Comparison of inference engine Jess, racer, And Jena

Jess (Java expert shell System) is a clisp inference engine based on the Java language.
Clisp is a production-based forward inference engine. It is a very basic thing. Many upper-layer reasoning tasks must be mapped to this inference engine for running.
In principle, clisp (or Jess) can handle reasoning tasks in various fields, as long as the system can provide clisp with domain-specific rules (in the form of production) and fact information in this field
(Assertion
Format ). As a type of application, ontology reasoning can also be mapped to clisp reasoning machines for reasoning, but relevant rules in the ontology field must be provided for the inference engine. For example
Clisp (Jess)
For owl reasoning, it is necessary to input the relevant rules of the meaning of each language component of owl into the system in the form of generative rules. With these rules, you can use clisp to give
The ontology is deduced. Similarly, if we perform Inference On the ontology given in the DAML form, we need to provide relevant rules for the meaning of each language component of DAML in clisp.
The advantage of Jess (clisp) is that the inference engine is open. Users can provide different rule systems to perform reasoning in different fields. Users can expand the inference capabilities of the inference engine.
However, as a forward inference system, Jess uses space for time, which produces a large amount of intermediate data and has low space efficiency. Meanwhile, Because Jess (clisp) is a general inference engine, no needle available
The ability to optimize various fields makes it difficult to optimize the efficiency of such reasoning mechanisms.

Currently, Ontology-based reasoning is increasingly concentrated on several standard ontology languages, such as owl,
DAML, RDFS/RDF, and so on. In this way, the researchers provide more efficient inference engines for these specific applications. Such as racer, fact, pellet, etc.
Inference Engine. Their advantages are high efficiency and convenient to use. They are not general inference engines, and their reasoning capabilities are limited to several specific ontology languages, in addition, it is difficult for users to expand it.
It is worth noting that racer, fact, and pellet use the descriptive logic as the theoretical basis,AlgorithmThe tableau algorithm is used. These systems have done a lot of optimization work.

Jena is an application development kit for semantic web. It contains comprehensive content, and the inference engine is only part of it.
The inference engine provided by Jena is similar to that provided by racer, fact, and pellet. It is an Ontology-based inference engine. However, Jena is not a 'inference engine design expert ', and it includes the inference engine.
Basically, it is a forward inference system that clisp works with the production rules of the ontology domain. Therefore, it is not very efficient. Fortunately, there is a dig interface,
Dig is a bit like ODBC in a database. It allows the front end to be mounted to different reasoning engines in the background. In this way, in Jean, you can also use racer, fact, and pellet to be more professional.
Some inference engines.

To sum up, we can make the following distinction: 1. the inference engine and racer in Jena are inference engines for specific ontology languages, which are highly targeted and efficient. 2. Jess is a common clisp inference engine with lower layers.

This can be explained: when a user uses owl to represent the ontology, protege uses Jena or racer reasoning, and when the user uses class/slot to represent the Ontology
Protege can only use Jess. In fact, in terms of capabilities, Jess can also complete owl Reasoning (the corresponding generative rule repository needs to be input), but the efficiency is low.