice, our experiments show that, in real ontologies, current techniques are only able to answer conjunctive queries of less than 7-10 atoms (depending on the complexity of the TBox), while Presto is actually able to handle conjunctive queries of up to 30 atoms. Furthermore, in the cases that are already successfully handled by previous approaches, Presto is significantly more efficient.

In this paper, we propose causality as a unified framework to explain query answers and non-answers, thus generalizing and extending several previously proposed approaches of provenance and missing query result explanations. We develop our framework starting from the well-studied definition of actual causes by Halpern and Pearl [13]. After identifying some undesirable characteristics of the original definition, we propose functional causes as a refined definition of causality with several desirable properties. These properties allow us to apply our notion of causality in a database context and apply it uniformly to define the causes of query results and their individual contributions in several ways: (i) we can model both provenance as well as nonanswers, (ii) we can define explanations as either data in the input relations or relational operations in a query plan, and (iii) we can give graded degrees of responsibility to individual causes, thus allowing us to rank causes. In particular, our approach allows us to explain contributions to relational aggregate functions and to rank causes according to their respective responsibilities. We give complexity results and describe polynomial algorithms for evaluating causality in tractable cases. Throughout the paper, we illustrate the applicability of our framework with several examples. Overall, we develop in this paper the theoretical foundations of causality theory in a database context.

Dialogue-based Question Answering (QA) is a highly complex task that brings together a QA system including various natural language processing components (i.e., components for question classification, information extraction, and retrieval) with dialogue systems for effective and natural communication. The dialogue-based access is difficult to establish when the QA system in use is complex and combines many different answer services with different quality and access characteristics. For example, some questions are processed by opendomain QA services with a broad coverage. Others should be processed by using a domain-specific instance ontology for more reliable answers. Different answer services may change their characteristics over time and the dialogue reaction models have to be updated according to that. To solve this problem, we developed introspective methods to integrate adaptable models of the answer services. We evaluated the impact of the learned models on the dialogue performance, i.e., whether the adaptable models can be used for a more convenient dialogue formulation process. We show significant effectiveness improvements in the resulting dialogues when using the machine learning (ML) models. Examples are provided in the context of the generation of system-initiative feedback to user questions and answers, as provided by heterogeneous information services.