Showing papers on "Knowledge representation and reasoning published in 2017"
Book•
01 Apr 2017TL;DR: This introduction presents the main motivations for the development of Description Logics as a formalism for representing knowledge, as well as some important basic notions underlying all systems that have been created in the DL tradition.
Abstract: This introduction presents the main motivations for the development of Description Logics (DLs) as a formalism for representing knowledge, as well as some important basic notions underlying all systems that have been created in the DL tradition. In addition, we provide the reader with an overview of the entire book and some guidelines for reading it.We first address the relationship between Description Logics and earlier semantic network and frame systems, which represent the original heritage of the field. We delve into some of the key problems encountered with the older efforts. Subsequently, we introduce the basic features of DL languages and related reasoning techniques.DL languages are then viewed as the core of knowledge representation systems. considering both the structure of a DL knowledge base and its associated reasoning services. The development of some implemented knowledge representation systems based on Description Logics and the first applications built with such systems are then reviewed.Finally, we address the relationship of Description Logics to other fields of Computer Science. We also discuss some extensions of the basic representation language machinery; these include features proposed for incorporation in the formalism that originally arose in implemented systems, and features proposed to cope with the needs of certain application domains.
470 citations
TL;DR: It is shown how explicit knowledge management, both symbolic and geometric, proves to be instrumental to richer and more natural humanrobot interactions by pushing for pervasive, human-level semantics within the robot's deliberative system.
273 citations
TL;DR: This work presents an overview of the improved FPN theories and models from the perspectives of reasoning algorithms, knowledge representations and FPN models, and offers directions for future research to improve the FPN performance.
151 citations
TL;DR: A content-aware search scheme, which can make semantic search more smart and employ the technology of multi-keyword ranked search over encrypted cloud data as the basis against two threat models to resolve the problem of privacy-preserving smart semantic search.
Abstract: Searchable encryption is an important research area in cloud computing. However, most existing efficient and reliable ciphertext search schemes are based on keywords or shallow semantic parsing, which are not smart enough to meet with users’ search intention. Therefore, in this paper, we propose a content-aware search scheme, which can make semantic search more smart. First, we introduce conceptual graphs (CGs) as a knowledge representation tool. Then, we present our two schemes (PRSCG and PRSCG-TF) based on CGs according to different scenarios. In order to conduct numerical calculation, we transfer original CGs into their linear form with some modification and map them to numerical vectors. Second, we employ the technology of multi-keyword ranked search over encrypted cloud data as the basis against two threat models and raise PRSCG and PRSCG-TF to resolve the problem of privacy-preserving smart semantic search based on CGs. Finally, we choose a real-world data set: CNN data set to test our scheme. We also analyze the privacy and efficiency of proposed schemes in detail. The experiment results show that our proposed schemes are efficient.
141 citations
TL;DR: This article discusses why the requirements of a robot knowledge processing system differ from what is commonly investigated in AI research, and proposes to re-consider a KR system as a semantically annotated view on information and algorithms that are often already available as part of the robot's control system.
136 citations
TL;DR: This work develops a novel method for feature learning on biological knowledge graphs that combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs.
Abstract: Motivation Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. Results: We develop a novel method for feature learning on biological knowledge graphs. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs. Through the use of symbolic logic, these embeddings contain both explicit and implicit information. We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target relations, and demonstrate performance that matches and sometimes outperforms traditional approaches based on manually crafted features. Our method can be applied to any biological knowledge graph, and will thereby open up the increasing amount of Semantic Web based knowledge bases in biology to use in machine learning and data analytics. Availability and implementation https://github.com/bio-ontology-research-group/walking-rdf-and-owl. Contact robert.hoehndorf@kaust.edu.sa. Supplementary information Supplementary data are available at Bioinformatics online.
124 citations
TL;DR: This work studies how to utilize semantic IoT data for reasoning of actionable knowledge by applying state-of-the-art semantic technologies, and evaluates latencies of reasoning introduced by different semantic data formats.
Abstract: Acquiring knowledge from continuous and heterogeneous data streams is a prerequisite for Internet of Things (IoT) applications. Semantic technologies provide comprehensive tools and applicable methods for representing, integrating, and acquiring knowledge. However, resource-constraints, dynamics, mobility, scalability, and real-time requirements introduce challenges for applying these methods in IoT environments. We study how to utilize semantic IoT data for reasoning of actionable knowledge by applying state-of-the-art semantic technologies. For performing these studies, we have developed a semantic reasoning system operating in a realistic IoT environment. We evaluate the scalability of different reasoning approaches, including a single reasoner, distributed reasoners, mobile reasoners, and a hybrid of them. We evaluate latencies of reasoning introduced by different semantic data formats. We verify the capabilities of promising semantic technologies for IoT applications through comparing the scalability and real-time response of different reasoning approaches with various semantic data formats. Moreover, we evaluate different data aggregation strategies for integrating distributed IoT data for reasoning processes.
119 citations
TL;DR: The inference capability introduced in this study was integrated into a joint space control loop for a humanoid robot, an iCub, for achieving similar goals to the human demonstrator online.
111 citations
15 Jun 2017
TL;DR: A novel multi-layer architecture for representing customer reviews that outperforms the well-known methods in previous studies on aspect-based sentiment analysis and generates the aspect ratings as well as aspect weights.
Abstract: Sentiment Analysis is the task of automatically discovering the exact sentimental ideas about a product (or service, social event, etc.) from customer textual comments (i.e. reviews) crawled from various social media resources. Recently, we can see the rising demand of aspect-based sentiment analysis, in which we need to determine sentiment ratings and importance degrees of product aspects. In this paper we propose a novel multi-layer architecture for representing customer reviews. We observe that the overall sentiment for a product is composed from sentiments of its aspects, and in turn each aspect has its sentiments expressed in related sentences which are also the compositions from their words. This observation motivates us to design a multiple layer architecture of knowledge representation for representing the different sentiment levels for an input text. This representation is then integrated into a neural network to form a model for prediction of product overall ratings. We will use the representation learning techniques including word embeddings and compositional vector models, and apply a back-propagation algorithm based on gradient descent to learn the model. This model consequently generates the aspect ratings as well as aspect weights (i.e. aspect importance degrees). Our experiment is conducted on a data set of reviews from hotel domain, and the obtained results show that our model outperforms the well-known methods in previous studies.
101 citations
TL;DR: Several general approaches for knowledge transfer in both SVM and ANN frameworks are described and algorithmic implementations and performance of one of these approaches is illustrated for several synthetic examples.
Abstract: The paper considers general machine learning models, where knowledge transfer is positioned as the main method to improve their convergence properties. Previous research was focused on mechanisms of knowledge transfer in the context of SVM framework; the paper shows that this mechanism is applicable to neural network framework as well. The paper describes several general approaches for knowledge transfer in both SVM and ANN frameworks and illustrates algorithmic implementations and performance of one of these approaches for several synthetic examples.
82 citations
TL;DR: An advanced version of Dual-PECCS, a cognitively-inspired knowledge representation and reasoning system aimed at extending the capabilities of artificial systems in conceptual categorization tasks, is presented and integrated and tested into two cognitive architectures, ACT-R and CLARION, implementing different assumptions on the underlying invariant structures governing human cognition.
Abstract: In this article we present an advanced version of Dual-PECCS, a cognitively-inspired knowledge representation and reasoning system aimed at extending the capabilities of artificial systems in conceptual categorization tasks. It combines different sorts of common-sense categorization (prototypical and exemplars-based categorization) with standard monotonic categorization procedures. These different types of inferential procedures are reconciled according to the tenets coming from the dual process theory of reasoning. On the other hand, from a representational perspective, the system relies on the hypothesis of conceptual structures represented as heterogeneous proxytypes. Dual-PECCS has been experimentally assessed in a task of conceptual categorization where a target concept illustrated by a simple common-sense linguistic description had to be identified by resorting to a mix of categorization strategies, and its output has been compared to human responses. The obtained results suggest that our approach c...
19 Aug 2017
TL;DR: In this article, an Image-Embodied Knowledge Representation Learning (IKRL) model is proposed, where knowledge representations are learned with both triple facts and images, and these image representations are then integrated into an aggregated image-based representation via an attention-based method.
Abstract: Entity images could provide significant visual information for knowledge representation learning. Most conventional methods learn knowledge representations merely from structured triples, ignoring rich visual information extracted from entity images. In this paper, we propose a novel Image-embodied Knowledge Representation Learning model (IKRL), where knowledge representations are learned with both triple facts and images. More specifically, we first construct representations for all images of an entity with a neural image encoder. These image representations are then integrated into an aggregated image-based representation via an attention-based method. We evaluate our IKRL models on knowledge graph completion and triple classification. Experimental results demonstrate that our models outperform all baselines on both tasks, which indicates the significance of visual information for knowledge representations and the capability of our models in learning knowledge representations with images.
TL;DR: This work implements one intelligent recommender system based on a general framework that extends the concept of a knowledge-basedRecommender system, and tests the versatility and performance of the framework with specialized criteria linked to the utilization of the knowledge.
TL;DR: The research presented in this paper introduces an innovative approach in AR system design that integrates probabilistic inference with the represented domain ontology through Markov Logic Network (MLN), which is a statistical relational learning approach.
Abstract: Designing an activity recognition system that models various activities of an occupant is the fundamental task in creating a smart home. Activity Recognition (AR) modeling, has witnessed a comprehensive range of research, that focuses independently on probabilistic approaches and on ontology based models as well. The research presented in this paper introduces an innovative approach in AR system design that integrates probabilistic inference with the represented domain ontology. Data obtained from sensors are uncertain in nature and mapping uncertainty over ontology will not yield good accuracy in the context of AR. The proposed system augments ontology based activity recognition with probabilistic reasoning through Markov Logic Network (MLN) which is a statistical relational learning approach. The proposed system utilizes the model theoretic semantic property of description logic, to convert the represented ontology activity model to its corresponding first order rules. MLN is constructed by learning weighted first order rules that enable probabilistic reasoning within a knowledge representation framework. The experiments based on datasets obtained from smart home prototypes illustrate the effectiveness of integrating probabilistic reasoning over domain ontology and the result analysis shows enhanced recognition accuracy in comparison with existing approaches.
01 Dec 2017
TL;DR: This study suggests that it is necessary to critically interrogate the advantages of big data approach to knowledge representation and organization to spark conversations about the cultural, technological, scholarly, societal and ethical implications of data driven approach to the knowledge representation, organization and discovery.
Abstract: We live in the age of big data, wherein production and analysis of the massive amounts of data in relation to the various interactions of humans, objects and technologies have become a new everyday common. It comes with no surprise that knowledge organization community has also embraced the data-driven inquiry to advance representation, organization and discovery of the knowledge. In particular, semantic technologies allowed to connect knowledge across institutions, platforms and cultures, bringing a new dimension to representation and organization of knowledge. This paper presents analysis of the knowledge organization research that employed a large-scale or big data analysis techniques to find what are methodologies, research questions, and implications of big data approaches are. Analysis of over 500 scholarly works indexed in Library and Information Science Full text and Google Scholar databases suggests advantages of a large-scale data integration approaches. For instance, Baca and Gill (2015) paper presents how semantic technologies have allowed multilingual and cross-cultural representation of Getty Art & Architecture Thesaurus (AAT), the Getty Thesaurus of Geographic Names (TGN) and the Union List of Artist Names (ULAN). Mayr and Zeng (2017) argue that the semantic web standards, such as SKOS, OWL, RDFS, and SPARQL allowes to publish knowledge organization systems (KOS) as Linked Open Data (LOD). Mayr and Zeng proposes the following outcomes of LOD application: transformation of KOS vocabulary into the lightweight OWL ontologies or SKOS vocabulary datasets, and accessibility of the data by means of SPARQL endpoints. However, the data-driven knowledge organization initiatives raise significant questions on whether data-driven access to the knowledge would facilitate and/or transform the use and accessibility of the knowledge organization systems, whether it would help us to understand humans’ knowledge representation, organization and discovery behavior, or whether it would usher new forms of biases, limitations and privacy incursions. A large corpus of knowledge representation and organization research have discussed various biases of knowledge organization systems, such as representation of marginalized and indigenous populations. For instance, indigenous scholars have demonstrated lack of understanding of indigenous epistemologies in representation of indigenous cultures that resulted in limited and partial representation of indigenous knowledge (Doyle, 2006; Metoyer & Doyle, 2015) . Moreover, algorithmic biases that are built-in in platforms and systems, such as Google search engine, are another major concern when it comes to such issues as utilization of user-generated content to complement traditional representation of resources. The data-driven approach also raises ethical issues related to incorporation of user-generated content without users’ consent. In this regard, Ibekwe-SanJuan and Bowker (2017) confront the relevance of universal bibliographic classification and thesaurus, arguing that big data will not remove the need for human constructed systems. Authors also suggest a shift from purely universalist and top-down approach to more descriptive bottom-up approaches that could potentially include diverse viewpoints. Taking into consideration the complexity of the process of representation of knowledge, we argue that data-drive approach would have little to no effect on eliminating limitations and biases of existing knowledge organization and discovery systems. This study suggests that it is necessary to critically interrogate the advantages of big data approach to knowledge representation and organization to spark conversations about the cultural, technological, scholarly, societal and ethical implications of data driven approach to the knowledge representation, organization and discovery. This study argues that while a data-driven approach would certainly be valuable in provision of a large-scale representation of knowledge, only human- and community- centered approaches to knowledge representation and organization would enhance and ensure multifaceted and rich representation of the knowledge. References Baca, M., & Gill, M. (2015). Encoding multilingual knowledge systems in the digital age: The Getty vocabularies. The fifth North American Symposium on Knowledge Organization ( NASKO 2015), June 18-19, 2015, Los Angeles, California. Retrieved from http://www.iskocus.org/NASKO2015proceedings/Gill%20.pdf Doyle, A. M. (2006). Naming and reclaiming indigenous knowledge: Intersections of landscape and experience. In G. Budin, C. Swertz & K.Mitgutsch (Eds.) Advances in knowledge organization (10), Knowledge Organization for a Global Learning Society: Proceedings of the Ninth International ISKO Conference in Vienna, Austria, 2006, Ergon Verlag, Wurzburg, pp. 435-442. Ibekwe-SanJuan, F., & Bowker, G.C. (2017). Implications of big data for knowledge organization. Knowledge Organization, 44 (3) , 187-198. Mayr, P., & Zeng, M. (2017). Knowledge organization systems in the semantic web. International Society for Knowledge Organization (ISKO), UK Conference 2017, September 11-12. 2017, London, UK. Retrieved from http://www.iskouk.org/content/knowledge-organization-systems-kos-semantic-web Metoyer, C. A., & Doyle, A.M. (2015). Introduction. Cataloging & Classification Quarterly,53 (5-6), 475-478.
TL;DR: The research presented in this paper shows that decomposing a building code into four levels and modeling rules based on the semantic-oriented paradigm is an effective modeling strategy for representing building codes in a computable form.
01 Jan 2017
TL;DR: This work proposes ETransR, a method which automatically learns entity and relation feature representations in continuous vector spaces, in order to measure the semantic relatedness of knowledge mentions for knowledge resolution.
Abstract: Knowledge resolution is the task of clustering knowledge mentions, e.g., entity and relation mentions into several disjoint groups with each group representing a unique entity or relation. Such resolution is a central step in constructing high-quality knowledge graph from unstructured text. Previous research has tackled this problem by making use of various textual and structural features from a semantic dictionary or a knowledge graph. This may lead to poor performance on knowledge mentions with poor or not well-known contexts. In addition, it is also limited by the coverage of the semantic dictionary or knowledge graph. In this work, we propose ETransR, a method which automatically learns entity and relation feature representations in continuous vector spaces, in order to measure the semantic relatedness of knowledge mentions for knowledge resolution. Experimental results on two benchmark datasets show that our proposed method delivers significant improvements compared with the state-of-the-art baselines on the task of knowledge resolution.
TL;DR: A survey of qualitative spatial and temporal reasoning methods can be found in this paper, where the authors present a classification of qualitative calculi according to their algebraic properties, including their computational properties.
Abstract: Qualitative spatial and temporal reasoning (QSTR) is concerned with symbolic knowledge representation, typically over infinite domains. The motivations for employing QSTR techniques include exploiting computational properties that allow efficient reasoning to capture human cognitive concepts in a computational framework. The notion of a qualitative calculus is one of the most prominent QSTR formalisms. This article presents the first overview of all qualitative calculi developed to date and their computational properties, together with generalized definitions of the fundamental concepts and methods that now encompass all existing calculi. Moreover, we provide a classification of calculi according to their algebraic properties.
TL;DR: This work exposed English, Mandarin, and Farsi native speakers to native language translations of the same stories during fMRI scanning to demonstrate that neuro‐semantic encoding of narratives happens at levels higher than individual semantic units and that this encoding is systematic across both individuals and languages.
Abstract: Drawing from a common lexicon of semantic units, humans fashion narratives whose meaning transcends that of their individual utterances. However, while brain regions that represent lower-level semantic units, such as words and sentences, have been identified, questions remain about the neural representation of narrative comprehension, which involves inferring cumulative meaning. To address these questions, we exposed English, Mandarin, and Farsi native speakers to native language translations of the same stories during fMRI scanning. Using a new technique in natural language processing, we calculated the distributed representations of these stories (capturing the meaning of the stories in high-dimensional semantic space), and demonstrate that using these representations we can identify the specific story a participant was reading from the neural data. Notably, this was possible even when the distributed representations were calculated using stories in a different language than the participant was reading. Our results reveal that identification relied on a collection of brain regions most prominently located in the default mode network. These results demonstrate that neuro-semantic encoding of narratives happens at levels higher than individual semantic units and that this encoding is systematic across both individuals and languages. Hum Brain Mapp 38:6096-6106, 2017. © 2017 Wiley Periodicals, Inc.
TL;DR: A new approach is focused on reducing complexity in the modelling process, which provides a more transparent and easy to use model for policy makers.
TL;DR: It is argued that the underlying structures that are common to the world's languages bear an intriguing connection with early emerging forms of "core knowledge" and that developmental researchers and cognitive scientists interested in (non-verbal) knowledge representation can exploit this connection to language by using observations about cross-linguistic grammatical tendencies to inspire hypotheses about core knowledge.
01 Aug 2017
TL;DR: The current version of the ontology for autonomous robotics as well as on its first implementation successfully validated for a human-robot interaction scenario are reported on, demonstrating the developed ontology's strengths which include semantic interoperability and capability to relate ontologies from different fields for knowledge sharing and interactions.
Abstract: Creating a standard for knowledge representation and reasoning in autonomous robotics is an urgent task if we consider recent advances in robotics as well as predictions about the insertion of robots in human daily life. Indeed, this will impact the way information is exchanged between multiple robots or between robots and humans and how they can all understand it without ambiguity. Indeed, Human Robot Interaction (HRI) represents the interaction of at least two cognition models (Human and Robot). Such interaction informs task composition, task assignment, communication, cooperation and coordination in a dynamic environment, requiring a flexible representation. Hence, this paper presents the IEEE RAS Autonomous Robotics (AuR) Study Group, which is a spin-off of the IEEE Ontologies for Robotics and Automation (ORA) Working Group, and and its ongoing work to develop the first IEEE-RAS ontology standard for autonomous robotics. In particular, this paper reports on the current version of the ontology for autonomous robotics as well as on its first implementation successfully validated for a human-robot interaction scenario, demonstrating the developed ontology's strengths which include semantic interoperability and capability to relate ontologies from different fields for knowledge sharing and interactions.
TL;DR: It is considered that testing the rest of variable examined in the Bayesian network can provide better accurate in the diagnostic of student’ knowledge possession.
TL;DR: In this article, a tutorial on formal concept analysis (FCA) and its applications is presented, which is an applied branch of Lattice theory, a mathematical discipline which enables formalisation of concepts as basic units of human thinking and analysing data in the object-attribute form.
Abstract: This paper is a tutorial on Formal Concept Analysis (FCA) and its applications. FCA is an applied branch of Lattice Theory, a mathematical discipline which enables formalisation of concepts as basic units of human thinking and analysing data in the object-attribute form. Originated in early 80s, during the last three decades, it became a popular human-centred tool for knowledge representation and data analysis with numerous applications. Since the tutorial was specially prepared for RuSSIR 2014, the covered FCA topics include Information Retrieval with a focus on visualisation aspects, Machine Learning, Data Mining and Knowledge Discovery, Text Mining and several others.
12 Jun 2017
TL;DR: This paper tries to improve Information Extraction in legal texts by creating a legal Named Entity Recognizer, Classifier and Linker, developed with relatively little effort by mapping the LKIF ontology to the YAGO ontology and through it, taking advantage of the mentions of entities in the Wikipedia.
Abstract: In this paper we try to improve Information Extraction in legal texts by creating a legal Named Entity Recognizer, Classifier and Linker. With this tool, we can identify relevant parts of texts and connect them to a structured knowledge representation, the LKIF ontology.More interestingly, this tool has been developed with relatively little effort, by mapping the LKIF ontology to the YAGO ontology and through it, taking advantage of the mentions of entities in the Wikipedia. These mentions are used as manually annotated examples to train the Named Entity Recognizer, Classifier and Linker.We have evaluated the approach on holdout texts from the Wikipedia and also on a small sample of judgments of the European Court of Human Rights, resulting in a very good performance, i.e., around 80% F-measure for different levels of granularity. We present an extensive error analysis to direct further developments, and we expect that this approach can be successfully ported to other legal subdomains, represented by different ontologies.
TL;DR: An in-depth understanding of the knowledge diffusion process in Stack overflow is obtained and the implications of URL sharing behavior for Q&A site design, developers who use crowdsourced knowledge in Stack Overflow, and future research on knowledge representation and search are exposed.
Abstract: Programming-specific Q&A sites (e.g., Stack Overflow) are being used extensively by software developers for knowledge sharing and acquisition. Due to the cross-reference of questions and answers (note that users also reference URLs external to the Q&A site. In this paper, URL sharing refers to internal URLs within the Q&A site, unless otherwise stated), knowledge is diffused in the Q&A site, forming a large knowledge network. In Stack Overflow, why do developers share URLs? How is the community feedback to the knowledge being shared? What are the unique topological and semantic properties of the resulting knowledge network in Stack Overflow? Has this knowledge network become stable? If so, how does it reach to stability? Answering these questions can help the software engineering community better understand the knowledge diffusion process in programming-specific Q&A sites like Stack Overflow, thereby enabling more effective knowledge sharing, knowledge use, and knowledge representation and search in the community. Previous work has focused on analyzing user activities in Q&A sites or mining the textual content of these sites. In this article, we present a methodology to analyze URL sharing activities in Stack Overflow. We use open coding method to analyze why users share URLs in Stack Overflow, and develop a set of quantitative analysis methods to study the structural and dynamic properties of the emergent knowledge network in Stack Overflow. We also identify system designs, community norms, and social behavior theories that help explain our empirical findings. Through this study, we obtain an in-depth understanding of the knowledge diffusion process in Stack Overflow and expose the implications of URL sharing behavior for Q&A site design, developers who use crowdsourced knowledge in Stack Overflow, and future research on knowledge representation and search.
TL;DR: The proposed approach extends the existing framework of representing temporal information in ontologies by allowing for representation of concepts evolving in time and of their properties in terms of qualitative descriptions in addition to quantitative ones, as well as integrating temporal reasoning support into the proposed representation.
Abstract: The representation of temporal information has been in the center of intensive research activities over the years in the areas of knowledge representation, databases and more recently, the Semantic Web. The proposed approach extends the existing framework of representing temporal information in ontologies by allowing for representation of concepts evolving in time (referred to as “dynamic” information) and of their properties in terms of qualitative descriptions in addition to quantitative ones (i.e., dates, time instants and intervals). For this purpose, we advocate the use of natural language expressions, such as “before” or “after”, for temporal entities whose exact durations or starting and ending points in time are unknown. Reasoning over all types of temporal information (such as the above) is also an important research problem. The current work addresses all these issues as follows: The representation of dynamic concepts is achieved using the “4D-fluents” or, alternatively, the “N-ary relations” mechanism. Both mechanisms are thoroughly explored and are expanded for representing qualitative and quantitative temporal information in OWL. In turn, temporal information is expressed using either intervals or time instants. Qualitative temporal information representation in particular, is realized using sets of SWRL rules and OWL axioms leading to a sound, complete and tractable reasoning procedure based on path consistency applied on the existing relation sets. Building upon existing Semantic Web standards (OWL), tools and member submissions (SWRL), as well as integrating temporal reasoning support into the proposed representation, are important design features of our approach.
Proceedings Article•
01 Jan 2017TL;DR: This paper proposes a novel Service-Oriented Architecture (SOA) based on a semantic blockchain for registration, discovery, selection and payment, implemented as smart contracts, allowing distributed execution and trust.
Abstract: Generally scarce computational and memory resource availability is a well known problem for the IoT, whose intrinsic volatility makes complex applications unfeasible. Noteworthy efforts in overcoming unpredictability (particularly in case of large dimensions) are the ones integrating Knowledge Representation technologies to build the so-called Semantic Web of Things (SWoT). In spite of allowed advanced discovery features, transactions in the SWoT still suffer from not viable trust management strategies. Given its intrinsic characteristics, blockchain technology appears as interesting from this perspective: a semantic resource/service discovery layer built upon a basic blockchain infrastructure gains a consensus validation. This paper proposes a novel Service-Oriented Architecture (SOA) based on a semantic blockchain for registration, discovery, selection and payment. Such operations are implemented as smart contracts, allowing distributed execution and trust. Reported experiments early assess the sustainability of the proposal.
TL;DR: It is argued for capturing physician knowledge using a novel knowledge representation model of the clinical picture based on structured patient presentation patterns, which calls for the collection of crowdsourced, automatically deidentified, structured patient patterns as means to support distributed knowledge accumulation and maintenance.
Abstract: Physicians intuitively apply pattern recognition when evaluating a patient. Rational diagnosis making requires that clinical patterns be put in the context of disease prior probability, yet physicians often exhibit flawed probabilistic reasoning. Difficulties in making a diagnosis are reflected in the high rates of deadly and costly diagnostic errors. Introduced 6 decades ago, computerized diagnosis support systems are still not widely used by internists. These systems cannot efficiently recognize patterns and are unable to consider the base rate of potential diagnoses. We review the limitations of current computer-aided diagnosis support systems. We then portray future diagnosis support systems and provide a conceptual framework for their development. We argue for capturing physician knowledge using a novel knowledge representation model of the clinical picture. This model (based on structured patient presentation patterns) holds not only symptoms and signs but also their temporal and semantic interrelations. We call for the collection of crowdsourced, automatically deidentified, structured patient patterns as means to support distributed knowledge accumulation and maintenance. In this approach, each structured patient pattern adds to a self-growing and -maintaining knowledge base, sharing the experience of physicians worldwide. Besides supporting diagnosis by relating the symptoms and signs with the final diagnosis recorded, the collective pattern map can also provide disease base-rate estimates and real-time surveillance for early detection of outbreaks. We explain how health care in resource-limited settings can benefit from using this approach and how it can be applied to provide feedback-rich medical education for both students and practitioners.
24 Mar 2017
TL;DR: Reports on the formation and activities undertaken by the Ontologies for Robotics and Automation (ORA) Working Group suggest that the standard provides a unified way of representing knowledge and provides a common set of terms and definitions, allowing for unambiguous knowledge transfer among any group of human, robots, and other artificial systems.
Abstract: Reports on the formation and activities undertaken by the Ontologies for Robotics and Automation (ORA) Working Group. ORA was established in 2011 by the IEEE Standard Association's Robotics Society. The goal of the group is to develop a standard to provide an overall ontology and associated methodology for knowledge representation and reasoning in robotics and automation together with the representation of concepts in an initial set of application domains. The standard provides a unified way of representing knowledge and provides a common set of terms and definitions, allowing for unambiguous knowledge transfer among any group of human, robots, and other artificial systems.