We propose a simple multi-pass sieve framework that applies tiers of deterministic normalization modules one at a time from highest to lowest precision for the task of normalizing names. While a sieve based architecture has been shown effective in coreference resolution, it has not yet been applied to the normalization task. We find that even in this task, the approach retains its characteristic features of being simple, and highly modular. In addition, it also proves robust when evaluated on two different kinds of data: clinical notes and biomedical text, by demonstrating high accuracy in normalizing disorder names found in both datasets.

With the current upward trend in semantically annotated data, ontology-based data access (OBDA) was formulated to tackle the problem of data integration and query answering, where an ontology is formalized as a description logic TBox. In order to meet usability requirements set by users, efforts have been made to equip OBDA system with explanation facilities. One important explanation tool for DL ontologies, referred to as query abduction, can be formalised as abductive reasoning. In particular, given an ontology and an observation (i.e., a query with an answer), an explanation to the observation is a set of facts that together with the ontology can entail the observation. In this paper, we develop a sound and complete algorithm of query abduction for general conjunctive queries in ELH ontologies. This is achieved through ontology approximation and query rewriting. We implemented a prototypical system using the highly optimized Prolog engine XSB. We evaluated our algorithm over university benchmark ontology and our experimental results show that the system is capable of handling query abduction problems for ontology that has approximately 10 millions ABox assertions.

In many applications, training data is provided in the form of related datasets obtained from several sources, which typically affects the sample distribution. The learned classification models, which are expected to perform well on similar data coming from new sources, often suffer due to bias introduced by what we call `spurious' samples -- those due to source characteristics and not representative of any other part of the data. As standard outlier detection and robust classification usually fall short of determining groups of spurious samples, we propose a procedure which identifies the common structure across datasets by minimizing a multi-dataset divergence metric, increasing accuracy for new datasets.

We present an approach to incorporate interesting and compelling characters in planning-based narrative generation. The approach is based on a computational model that utilizes character actions to portray these as having distinct and well-defined personalities.

The NO paradigm can model many operations. Examples When agents dwell inside an organization, they form patterns are systems of river dam control, factory cells, electrical of interactions that we call paradigms. There are many power grids, and traffic control on land, sea, and space. As existing paradigms to describe organizations, which affect a paradigm, it does not functionally alter the operations to its performance features. These paradigms include hierarchies, which it is applied. The paradigm can be understood in terms holarchies, coalitions, teams, congregations, societies, of the ways it permits command and control regimes. Invariably, federations, markets and matrix organizations (Horling and NO relies on a network on which it dwells.

In recent years, there has been renewed interest in the NLP community in genuine language understanding and dialogue. Thus the long-standing issue of how the semantic content of language should be represented is reentering the communal discussion. This paper provides a brief "opinionated survey" of broad-coverage semantic representation (SR). It suggests multiple desiderata for such representations, and then outlines more than a dozen approaches to SR — some long-standing, and some more recent, providing quick characterizations, pros, cons, and some comments on implementations.

Most real-world games and many recreational games are games of incomplete information. Over the last dozen years, abstraction has emerged as a key enabler for solving large incomplete-information games. First, the game is abstracted to generate a smaller, abstract game that is strategically similar to the original game. Second, an approximate equilibrium is computed in the abstract game. Third, the strategy from the abstract game is mapped back to the original game. In this paper, I will review key developments in the field. I present reasons for abstracting games, and point out the issue of abstraction pathology. I then review the practical algorithms for information abstraction and action abstraction. I then cover recent theoretical breakthroughs that beget bounds on the quality of the strategy from the abstract game, when measured in the original game. I then discuss how to reverse map the opponent's action into the abstraction if the opponent makes a move that is not in the abstraction. Finally, I discuss other topics of current and future research.

Cyber-Physical Production Systems (CPPSs) are in the focus of research, industry and politics: By applying new IT and new computer science solutions, production systems will become more adaptable, more resource ef- ficient and more user friendly. The analysis and diagnosis of such systems is a major part of this trend: Plants should detect automatically wear, faults and suboptimal configurations. This paper reflects the current state-of- the-art in diagnosis against the requirements of CPPSs, identifies three main gaps and gives application scenarios to outline first ideas for potential solutions to close these gaps.

This paper is concerned with knowledge compilation (KC), a family of approaches developed in AI for more than twenty years. Knowledge compilation consists in pre-processing some pieces of the available information in order to improve the computational efficiency (especially, the time complexity) of some tasks. In this paper, the focus is laid on three KC topics which gave rise to many works: the development of knowledge compilation techniques for the clausal entailment problem in propositional logic, the concept of compilability and the notion of knowledge compilation map. The three topics, as well as an overview of the main results from the literature, are presented. Some recent research lines are also discussed.

This paper summarizes an ongoing multi-year project aiming to uncover knowledge and techniques for devising intelligent environments for user-adaptive visualizations. We ran three studies designed to investigate the impact of user and task characteristics on user performance and satisfaction in different visualization contexts. Eye-tracking data collected in each study was analyzed to uncover possible interactions between user/task characteristics and gaze behavior during visualization processing. Finally, we investigated user models that can assess user characteristics relevant for adaptation from eye tracking data.