computer science

Rulelog

Rulelog is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Rulelog rather than just read about it. In short: Rulelog is an expressive semantic rule-based knowledge representation and reasoning (KRR) language. It underlies knowledge representation languages used in systems such as Flora-2, SILK and others.

Key takeaways

Rulelog belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Rulelog to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Rulelog from memory before moving on to harder problems.

Reference excerpt

Rulelog is an expressive semantic rule-based knowledge representation and reasoning (KRR) language. It underlies knowledge representation languages used in systems such as Flora-2, SILK and others. It extends well-founded declarative logic programs with features for higher-order syntax, frame syntax, defeasibility, general quantified expressions both in the bodies of the rules and their heads, user-defined functions, and restraint bounded rationality.

Features Rulelog extends well-founded semantics for declarative logic rules with features for higher-order syntax (HiLog), frame syntax (cf. F-Logic), defeasibility (prioritized defaults), general formulas (including existentials and disjunctions in rule heads), user-defined functions, and restraint bounded rationality. Overall, Rulelog combines deep logical/probabilistic reasoning with natural language processing (NLP), and complements machine learning (ML). Rulelog interoperates and composes well with graph databases, relational databases, spreadsheets, XML, RDF/OWL, and can orchestrate overall hybrid KRR. Despite its expressibility, Rulelog is computationally affordable (inferencing is worst-case polynomial time when radial restraint is employed). The more capable and efficient implementations of Rulelog, such as Ergo, Flora-2, and Ontobroker leverage methods from Logic programming, Non-monotonic reasoning, Business rules, the Semantic Web, and Databases. Rulelog implementation methods (in systems like Ergo, Flora-2 and some others) include dependency-aware smart caching of reasoning results (memoization, also known as tabling in logic programming), indexing, and goal reordering (for improving the performance of joins).

History Rulelog builds on decades of work in Logic Programming and Deductive database research; it combines several different extensions of declarative logic programs whose language and implementations were originally developed by a number of different researchers since 1990's. Many of Rulelog's features derive from earlier systems, including Flora-2, SweetRules, XSB, SWSL, and others.

Standardization Efforts There was a number of standardization efforts for precursors of Rulelog:

Semantic Web Services Language was submitted as a member submission to W3C in April, 2005. RIF Framework for Logic Dialects (RIF-FLD) is a W3C recommendation, which is intended as a means for formal specification of Web logic languages such as Rulelog. Rulelog: Syntax and Semantics Archived 2018-12-19 at the Wayback Machine. A version of the Rulelog specification using the RIF-FLD framework is standardized by RuleML. The source files for that specification are found here.

Systems Implementing Rulelog Flora-2: an open source rule-based system for knowledge representation and reasoning. ErgoAI: an implementation of Rulelog by Coherent Knowledge, which includes an IDE and many extensions. This was originally commercial, but has now been available open source (Apache license). Sunflower: an integrated development environment for Flora-2. SILK: a precursor to Ergo. Ontobroker: a commercial implementation of a subset of Rulelog, which is largely based on F-logic with various extensions. XSB: supports a smaller subset of Rulelog's features, but a number of other systems, like Flora-2 and Ergo, are based on XSB. Open source.

See also RuleML Flora-2 Ontology (computer science) Semantic Web Rule Language

References

Worked examples

Example 1 — a first encounter with Rulelog

Start with the simplest possible case. Write down what Rulelog claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Rulelog before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Rulelog ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Rulelog

In research: Rulelog appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Rulelog in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Rulelog is common in secondary-school and first-year university syllabi. It links to neighbouring topics Declarative programming languages, Knowledge representation software, so understanding it makes those chapters shorter.
In everyday life: Look for Rulelog outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Rulelog in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Rulelog means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Rulelog out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Rulelog in simple terms?

Rulelog is an expressive semantic rule-based knowledge representation and reasoning (KRR) language. It underlies knowledge representation languages used in systems such as Flora-2, SILK and others.

Why does Rulelog matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Rulelog?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Rulelog.