scholarly journals Error Detection in a Large-Scale Lexical Taxonomy

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 97 ◽  
Author(s):  
Yinan An ◽  
Sifan Liu ◽  
Hongzhi Wang
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Base ◽  
Error Detection ◽  
Large Scale ◽  
Knowledge Bases ◽  
Experimental Results ◽  
Knowledge Graph ◽  
Significant Challenge

Knowledge base (KB) is an important aspect in artificial intelligence. One significant challenge faced by KB construction is that it contains many noises, which prevent its effective usage. Even though some KB cleansing algorithms have been proposed, they focus on the structure of the knowledge graph and neglect the relation between the concepts, which could be helpful to discover wrong relations in KB. Motived by this, we measure the relation of two concepts by the distance between their corresponding instances and detect errors within the intersection of the conflicting concept sets. For efficient and effective knowledge base cleansing, we first apply a distance-based model to determine the conflicting concept sets using two different methods. Then, we propose and analyze several algorithms on how to detect and repair the errors based on our model, where we use a hash method for an efficient way to calculate distance. Experimental results demonstrate that the proposed approaches could cleanse the knowledge bases efficiently and effectively.

Constructing and Utilizing Video Ontology for Accurate and Fast Retrieval

2011 ◽  
Vol 2 (4) ◽  
pp. 59-75 ◽  
Author(s):  
Kimiaki Shirahama ◽  
Kuniaki Uehara
Keyword(s):  
Knowledge Base ◽  
Large Scale ◽  
Video Retrieval ◽  
Computational Cost ◽  
Semantic Content ◽  
Video Data ◽  
Experimental Results ◽  
Huge Number ◽  
Dempster Shafer Theory ◽  
Shafer Theory

This paper examines video retrieval based on Query-By-Example (QBE) approach, where shots relevant to a query are retrieved from large-scale video data based on their similarity to example shots. This involves two crucial problems: The first is that similarity in features does not necessarily imply similarity in semantic content. The second problem is an expensive computational cost to compute the similarity of a huge number of shots to example shots. The authors have developed a method that can filter a large number of shots irrelevant to a query, based on a video ontology that is knowledge base about concepts displayed in a shot. The method utilizes various concept relationships (e.g., generalization/specialization, sibling, part-of, and co-occurrence) defined in the video ontology. In addition, although the video ontology assumes that shots are accurately annotated with concepts, accurate annotation is difficult due to the diversity of forms and appearances of the concepts. Dempster-Shafer theory is used to account the uncertainty in determining the relevance of a shot based on inaccurate annotation of this shot. Experimental results on TRECVID 2009 video data validate the effectiveness of the method.

COMBINING DEFAULT LOGIC DATABASES

1994 ◽  
Vol 03 (03) ◽  
pp. 319-348 ◽  
Author(s):  
CHITTA BARAL ◽  
SARIT KRAUS ◽  
JACK MINKER ◽  
V. S. SUBRAHMANIAN
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Large Scale ◽  
Future Generation ◽  
Knowledge Bases ◽  
Default Logic ◽  
Integrity Constraints ◽  
Specialized Knowledge ◽  
The Past ◽  
The Future

During the past decade, it has become increasingly clear that the future generation of large-scale knowledge bases will consist, not of one single isolated knowledge base, but a multiplicity of specialized knowledge bases that contain knowledge about different domains of expertise. These knowledge bases will work cooperatively, pooling together their varied bodies of knowledge, so as to be able to solve complex problems that no single knowledge base, by itself, would have been able to address successfully. In any such situation, inconsistencies are bound to arise. In this paper, we address the question: "Suppose we have a set of knowledge bases, KB1, …, KBn, each of which uses default logic as the formalism for knowledge representation, and a set of integrity constraints IC. What knowledge base constitutes an acceptable combination of KB1, …, KBn?"

scholarly journals An Approach to Knowledge Base Completion by a Committee-Based Knowledge Graph Embedding

2020 ◽  
Vol 10 (8) ◽  
pp. 2651
Author(s):  
Su Jeong Choi ◽  
Hyun-Je Song ◽  
Seong-Bae Park
Keyword(s):  
Knowledge Base ◽  
Language Processing ◽  
Graph Embedding ◽  
Knowledge Bases ◽  
Knowledge Graph ◽  
Data Sets ◽  
Complete Knowledge ◽  
Proposed Model ◽  
Ranking Task ◽  
Low Dimensional

Knowledge bases such as Freebase, YAGO, DBPedia, and Nell contain a number of facts with various entities and relations. Since they store many facts, they are regarded as core resources for many natural language processing tasks. Nevertheless, they are not normally complete and have many missing facts. Such missing facts keep them from being used in diverse applications in spite of their usefulness. Therefore, it is significant to complete knowledge bases. Knowledge graph embedding is one of the promising approaches to completing a knowledge base and thus many variants of knowledge graph embedding have been proposed. It maps all entities and relations in knowledge base onto a low dimensional vector space. Then, candidate facts that are plausible in the space are determined as missing facts. However, any single knowledge graph embedding is insufficient to complete a knowledge base. As a solution to this problem, this paper defines knowledge base completion as a ranking task and proposes a committee-based knowledge graph embedding model for improving the performance of knowledge base completion. Since each knowledge graph embedding has its own idiosyncrasy, we make up a committee of various knowledge graph embeddings to reflect various perspectives. After ranking all candidate facts according to their plausibility computed by the committee, the top-k facts are chosen as missing facts. Our experimental results on two data sets show that the proposed model achieves higher performance than any single knowledge graph embedding and shows robust performances regardless of k. These results prove that the proposed model considers various perspectives in measuring the plausibility of candidate facts.

scholarly journals Knowledge-Based Systems Manufacturing With Viability Ensuring

2021 ◽  
Author(s):  
Valeriya V. Gribova ◽  
Elena A. Shalfeeva
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Base ◽  
Product Family ◽  
Knowledge Bases ◽  
Knowledge Based Systems ◽  
Manufacturing Environment ◽  
Development Environment ◽  
Systematic Analysis ◽  
Knowledge Based ◽  
Medical Software

Abstract With highly increased competition, intelligent product manufacturing based on interpretable knowledge bases has been recognized as an effective method for building applications of explainable Artificial Intelligence that is the hottest topic in the field of Artificial Intelligence. The success of product family directly depends on how effective the viability mechanisms are laid down in its design. In this paper, a systematic cloud-based set of tool family is proposed to develop viable knowledge-based systems. For productive participation of domain and cognitive specialists in manufacturing, the knowledge base should be declarative, testable and integratable with other architectural components. Mechanisms to ensure KBS viability are provided in an ontology-oriented development environment, where each component is formed in terms of domain ontology by using the adaptable instrumental support. Due to the explicit separation of ontology from knowledge, it became possible to divide competencies between specialists creating an ontology and specialists creating a knowledge base. We rely on the fact that the activity of creating an ontology is significantly different from the activity of creating a knowledge base. Creating an ontology is a creative process that requires a systematic analysis of the domain area in order to identify common patterns among its knowledge.The characteristic properties of knowledge-based systems related to viability are described. It is explained, how these properties are provided in development environments implemented on cloud platform. The concept of a specialized manufacturing environment for knowledge-based system is introduced. The necessary set of tools for such ontology-oriented environment construction is determined. The example of tools for creating specialized manufacturing environments is the instruments implemented on the «IACPaaS» platform. The IACPaaS is already used for collective development of thematic cloud knowledge portals with viable knowledge-based systems. This specialized manufacturing environment has enabled the creation of multi-purpose medical software services to support specialist solutions based on knowledge being remotely improved by experts.

scholarly journals Rules-Based System for Writing Arabic Numerals in Indonesian Words

2021 ◽  
Vol 12 (4) ◽  
pp. 177-185
Author(s):  
F.X. Wisnu Yudo Untoro
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Base ◽  
Experimental Results ◽  
Rule Based ◽  
Forward Chaining ◽  
Rule Based System ◽  
Arabic Number ◽  
Made In

One of the algorithms stored in natural intelligence is the writing of Arabic numerals in Indonesian words. Algorithms in naturals intelligence are not easy to find. This problem gave us an idea to create artificial intelligence that tries to mimic natural intelligence algorithms. The proposed algorithm for building artificial intelligence is an R-Z rule-based system. This rule-based system contains a knowledge base of R-Z rules and a knowledge base of facts. In the knowledge base, the R-Z rule provides the R rule and the Z rule, while the facts knowledge base provides facts in the form of a definite standard number and an affix word. R-Z rule-based system for reasoning writing Arabic numerals in Indonesian words uses forward chaining. Artificial intelligence designs that mimic naturals intelligence in writing numbers in Indonesian words were made in C using Borland C++ 5.02 software. The experimental results show that by applying the R's rule of seven rules and Z's of twenty-five rules, the R-Z rule-based system can write Arabic numerals in Indonesian words from Arabic numerals "0" to Arabic numerals "9999999". For example, to write the Arabic number "10" in Indonesian words, the R-Z rule-based system starts with the R2 rule. Rule R2 takes action on Z3 to create new facts about Arabic numerals in the Indonesian word, namely "SEPULUH."

Telederm

2010 ◽  
pp. 154-176
Author(s):  
Geoff West ◽  
Mihai Lazarescu ◽  
Monica Ou
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Base ◽  
General Practitioners ◽  
Large Scale ◽  
Case Based Reasoning ◽  
Web Based ◽  
Query Process ◽  
Reasoning System ◽  
Knowledge Validation ◽  
Case Based

In this chapter we describe a web-based decision support system called Telederm that has been developed with the aim of helping general practitioners diagnose skin ailments from a knowledge base while allowing incremental updates of the knowledge base as cases occur. We outline the two major challenges in developing the Telederm system: developing a general practitioner query process that is easily accessible and building knowledge validation in a case-based reasoning system. We provide a detailed description of our approaches to address these problems which involve the use of artificial intelligence classification and reasoning techniques. The system was deployed in a large scale trial in the Eastern Goldfields of Western Australia and we present the results and feedback obtained from an evaluation by the general practitioners involved.

Research on Automatic Construction of Financial Ontology Using Chinese Encyclopedia Resource

2013 ◽  
Vol 765-767 ◽  
pp. 1240-1244
Author(s):  
Qian Mo ◽  
Shu Zhang
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Information Retrieval ◽  
Semantic Web ◽  
Large Scale ◽  
Semantic Information ◽  
Dominant Role ◽  
Experimental Results ◽  
Automatic Construction ◽  
Scale Creation

Ontology plays a dominant role in a growing number of different fields, such as information retrieval, artificial intelligence, semantic Web and knowledge management, etc. However, manual construction of large ontology is not feasible. This article discusses how to create Financial Ontology automatically from a resource of Chinese Encyclopedia. Financial Ontology includes Is-A relationship, Class-Instance relationship, Attribute-of relationship and Synonym relationship. Experimental Results show us that the constructed Financial Ontology has great advantages in the large scale, creation cost and the richness of semantic information.

scholarly journals XLORE2: Large-scale Cross-lingual Knowledge Graph Construction and Application

2019 ◽  
Vol 1 (1) ◽  
pp. 77-98 ◽  
Author(s):  
Hailong Jin ◽  
Chengjiang Li ◽  
Jing Zhang ◽  
Lei Hou ◽  
Juanzi Li ◽  
...  
Keyword(s):  
Large Scale ◽  
Knowledge Bases ◽  
Type Inference ◽  
Knowledge Graph ◽  
Entity Linking ◽  
Fine Grained ◽  
Cross Lingual

Knowledge bases (KBs) are often greatly incomplete, necessitating a demand for KB completion. Although XLORE is an English-Chinese bilingual knowledge graph, there are only 423,974 cross-lingual links between English instances and Chinese instances. We present XLORE2, an extension of the XLORE that is built automatically from Wikipedia, Baidu Baike and Hudong Baike. We add more facts by making cross-lingual knowledge linking, cross-lingual property matching and fine-grained type inference. We also design an entity linking system to demonstrate the effectiveness and broad coverage of XLORE2.

scholarly journals LENA: Locality-Expanded Neural Embedding for Knowledge Base Completion

2019 ◽  
Vol 33 ◽  
pp. 2895-2902
Author(s):  
Fanshuang Kong ◽  
Richong Zhang ◽  
Yongyi Mao ◽  
Ting Deng
Keyword(s):  
Knowledge Base ◽  
Structural Information ◽  
Relevant Information ◽  
Knowledge Bases ◽  
Loss Functions ◽  
Knowledge Graph ◽  
Global Information ◽  
Sufficient Statistic ◽  
Proposed Model ◽  
Significant Research

Embedding based models for knowledge base completion have demonstrated great successes and attracted significant research interest. In this work, we observe that existing embedding models all have their loss functions decomposed into atomic loss functions, each on a triple or an postulated edge in the knowledge graph. Such an approach essentially implies that conditioned on the embeddings of the triple, whether the triple is factual is independent of the structure of the knowledge graph. Although arguably the embeddings of the entities and relation in the triple contain certain structural information of the knowledge base, we believe that the global information contained in the embeddings of the triple can be insufficient and such an assumption is overly optimistic in heterogeneous knowledge bases. Motivated by this understanding, in this work we propose a new embedding model in which we discard the assumption that the embeddings of the entities and relation in a triple is a sufficient statistic for the triple’s factual existence. More specifically, the proposed model assumes that whether a triple is factual depends not only on the embedding of the triple but also on the embeddings of the entities and relations in a larger graph neighbourhood. In this model, attention mechanisms are constructed to select the relevant information in the graph neighbourhood so that irrelevant signals in the neighbourhood are suppressed. Termed locality-expanded neural embedding with attention (LENA), this model is tested on four standard datasets and compared with several stateof-the-art models for knowledge base completion. Extensive experiments suggest that LENA outperforms the existing models in virtually every metric.

scholarly journals Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment

2017 ◽  
Author(s):  
Muhao Chen ◽  
Yingtao Tian ◽  
Mohan Yang ◽  
Carlo Zaniolo
Keyword(s):  
Knowledge Base ◽  
Knowledge Bases ◽  
Loss Functions ◽  
Knowledge Graph ◽  
Graph Embeddings ◽  
Linear Transformations ◽  
Human Labor ◽  
Knowledge Graphs ◽  
Cross Lingual ◽  
Entity Relationships

Many recent works have demonstrated the benefits of knowledge graph embeddings in completing monolingual knowledge graphs. Inasmuch as related knowledge bases are built in several different languages, achieving cross-lingual knowledge alignment will help people in constructing a coherent knowledge base, and assist machines in dealing with different expressions of entity relationships across diverse human languages. Unfortunately, achieving this highly desirable cross-lingual alignment by human labor is very costly and error-prone. Thus, we propose MTransE, a translation-based model for multilingual knowledge graph embeddings, to provide a simple and automated solution. By encoding entities and relations of each language in a separated embedding space, MTransE provides transitions for each embedding vector to its cross-lingual counterparts in other spaces, while preserving the functionalities of monolingual embeddings. We deploy three different techniques to represent cross-lingual transitions, namely axis calibration, translation vectors, and linear transformations, and derive five variants for MTransE using different loss functions. Our models can be trained on partially aligned graphs, where just a small portion of triples are aligned with their cross-lingual counterparts. The experiments on cross-lingual entity matching and triple-wise alignment verification show promising results, with some variants consistently outperforming others on different tasks. We also explore how MTransE preserves the key properties of its monolingual counterpart.

Sign in / Sign up

Export Citation Format

Share Document