Search query: Decomposability in knowledge structures

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abstract: We use patent data from the worldwide semiconductor industry from 1984 to 1994 to study the effect of the structure of organizational knowledge bases, or the patterns of coupling between their elements of technical knowledge, on the usefulness of inventions and knowledge base malleability. We argue that organizational variations in coupling patterns between knowledge elements can be reflected in a spectrum of knowledge base structures varying from fully decomposable (the knowledge base is composed of distinct clusters of knowledge elements coupled together with no significant ties between clusters) through nearly decomposable (knowledge clusters are discernable but are connected through cross cluster couplings) to non decomposable (no knowledge clusters emerge, as the couplings are pervasively distributed) and that organizations may differ in the way they use their knowledge because of variations in their knowledge base structure, rather than because of differences in the knowledge elements themselves. Results show that a nearly decomposable knowledge base increases the usefulness of the inventions generated from it, as measured by patent citations, and also the knowledge base's malleability or capacity for change.
author_names: Sai Yayavaram

Gautam Ahuja
title: Decomposability in Knowledge Structures and Its Impact on the Usefulness of Inventions and Knowledge base Malleability
year: 2008

abstract: PRELIMINARIES: BASIC MATHEMATICAL NOTIONS. PROBABILITY. Basic Notions. Independence and Conditional Probability: Events. Independence and Conditional Probability: Two Random Variates. Independence and Conditional Probabilities: Random Fields. Log Linear Representations of Probability Distributions of Random Fields. Appendix: Where Does the Probability Come From? GRAPHS AND PROBABILITY. Graphs. From Hierachial Log Linear Models to Graphical Representation of Probability Distributions of Random Fields. Markov Properties. Decomposability and Collapsibility. Decomposability and Approximation. Appendix: Some Additional Facts Concerning Graphs. DECISION MAKING UNDER UNCERTAINTY. Decision Task. Decision Under Ignorance. Maximum Entropy Principle. Minimax Principle. LOCAL COMPUTATIONS WITH PROBABILITIES ON GRAPHICAL STRUCTURES AND INFLUENCE DIAGRAMS. Causal Graphs and Conditional Probability Tables. Local Representation of Probabilities. Local Computations: Inference Engine. Local Computations: Some Technicalities. Shachter's Method. KNOWLEDGE INTEGRATION METHODS. Completeness of Input Knowledge. Optimal Decision. Lagrange Multiplers Method. Iterative Proportional Fitting Procedure. D.S.S. Approximations. Studeng's Method. AN INTRODUCTION TO COMPOSITIONAL SYSTEMS. Basic Definitions and Assumptions on Compositional Systems. Some Properties of Combining Functions. Backward Chaining. Three Valued Systems. The Most Modest Runs. Additional Information on Propositional Logic. COMPOSITIONAL SYSTEMS: AN ALGEBRAIC ANALYSIS. Compositional Systems and Ordered Abelian Groups. Comparative Properties of Compositional Systems. Finitely Generated Ordered Abelian Groups. Where Are Weights of Rules From? A PROBABILISTIC ANALYSIS OF COMPOSITIONAL SYSTEMS. Uncertainty and Probability in Classical Systems. Compositional Systems and Log Linear Representation. Compositional Systems and Graphical Models: The Method of Guarded Use. THE DEMPSTER SHAFER THEORY OF EVIDENCE AND ITS USE IN EXPERT SYSTEMS. An Introduction to Dempster Shafer Theory. Dempster Shafer Theory and Local Computations. Belief Functions and Compositional Systems. ESTIMATION OF PROBABILITIES AND STRUCTURES. Estimation of Probabilities. Estimation of Structures. References.
author_names: Petr Hajek

Tomas Havranek

Radim Jirousek
title: Uncertain information processing in expert systems
year: 1992

abstract: Abstract In knowledge space theory a knowledge structure provides a deterministic representation of the implications among the items in a given set Q Concrete procedures for the efficient assessment of knowledge by means of a knowledge structure have been proposed by Doignon and Falmagne [Falmagne, J. C. Doignon, J. P. (1988a) A class of stochastic procedures for the assessment of knowledge. British Journal of Mathematical and Statistical Psychology, 41, 1 23; Falmagne, J. C. Doignon, J. P. (1988b) A markovian procedure for assessing the state of a system. Journal of Mathematical Psychology, 32, 232 258] The primitive idea at the core of such procedures is that the (correct or wrong) answers of a student to a subset A Q of items could be inferred from the answers to a subset B Q of items that were previously presented to that student. Since B provides information about A from the viewpoint of the teacher these two subsets are not independent. This idea of dependence vs. independence is formalized in this paper in terms of an independence relation on the power set of Q A nice characterization of this relation allows to express an arbitrary knowledge structure as the combination of a number of substructures each of which is independent of each other. An algorithm is then proposed which checks for independence in a knowledge structure and decomposes this last into a collection of independent substructures.
author_names: Luca Stefanutti
title: A characterization of the concept of independence in knowledge structures
year: 2008

abstract: Knowledge graphs contain knowledge about the world and provide a structured representation of this knowledge. Current knowledge graphs contain only a small subset of what is true in the world. Link prediction approaches aim at predicting new links for a knowledge graph given the existing links among the entities. Tensor factorization approaches have proved promising for such link prediction problems. Proposed in 1927, Canonical Polyadic (CP) decomposition is among the first tensor factorization approaches. CP generally performs poorly for link prediction as it learns two independent embedding vectors for each entity, whereas they are really tied. We present a simple enhancement of CP (which we call SimplE) to allow the two embeddings of each entity to be learned dependently. The complexity of SimplE grows linearly with the size of embeddings. The embeddings learned through SimplE are interpretable, and certain types of background knowledge can be incorporated into these embeddings through weight tying. We prove SimplE is fully expressive and derive a bound on the size of its embeddings for full expressivity. We show empirically that, despite its simplicity, SimplE outperforms several state of the art tensor factorization techniques. SimplE's code is available on GitHub at this https URL.
author_names: Seyed Mehran Kazemi

David L Poole
title: SimplE Embedding for Link Prediction in Knowledge Graphs
venue: NeurIPS
year: 2018

abstract: Abstract Achieving efficient disinfection of waterborne pathogens with minimized harmful disinfection byproducts demands a facile, cost effective, and environmentally friendly technology. Recently, photocatalytic water disinfection has attracted an ever growing worldwide attention due to its powerful oxidative capability and promising potential in solar energy utilization. Among waterborne pathogens, viruses, which have been found with very small sizes, high risks of illness, and resistant to environmental inactivation/decomposition, pose a great threat to public health. Over the past a few decades, efforts have been made to employ photocatalysis to achieve effective viral inactivation. Though photocatalysis has been comprehensively reviewed for bacterial disinfection, photocatalytic disinfection of viruses with quite different compositions, structures, and resistance to oxidative stress compared to bacteria was not systematically documented. Here, we present an overview of antiviral effects of a wide range of photocatalysts, including TiO2 based, metal containing (other than TiO2) and metal free photocatalysts. Moreover, the development of photocatalytic reactors for viral inactivation is summarized to promote practical engineering applications for water disinfection. In addition, key mechanisms that determine the performance of photocatalytic viral disinfection are reviewed. Future perspectives of research opportunities and challenges in photocatalytic viral disinfection are also included. This review will shed light on the development and implementation of sustainable disinfection strategies for controlling waterborne viruses in the future.
author_names: Chi Zhang

Yi Li

Danmeng Shuai

Yun Shen

Dawei Wang
title: Progress and challenges in photocatalytic disinfection of waterborne Viruses: A review to fill current knowledge gaps
year: 2019

abstract: In recent years, different web knowledge graphs, both free and commercial, have been created. Knowledge graphs use relations between entities to describe facts in the world. We engage in embedding a large scale knowledge graph into a continuous vector space. TransE, TransH, TransR and TransD are promising methods proposed in recent years and achieved state of the art predictive performance. In this paper, we discuss that graph structures should be considered in embedding and propose to embed substructures called "one relation circle" (ORC) to further improve the performance of the above methods as they are unable to encode ORC substructures. Some complex models are capable of handling ORC structures but sacrifice efficiency in the process. To make a good trade off between the model capacity and efficiency, we propose a method to decompose ORC substructures by using two vectors to represent the entity as a head or tail entity with the same relation. In this way, we can encode the ORC structure properly when apply it to TransH, TransR and TransD with almost the same model complexity of themselves. We conduct experiments on link prediction with benchmark dataset WordNet. Our experiments show that applying our method improves the results compared with the corresponding original results of TransH, TransR and TransD.
author_names: Wen Zhang
title: Knowledge Graph Embedding with Diversity of Structures
venue: WWW
year: 2017

abstract: Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts based on existing ones. We propose TuckER, a relatively straightforward but powerful linear model based on Tucker decomposition of the binary tensor representation of knowledge graph triples. TuckER outperforms previous state of the art models across standard link prediction datasets, acting as a strong baseline for more elaborate models. We show that TuckER is a fully expressive model, derive sufficient bounds on its embedding dimensionalities and demonstrate that several previously introduced linear models can be viewed as special cases of TuckER.
author_names: Ivana Balazevic

Carl Allen

Timothy M Hospedales
title: TuckER: Tensor Factorization for Knowledge Graph Completion
venue: EMNLP
year: 2019

abstract: Generating long and semantic coherent reports to describe medical images poses great challenges towards bridging visual and linguistic modalities, incorporating medical domain knowledge, and generating realistic and accurate descriptions. We propose a novel Knowledge driven Encode, Retrieve, Paraphrase (KERP) approach which reconciles traditional knowledge and retrieval based methods with modern learning based methods for accurate and robust medical report generation. Specifically, KERP decomposes medical report generation into explicit medical abnormality graph learning and subsequent natural language modeling. KERP first employs an Encode module that transforms visual features into a structured abnormality graph by incorporating prior medical knowledge; then a Retrieve module that retrieves text templates based on the detected abnormalities; and lastly, a Paraphrase module that rewrites the templates according to specific cases. The core of KERP is a proposed generic implementation unit Graph Transformer (GTR) that dynamically transforms high level semantics between graph structured data of multiple domains such as knowledge graphs, images and sequences. Experiments show that the proposed approach generates structured and robust reports supported with accurate abnormality description and explainable attentive regions, achieving the state of the art results on two medical report benchmarks, with the best medical abnormality and disease classification accuracy and improved human evaluation performance.
author_names: Christy Y Li

Xiaodan Liang

Zhiting Hu

Eric P Xing
title: Knowledge driven Encode, Retrieve, Paraphrase for Medical Image Report Generation
venue: AAAI
year: 2019

abstract: Landfills are a vital component of our waste handling processes. Our lack of knowledge on the microbial processes in these systems, however, hampers our ability to design the next generation of landfills that: (1) enhance the rate and extent of waste decomposition, (2) produce byproducts of some value (e.g. methane that can be used for energy generation) and, (3) minimize their overall impact on driving climate change through the emission of greenhouse gases. In this review, the current state of knowledge the microbial community structure and activity in both the refuse and overlying cover soils is discussed, and suggestions provided for future research in this critical aspect of our infrastructure.
author_names: Jeremy D Semrau
title: Current knowledge of microbial community structures in landfills and its cover soils
venue: Applied Microbiology and Biotechnology
year: 2010

abstract: Customer requirements provide objectives and constraints for all phases of the product development process. For complex system products with a high degree of customization, it is no mean task to ensure that the right persons at the right time have ready access to the selected requirement specifications that they should adhere to and strive to satisfy. In the present work a systematic sequence of development phases associated with computer implemented information structures for requirements, functions and systems have been studied at three companies with widely different products and business scenarios. Although individual adaptations are required, the overall processes for requirement decomposition and propagation appear surprisingly similar. To fully exploit the potential of such information systems, many companies would benefit from also including knowledge structures in their product models. Fundamental product and process knowledge often evolves slowly over time, can be gradually upgraded and be reused many times. It also constitutes one of the company's most valuable assets and should be carefully maintained and enhanced. In the work presented here a few different approaches to integrating knowledge structures into the total product information structure have been developed and exemplified for the three companies studied.Copyright (c) 2003 by ASME
author_names: Staffan Sunnersjo

Ingvar Rask

Rafael Amen
title: Requirement Driven Design Processes With Integrated Knowledge Structures
year: 2003