Influence of Similarity Measures for Rules and Clusters on the Efficiency of Knowledge Mining in Rule-Based Knowledge Bases

2017 ◽  
pp. 67-78 ◽  
Author(s):  
Agnieszka Nowak-Brzezińska ◽  
Tomasz Rybotycki
Keyword(s):  
Similarity Measures ◽  
Knowledge Bases ◽  
Rule Based ◽  
Knowledge Mining

Data mining for building knowledge bases: techniques, architectures and applications

2016 ◽  
Vol 31 (2) ◽  
pp. 97-123 ◽  
Author(s):  
Alfred Krzywicki ◽  
Wayne Wobcke ◽  
Michael Bain ◽  
John Calvo Martinez ◽  
Paul Compton
Keyword(s):  
Data Mining ◽  
Knowledge Base ◽  
Question Answering ◽  
Knowledge Bases ◽  
Event Extraction ◽  
Data Sources ◽  
Small Scale ◽  
Knowledge Mining ◽  
Practical Applications ◽  
Unstructured Text

AbstractData mining techniques for extracting knowledge from text have been applied extensively to applications including question answering, document summarisation, event extraction and trend monitoring. However, current methods have mainly been tested on small-scale customised data sets for specific purposes. The availability of large volumes of data and high-velocity data streams (such as social media feeds) motivates the need to automatically extract knowledge from such data sources and to generalise existing approaches to more practical applications. Recently, several architectures have been proposed for what we callknowledge mining: integrating data mining for knowledge extraction from unstructured text (possibly making use of a knowledge base), and at the same time, consistently incorporating this new information into the knowledge base. After describing a number of existing knowledge mining systems, we review the state-of-the-art literature on both current text mining methods (emphasising stream mining) and techniques for the construction and maintenance of knowledge bases. In particular, we focus on mining entities and relations from unstructured text data sources, entity disambiguation, entity linking and question answering. We conclude by highlighting general trends in knowledge mining research and identifying problems that require further research to enable more extensive use of knowledge bases.

scholarly journals Well-founded operators for normal hybrid MKNF knowledge bases

2017 ◽  
Vol 17 (5-6) ◽  
pp. 889-905
Author(s):  
JIANMIN JI ◽  
FANGFANG LIU ◽  
JIA-HUAI YOU
Keyword(s):  
Artificial Intelligence ◽  
Search Engine ◽  
Knowledge Bases ◽  
Rule Based ◽  
Open World ◽  
Closed World ◽  
Ontology Languages ◽  
Inference Methods ◽  
Partial Partitions ◽  
Normal Hybrid

AbstractHybrid MKNF knowledge bases have been considered one of the dominant approaches to combining open world ontology languages with closed world rule-based languages. Currently, the only known inference methods are based on the approach of guess-and-verify, while most modern SAT/ASP solvers are built under the DPLL architecture. The central impediment here is that it is not clear what constitutes a constraint propagator, a key component employed in any DPLL-based solver. In this paper, we address this problem by formulating the notion of unfounded sets for non-disjunctive hybrid MKNF knowledge bases, based on which we propose and study two new well-founded operators. We show that by employing a well-founded operator as a constraint propagator, a sound and complete DPLL search engine can be readily defined. We compare our approach with the operator based on the alternating fixpoint construction by Knorr et al. (2011. Artificial Intelligence 175, 9, 1528–1554) and show that, when applied to arbitrary partial partitions, the new well-founded operators not only propagate more truth values but also circumvent the non-converging behavior of the latter. In addition, we study the possibility of simplifying a given hybrid MKNF knowledge base by employing a well-founded operator and show that, out of the two operators proposed in this paper, the weaker one can be applied for this purpose and the stronger one cannot. These observations are useful in implementing a grounder for hybrid MKNF knowledge bases, which can be applied before the computation of MKNF models.

scholarly journals RULE RESPONDER: RULE-BASED AGENTS FOR THE SEMANTIC-PRAGMATIC WEB

2011 ◽  
Vol 20 (06) ◽  
pp. 1043-1081 ◽  
Author(s):  
ADRIAN PASCHKE ◽  
HAROLD BOLEY
Keyword(s):  
Distributed System ◽  
Autonomous Agents ◽  
Knowledge Bases ◽  
Virtual Organizations ◽  
Rule Based ◽  
Semantic Domain ◽  
Multi Agent ◽  
Web Infrastructure ◽  
Organizational Semiotics ◽  
Event Based

Rule Responder is a Pragmatic Web infrastructure for distributed rule-based event processing multi-agent eco-systems. This allows specifying virtual organizations — with their shared and individual (semantic and pragmatic) contexts, decisions, and actions/events for rule-based collaboration between the distributed members. The (semi-)autonomous agents use rule engines and Semantic Web rules to describe and execute derivation and reaction logic which declaratively implements the organizational semiotics and the different distributed system/agent topologies with their negotiation/coordination mechanisms. They employ ontologies in their knowledge bases to represent semantic domain vocabularies, normative pragmatics and pragmatic context of event-based conversations and actions.

Exploration of rule-based knowledge bases: A knowledge engineer’s support

2019 ◽  
Vol 485 ◽  
pp. 301-318 ◽  
Author(s):  
Agnieszka Nowak-Brzezińska ◽  
Alicja Wakulicz-Deja
Keyword(s):  
Knowledge Bases ◽  
Rule Based

MEASURING SIMILARITY BETWEEN BIOMEDICAL DATA BY USING FURIA ENSEMBLES RULE-BASED CLASSIFICATION

2020 ◽  
Vol 2 (1) ◽  
pp. 116-127
Author(s):  
Simon Fong
Keyword(s):  
Similarity Measures ◽  
Fuzzy Rule ◽  
Ensemble Methods ◽  
Biomedical Data ◽  
Rule Based ◽  
Reasoning System ◽  
Evaluation Approach ◽  
Common Task ◽  
Test Instance ◽  
The Difference

Similarity measures are essential to solve many pattern recognition problems such as classification, clustering, and retrieval problems. Various distance/similarity measures that is applicable to compare two probability density functions. Data comparison is widely used field in our society nowadays, and it is a very import part. To compare two objects is a common task that people from all walks of life would do. People always want or need to find the similarity between two different objects or the difference between two similar objects. Some different data may share some similarity in some given attribute(s). To compare with two datasets based on attributes by classification algorithms, for the attributes, we need to select them out by rules and the system is known as rule-based reasoning system or expert system which classifies a given test instance into a particular outcome from the learned rules. The test instance carries multiple attributes, which are usually the values of diagnostic tests. In this article, we are proposing a classifier ensemble-based method for comparison of two datasets or one dataset with different features. The ensemble data mining learning methods are applied for rule generation, and a multi-criterion evaluation approach is used for selecting reliable rules over the results of the ensemble methods. The efficacy of the proposed methodology is illustrated via an example of two disease datasets; it is a combined dataset with the same instances and normal attributes but the class in strictly speaking. This article introduces a fuzzy rule-based classification method called FURIA, to get the relationship between two datasets by FURIA rules. And find the similarity between these two datasets.

Principles and practice in verifying rule-based systems

1992 ◽  
Vol 7 (2) ◽  
pp. 115-141 ◽  
Author(s):  
Alun D. Preece ◽  
Rajjan Shinghal ◽  
Aïda Batarekh
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
State Of The Art ◽  
Knowledge Bases ◽  
Order Logic ◽  
First Order Logic ◽  
Rule Based ◽  
First Order ◽  
Rule Bases ◽  
System Knowledge

AbstractThis paper surveys the verification of expert system knowledge bases by detecting anomalies. Such anomalies are highly indicative of errors in the knowledge base. The paper is in two parts. The first part describes four types of anomaly: redundancy, ambivalence, circularity, and deficiency. We consider rule bases which are based on first-order logic, and explain the anomalies in terms of the syntax and semantics of logic. The second part presents a review of five programs which have been built to detect various subsets of the anomalies. The four anomalies provide a framework for comparing the capabilities of the five tools, and we highlight the strengths and weaknesses of each approach. This paper therefore provides not only a set of underlying principles for performing knowledge base verification through anomaly detection, but also a survey of the state-of-the-art in building practical tools for carrying out such verification. The reader of this paper is expected to be familiar with first-order logic.

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