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19th International Conference on Human-Computer Interaction (HCI International)

作者： Martono, Niken Prasasti Abe, Keisuke Yamaguchi, Takehiko Ohwada, Hayato Giovannetti, Tania Tokyo Univ Sci Dept Ind Adm Noda Chiba Japan Tokyo Univ Sci Dept Appl Elect Tokyo Japan Temple Univ Dept Psychol Philadelphia PA 19122 USA

ISBN: (纸本)9783319585246;9783319585239

Developments in virtual reality (VR) have advanced numerous applications in clinical settings in the areas of learning and treatment in neuropsychology. Emerging VR applications today focus on the challenge of diagnosis and cognitive training of mild cognitive impairment (MCI) and dementia patients and address navigation and orientation, face recognition, cognitive functionality, and other instrumental activities of daily living (IADL). The information recorded and captured by VR-based technology is real-time and can be advantageous for further analysis of patients' characteristics. The present study sought to utilize the data collected from VR-based software and a leap-motion device for learning in MCI cases to generate the rules for errors and action slips based on finger-action transitions when performing IADL. The finger motion was recorded as a time-series database, then an induction technique called inductive logic programming (ILP), which uses logical and clausal language to represent the training data, was used to discover a concise classification rule using logical programming.

关键词： Virtual reality inductive logic programming Micro slips Micro errors Mild cognitive impairment

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inductive logic programming AS ABDUCTIVE SEARCH

INDUCTIVE LOGIC PROGRAMMING AS ABDUCTIVE SEARCH

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26th International Conference on logic programming (ICLP)

作者： Corapi, Domenico Russo, Alessandra Lupu, Emil Imperial Coll London Dept Comp 180 Queens Gate London SW7 2AZ England

ISBN: (纸本)9783939897170

We present a novel approach to non-monotonic ILP and its implementation called TAL (Top-directed Abductive Learning). TAL overcomes some of the completeness problems of ILP systems based on Inverse Entailment and is the first top-down ILP system that allows background theories and hypotheses to be normal logic programs. The approach relies on mapping an ILP problem into an equivalent ALP one. This enables the use of established ALP proof procedures and the specification of richer language bias with integrity constraints. The mapping provides a principled search space for an ILP problem, over which an abductive search is used to compute inductive solutions.

关键词： inductive logic programming Abductive logic programming Non-monotonic Reasoning

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Microarray data classification using inductive logic programming and gene ontology background information

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JOURNAL OF CHEMOMETRICS 2010年第5-6期24卷 231-240页

作者： Ryeng, Einar Alsberg, Bjorn Kare Norwegian Univ Sci & Technol NTNU Dept Chem N-7491 Trondheim Norway

There exists many databases containing information on genes that are useful for background information in machine learning analysis of microarray data. The gene ontology and gene ontology annotation projects are among the most comprehensive of these. We demonstrate how inductive logic programming (ILP) can be used to build classification rules for microarray data which naturally incorporates the gene ontology and annotations to it as background knowledge without removing the inherent graph structure of the ontology. The ILP rules generated are parsimonious and easy to interpret. Copyright (C) 2010 John Wiley & Sons, Ltd.

关键词： bioinformatics gene ontology inductive logic programming microarray

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Statistical relational learning for workflow mining

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INTELLIGENT DATA ANALYSIS 2016年第3期20卷 515-541页

作者： Bellodi, Elena Riguzzi, Fabrizio Lamma, Evelina Univ Ferrara Dipartimento Ingn Via Saragat I-44122 Ferrara Italy Univ Ferrara Dipartimento Matemat & Informat Via Saragat I-44122 Ferrara Italy

The management of business processes can support efficiency improvements in organizations. One of the most interesting problems is the mining and representation of process models in a declarative language. Various recently proposed knowledge-based languages showed advantages over graph-based procedural notations. Moreover, rapid changes of the environment require organizations to check how compliant are new process instances with the deployed models. We present a Statistical Relational Learning approach to Workflow Mining that takes into account both flexibility and uncertainty in real environments. It performs automatic discovery of process models expressed in a probabilistic logic. It uses the existing DPML algorithm for extracting first-order logic constraints from process logs. The constraints are then translated into Markov logic to learn their weights. Inference on the resulting Markov logic model allows a probabilistic classification of test traces, by assigning them the probability of being compliant to the model. We applied this approach to three datasets and compared it with DPML alone, five Petri net- and EPC-based process mining algorithms and Tilde. The technique is able to better classify new execution traces, showing higher accuracy and areas under the PR/ROC curves in most cases.

关键词： Workflow mining process mining knowledge-based process models inductive logic programming statistical relational learning business process management

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MeSH Now: automatic MeSH indexing at PubMed scale via learning to rank

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JOURNAL OF BIOMEDICAL SEMANTICS 2017年第1期8卷 15-15页

作者： Mao, Yuqing Lu, Zhiyong Nanjing Univ Chinese Med 138 Xianlin Ave Nanjing 210023 Jiangsu Peoples R China NCBI 8600 Rockville Pike Bethesda MD 20894 USA

Background: MeSH indexing is the task of assigning relevant MeSH terms based on a manual reading of scholarly publications by human indexers. The task is highly important for improving literature retrieval and many other scientific investigations in biomedical research. Unfortunately, given its manual nature, the process of MeSH indexing is both time-consuming (new articles are not immediately indexed until 2 or 3 months later) and costly (approximately ten dollars per article). In response, automatic indexing by computers has been previously proposed and attempted but remains challenging. In order to advance the state of the art in automatic MeSH indexing, a community-wide shared task called BioASQ was recently organized. Methods: We propose MeSH Now, an integrated approach that first uses multiple strategies to generate a combined list of candidate MeSH terms for a target article. Through a novel learning-to-rank framework, MeSH Now then ranks the list of candidate terms based on their relevance to the target article. Finally, MeSH Now selects the highest-ranked MeSH terms via a post-processing module. Results: We assessed MeSH Now on two separate benchmarking datasets using traditional precision, recall and F(1-)score metrics. In both evaluations, MeSH Now consistently achieved over 0.60 in F-score, ranging from 0.610 to 0. 612. Furthermore, additional experiments show that MeSH Now can be optimized by parallel computing in order to process MEDLINE documents on a large scale. Conclusions: We conclude that MeSH Now is a robust approach with state-of-the-art performance for automatic MeSH indexing and that MeSH Now is capable of processing PubMed scale documents within a reasonable time frame.

关键词： Text Classification MeSH Term Unify Medical Language System inductive logic programming Target Article

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Optimal Multirobot Path Planning on Graphs: Complete Algorithms and Effective Heuristics

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IEEE TRANSACTIONS ON ROBOTICS 2016年第5期32卷 1163-1177页

作者： Yu, Jingjin LaValle, Steven M. Rutgers State Univ Dept Comp Sci New Brunswick NJ 08901 USA Univ Illinois Dept Comp Sci Champaign IL 61820 USA

We study optimal multirobot path planning on graphs (MPP) over four minimization objectives: the makespan (last arrival time), the maximum (single-robot traveled) distance, the total arrival time, and the total distance. Having established previously that these objectives are distinct and NP-hard to optimize, in this paper, we focus on efficient algorithmic solutions for solving these optimal MPP problems. Toward this goal, we first establish a one-to-one solution mapping between MPP and a special type of multiflow network. Based on this equivalence and integer linear programming (ILP), we design novel and complete algorithms for optimizing over each of the four objectives. In particular, our exact algorithm for computing optimal makespan solutions is a first that is capable of solving extremely challenging problems with robot-vertex ratios as high as 100%. Then, we further improve the computational performance of these exact algorithms through the introduction of principled heuristics, at the expense of slight optimality loss. The combination of ILP model based algorithms and the heuristics proves to be highly effective, allowing the computation of 1.x-optimal solutions for problems containing hundreds of robots, densely populated in the environment, often in just seconds.

关键词： Heuristics inductive logic programming Path planning algorithms arrival time PROBLEM SOLUTION Nondeterministic polynomial-time hard computational performance

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Assessing sentence similarity through lexical, syntactic and semantic analysis

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COMPUTER SPEECH AND LANGUAGE 2016年 39卷 1-28页

作者： Ferreira, Rafael Lins, Rafael Dueire Simske, Steven J. Freitas, Fred Riss, Marcelo Univ Fed Pernambuco Informat Ctr Recife PE Brazil Univ Fed Rural Pernambuco Dept Stat & Informat Recife PE Brazil HP Labs Ft Collins CO 80528 USA HP Brazil Porto Alegre RS Brazil

The degree of similarity between sentences is assessed by sentence similarity methods. Sentence similarity methods play an important role in areas such as summarization, search, and categorization of texts, machine translation, etc. The current methods for assessing sentence similarity are based only on the similarity between the words in the sentences. Such methods either represent sentences as bag of words vectors or are restricted to the syntactic information of the sentences. Two important problems in language understanding are not addressed by such strategies: the word order and the meaning of the sentence as a whole. The new sentence similarity assessment measure presented here largely improves and refines a recently published method that takes into account the lexical, syntactic and semantic components of sentences. The new method was benchmarked using Li-McLean, showing that it outperforms the state of the art systems and achieves results comparable to the evaluation made by humans. Besides that, the method proposed was extensively tested using the SemEval 2012 sentence similarity test set and in the evaluation of the degree of similarity between summaries using the CNN-corpus. In both cases, the measure proposed here was proved effective and useful. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Graph-based model Sentence simplification Relation extraction inductive logic programming

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Policy-based memoization for ILP-based concept discovery systems

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2016年第1期46卷 99-120页

作者： Mutlu, Alev Karagoz, Pinar Selcuk Univ Dept Comp Engn TR-42075 Konya Turkey Middle E Tech Univ Dept Comp Engn TR-06800 Ankara Turkey

inductive programming logic (ILP)-based concept discovery systems aim to find patterns that describe a target relation in terms of other relations provided as background knowledge. Such systems usually work within first order logic framework, build large search spaces, and have long running times. Memoization has widely been incorporated in concept discovery systems to improve their running times. One of the problems that memoization brings to such systems is the memory overhead which may be a bottleneck. In this work we propose policies that decide what types of concept descriptors to store in memotables and for how long to keep them. The proposed policies have been implemented as extensions to a concept discovery system called Tabular CRIS wEF, and the resulting system is named Policy-based Tabular CRIS. Effects of the proposed policies are evaluated on several datasets. The experimental results show that the proposed policies greatly improve the memory consumption while preserving the benefits introduced by memoization.

关键词： Multi-relational data mining inductive logic programming Concept discovery Memoization Memory consumption Scalability

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Automatic discovery of relational concepts by an incremental graph-based representation

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ROBOTICS AND AUTONOMOUS SYSTEMS 2016年 83卷 1-14页

作者： Tenorio-Gonzalez, Ana C. Morales, Eduardo F. Inst Nacl Astrofis Opt & Electr Luis Enrique Erro 1 Puebla 72840 Mexico

Automatic discovery of concepts has been an elusive area in machine learning. In this paper, we describe a system, called ADC, that automatically discovers concepts in a robotics domain, performing predicate invention. Unlike traditional approaches of concept discovery, our approach automatically finds and collects instances of potential relational concepts. An agent, using ADC, creates an incremental graph-based representation with the information it gathers while exploring its environment, from which common sub-graphs are identified. The subgraphs discovered are instances of potential relational concepts which are induced with inductive logic programming and predicate invention. Several concepts can be induced concurrently and the learned concepts can form arbitrarily hierarchies. The approach was tested for learning concepts of polygons, furniture, and floors of buildings with a simulated robot and compared with concepts suggested by users. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Robot learning Automatic concept learning Concept discovery Predicate invention inductive logic programming

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ILP-assisted de novo drug design

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MACHINE LEARNING 2016年第3期103卷 309-341页

作者： Kaalia, Rama Srinivasan, Ashwin Kumar, Amit Ghosh, Indira Jawaharlal Nehru Univ Sch Computat & Integrat Sci New Delhi 110067 India BITS Dept Comp Sci & Informat Syst Pilani Goa India

De novo design of drugs uses the three-dimensional structure of a target protein (often called the receptor) to design molecules (or ligands) that could bind to the receptor and hence inhibit its functioning. Thus, unlike a ligand-based approach, this form of drug design does not require prior knowledge of inhibitors. In this paper, the three-dimensional structure of a receptor is used indirectly, in the form of molecular interaction fields of the receptor and small molecules (or probes). In addition, we also use domain-specific constraints encoding basic geometric and pharmacological requirements imposed by the target. Interaction energies of one or more targets with a set of probes are used to identify three-dimensional constraints that occur in many-preferably all-targets. In a graph-theoretic sense, the constraints are (small, fixed-size) cliques in graphs with labelled vertices representing probe-specific points of high interaction energy, and edges between a pair of vertices are labelled by the three-dimensional distance between the corresponding points of interaction. Our interest is in the discovery of frequent cliques that satisfy domain-specific constraints. In the paper, the discovery of such patterns is done using an inductive logic programming (ILP) engine. The case for the use of ILP stems primarily from the explicit ways of incorporating domain-constraints, but any other technique capable of discovering frequent cliques from data can be used with some additional effort. The frequent cliques discovered are used to hypothesize pharmacophore-like structures on potential ligands. We test the utility of this approach by conducting a case study on the discovery of anti-malarials. Specifically, we test the approach on proteins belonging to the class of aspartic proteases. We are particularly interested in plasmepsin II, which is an enzyme in the haemoglobin degradation pathway of Plasmodium falciparum. We assess the pharmacophore-like constraints using: (a)

关键词： De novo drug design inductive logic programming Pharmacophores Frequent cliques

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