scholarly journals Recent Advances in Querying Probabilistic Knowledge Bases

2018 ◽  
Author(s):  
Stefan Borgwardt ◽  
İsmail İlkan Ceylan ◽  
Thomas Lukasiewicz
Keyword(s):  
Large Scale ◽  
Knowledge Bases ◽  
Closed Domain ◽  
Independence Assumption ◽  
Commonsense Knowledge ◽  
Closed World Assumption ◽  
Recent Advances ◽  
Closed World ◽  
Probabilistic Knowledge ◽  
Computational Properties

We give a survey on recent advances at the forefront of research on probabilistic knowledge bases for representing and querying large-scale automatically extracted data. We concentrate especially on increasing the semantic expressivity of formalisms for representing and querying probabilistic knowledge (i) by giving up the closed-world assumption, (ii) by allowing for commonsense knowledge (and in parallel giving up the tuple-independence assumption), and (iii) by giving up the closed-domain assumption, while preserving some computational properties of query answering in such formalisms.

scholarly journals Open-World Probabilistic Databases: An Abridged Report

2017 ◽  
Author(s):  
Ismail Ilkan Ceylan ◽  
Adnan Darwiche ◽  
Guy Van den Broeck
Keyword(s):  
Large Scale ◽  
Knowledge Bases ◽  
Probabilistic Databases ◽  
Open World ◽  
Probabilistic Database ◽  
Closed World ◽  
Complexity Dichotomy ◽  
Probabilistic Knowledge ◽  
Probability Zero ◽  
Evaluation Algorithm

Large-scale probabilistic knowledge bases are becoming increasingly important in academia and industry alike. They are constantly extended with new data, powered by modern information extraction tools that associate probabilities with database tuples. In this paper, we revisit the semantics underlying such systems. In particular, the closed-world assumption of probabilistic databases, that facts not in the database have probability zero, clearly conflicts with their everyday use. To address this discrepancy, we propose an open-world probabilistic database semantics, which relaxes the probabilities of open facts to default intervals. For this open-world setting, we lift the existing data complexity dichotomy of probabilistic databases, and propose an efficient evaluation algorithm for unions of conjunctive queries. We also show that query evaluation can become harder for non-monotone queries.

scholarly journals TopicKA: Generating Commonsense Knowledge-Aware Dialogue Responses Towards the Recommended Topic Fact

2020 ◽  
Author(s):  
Sixing Wu ◽  
Ying Li ◽  
Dawei Zhang ◽  
Yang Zhou ◽  
Zhonghai Wu
Keyword(s):  
Large Scale ◽  
Sampling Methods ◽  
Knowledge Bases ◽  
Generation Process ◽  
Dialogue Systems ◽  
Semantic Meaning ◽  
Commonsense Knowledge ◽  
Sampling Schemes ◽  
Teacher Student ◽  
Generation Network

Insufficient semantic understanding of dialogue always leads to the appearance of generic responses, in generative dialogue systems. Recently, high-quality knowledge bases have been introduced to enhance dialogue understanding, as well as to reduce the prevalence of boring responses. Although such knowledge-aware approaches have shown tremendous potential, they always utilize the knowledge in a black-box fashion. As a result, the generation process is somewhat uncontrollable, and it is also not interpretable. In this paper, we introduce a topic fact-based commonsense knowledge-aware approach, TopicKA. Different from previous works, TopicKA generates responses conditioned not only on the query message but also on a topic fact with an explicit semantic meaning, which also controls the direction of generation. Topic facts are recommended by a recommendation network trained under the Teacher-Student framework. To integrate the recommendation network and the generation network, this paper designs four schemes, which include two non-sampling schemes and two sampling methods. We collected and constructed a large-scale Chinese commonsense knowledge graph. Experimental results on an open Chinese benchmark dataset indicate that our model outperforms baselines in terms of both the objective and the subjective metrics.

scholarly journals Large-Scale Ontological Reasoning via Datalog

2020 ◽  
Author(s):  
Mario Alviano ◽  
Marco Manna
Keyword(s):  
Large Scale ◽  
Knowledge Bases ◽  
Ontological Reasoning ◽  
Magic Sets ◽  
The One ◽  
Computational Properties

Reasoning over OWL 2 is a very expensive task in general, and therefore the W3C identified tractable profiles exhibiting good computational properties. Ontological reasoning for many fragments of OWL 2 can be reduced to the evaluation of Datalog queries. This paper surveys some of these compilations, and in particular the one addressing queries over Horn-SHIQ knowledge bases and its implementation in DLV2 enanched by a new version of the Magic Sets algorithm.

scholarly journals Closed-World Semantics for Conjunctive Queries with Negation over ELH-bottom Ontologies

2019 ◽  
Author(s):  
Stefan Borgwardt ◽  
Walter Forkel
Keyword(s):  
Polynomial Time ◽  
Description Logic ◽  
Background Knowledge ◽  
Query Answering ◽  
Canonical Model ◽  
Closed Domain ◽  
Data Complexity ◽  
Conjunctive Queries ◽  
Closed World Assumption ◽  
Closed World

Ontology-mediated query answering is a popular paradigm for enriching answers to user queries with background knowledge.  For querying the absence of information, however, there exist only few ontology-based approaches.  Moreover, these proposals conflate the closed-domain and closed-world assumption, and therefore are not suited to deal with the anonymous objects that are common in ontological reasoning. We propose a new closed-world semantics for answering conjunctive queries with negation over ontologies formulated in the description logic ELH-bottom, based on the minimal canonical model.  We propose a rewriting strategy for dealing with negated query atoms, which shows that query answering is possible in polynomial time in data complexity.

scholarly journals On Constrained Open-World Probabilistic Databases

2019 ◽  
Author(s):  
Tal Friedman ◽  
Guy Van den Broeck
Keyword(s):  
Large Class ◽  
Recent Work ◽  
Large Scale ◽  
Knowledge Bases ◽  
Probabilistic Databases ◽  
Open World ◽  
Probabilistic Database ◽  
Probabilistic Knowledge ◽  
Reasonable Answers

Increasing amounts of available data have led to a heightened need for representing large-scale probabilistic knowledge bases. One approach is to use a probabilistic database, a model with strong assumptions that allow for efficiently answering many interesting queries. Recent work on open-world probabilistic databases strengthens the semantics of these probabilistic databases by discarding the assumption that any information not present in the data must be false. While intuitive, these semantics are not sufficiently precise to give reasonable answers to queries. We propose overcoming these issues by using constraints to restrict this open world. We provide an algorithm for one class of queries, and establish a basic hardness result for another. Finally, we propose an efficient and tight approximation for a large class of queries. 

Withdrawn: A New Secret Sharing Scheme of Large-scale Electronic Voting

2019 ◽  
Vol 12 ◽  
Author(s):  
Ting Liu ◽  
Zhe Cui ◽  
Hongquan Pu ◽  
Jintao Rao
Keyword(s):  
Secret Sharing ◽  
Large Scale ◽  
Electronic Voting ◽  
Secret Sharing Scheme ◽  
Technical Errors ◽  
Electronic Engineering ◽  
Sharing Scheme ◽  
Recent Advances ◽  
Copyright Permission ◽  
Legal Right

The article for the journal Recent Advances in Electrical and Electronic Engineering has been withdrawn on the request of the authors due to some technical errors in the article. Bentham Science apologizes to the readers of the journal for any inconvenience this may cause. BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

scholarly journals Evaluation of recent advances in recommender systems on Arabic content

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mehdi Srifi ◽  
Ahmed Oussous ◽  
Ayoub Ait Lahcen ◽  
Salma Mouline
Keyword(s):  
Recommender Systems ◽  
High Performance ◽  
Large Scale ◽  
State Of The Art ◽  
Experimental Results ◽  
Recent Advances ◽  
Research Gap ◽  
Text Preprocessing

AbstractVarious recommender systems (RSs) have been developed over recent years, and many of them have concentrated on English content. Thus, the majority of RSs from the literature were compared on English content. However, the research investigations about RSs when using contents in other languages such as Arabic are minimal. The researchers still neglect the field of Arabic RSs. Therefore, we aim through this study to fill this research gap by leveraging the benefit of recent advances in the English RSs field. Our main goal is to investigate recent RSs in an Arabic context. For that, we firstly selected five state-of-the-art RSs devoted originally to English content, and then we empirically evaluated their performance on Arabic content. As a result of this work, we first build four publicly available large-scale Arabic datasets for recommendation purposes. Second, various text preprocessing techniques have been provided for preparing the constructed datasets. Third, our investigation derived well-argued conclusions about the usage of modern RSs in the Arabic context. The experimental results proved that these systems ensure high performance when applied to Arabic content.

Generalized Closed World Assumption is II02-Complete

1990 ◽  
Vol 34 (6) ◽  
pp. 289-291 ◽  
Author(s):  
Jan Chomicki ◽  
V.S. Subrahmanian
Keyword(s):  
Closed World Assumption ◽  
Closed World

scholarly journals Yield Prediction for Tomato Greenhouse Using EFuNN

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Kefaya Qaddoum ◽  
E. L. Hines ◽  
D. D. Iliescu
Keyword(s):  
Large Scale ◽  
Management Practices ◽  
Intelligent System ◽  
Vapour Pressure Deficit ◽  
Knowledge Bases ◽  
Yield Prediction ◽  
Tomato Yield ◽  
Fuzzy Neural ◽  
Rule Sets ◽  
Human Operators

In the area of greenhouse operation, yield prediction still relies heavily on human expertise. This paper proposes an automatic tomato yield predictor to assist the human operators in anticipating more effectively weekly fluctuations and avoid problems of both overdemand and overproduction if the yield cannot be predicted accurately. The parameters used by the predictor consist of environmental variables inside the greenhouse, namely, temperature, CO2, vapour pressure deficit (VPD), and radiation, as well as past yield. Greenhouse environment data and crop records from a large scale commercial operation, Wight Salads Group (WSG) in the Isle of Wight, United Kingdom, collected during the period 2004 to 2008, were used to model tomato yield using an Intelligent System called “Evolving Fuzzy Neural Network” (EFuNN). Our results show that the EFuNN model predicted weekly fluctuations of the yield with an average accuracy of 90%. The contribution suggests that the multiple EFUNNs can be mapped to respective task-oriented rule-sets giving rise to adaptive knowledge bases that could assist growers in the control of tomato supplies and more generally could inform the decision making concerning overall crop management practices.

Sign in / Sign up

Export Citation Format

Share Document