RORS: Enhanced Rule-based OWL Reasoning on Spark

The rule-based OWL reasoning is to compute the deductive closure of an ontology by applying RDF/RDFS and OWL entailment rules. The performance of the rule-based OWL reasoning is often sensitive to the rule execution order. In this paper, we present an approach to enhancing the performance of the rule-based OWL reasoning on Spark based on a locally optimal executable strategy. Firstly, we divide all rules (27 in total) into four main classes, namely, SPO rules (5 rules), type rules (7 rules), sameAs rules (7 rules), and schema rules (8 rules) since, as we investigated, those triples corresponding to the first three classes of rules are overwhelming (e.g., over 99% in the LUBM dataset) in our practical world. Secondly, based on the interdependence among those entailment rules in each class, we pick out an optimal rule executable order of each class and then combine them into a new rule execution order of all rules. Finally, we implement the new rule execution order on Spark in a prototype called RORS. The experimental results show that the running time of RORS is improved by about 30% as compared to Kim & Park's algorithm (2015) using the LUBM200 (27.6 million triples).Comment: 12 page

Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning

Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings can be used for knowledge graph reasoning and they have their own advantages and difficulties. Rule-based reasoning is accurate and explainable but rule learning with searching over the graph always suffers from efficiency due to huge search space. Embedding-based reasoning is more scalable and efficient as the reasoning is conducted via computation between embeddings, but it has difficulty learning good representations for sparse entities because a good embedding relies heavily on data richness. Based on this observation, in this paper we explore how embedding and rule learning can be combined together and complement each other's difficulties with their advantages. We propose a novel framework IterE iteratively learning embeddings and rules, in which rules are learned from embeddings with proper pruning strategy and embeddings are learned from existing triples and new triples inferred by rules. Evaluations on embedding qualities of IterE show that rules help improve the quality of sparse entity embeddings and their link prediction results. We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently. Experiments show that iteratively learning embeddings and rules benefit each other during learning and prediction.Comment: This paper is accepted by WWW'1

Knowledge Graph Embedding with Iterative Guidance from Soft Rules

Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Combining such an embedding model with logic rules has recently attracted increasing attention. Most previous attempts made a one-time injection of logic rules, ignoring the interactive nature between embedding learning and logical inference. And they focused only on hard rules, which always hold with no exception and usually require extensive manual effort to create or validate. In this paper, we propose Rule-Guided Embedding (RUGE), a novel paradigm of KG embedding with iterative guidance from soft rules. RUGE enables an embedding model to learn simultaneously from 1) labeled triples that have been directly observed in a given KG, 2) unlabeled triples whose labels are going to be predicted iteratively, and 3) soft rules with various confidence levels extracted automatically from the KG. In the learning process, RUGE iteratively queries rules to obtain soft labels for unlabeled triples, and integrates such newly labeled triples to update the embedding model. Through this iterative procedure, knowledge embodied in logic rules may be better transferred into the learned embeddings. We evaluate RUGE in link prediction on Freebase and YAGO. Experimental results show that: 1) with rule knowledge injected iteratively, RUGE achieves significant and consistent improvements over state-of-the-art baselines; and 2) despite their uncertainties, automatically extracted soft rules are highly beneficial to KG embedding, even those with moderate confidence levels. The code and data used for this paper can be obtained from https://github.com/iieir-km/RUGE.Comment: To appear in AAAI 201

Free Triples, Large Indifference Classes and the Majority Rule

We consider situations in which agents are not able to completely distinguish between all alternatives. Preferences respect individual objective indifferences if any two alternatives are indifferent whenever an agent cannot distinguish between them. We present necessary and sufficient conditions of such a domain of preferences under which majority rule is quasi-transitive and thus Condorcet winners exist for any set of alternatives. Finally, we compare our proposed restrictions with others in the literature, to conclude that they are independent of any previously discussed domain restriction.Quasi-Transitivity

Integrated Expert System Development Environment With an Automatic Verification Feature: An Application To Grinding Process

The concerns of this Master degree project are the implementations of an expert system development environment and an application to manufacturing. The environment is given the name of IDEA (Integrated expert system Development Environment with Automatic verification feature), and it integrates an expert system shell with a rule base verification facility and a rule base builder into a single program. The interface of IDEA has the look of a general purpose computer language. The knowledge bases referenced by IDEA consist of LF-THEN rules with the syntax of Object-Attribute-Value (O-A-V) triples and confidence factors. An application is developed for grinding chattering diagnosis problems using the IDEA. The verification facility of IDEA realizes the main functions of CHECK, a verification tool, developed by T. Nguyen et al, 1987, so that it can identify some major problems of inconsistency and incompleteness. Different from CHECK, IDEA uses a unique approach based on rule tree algorithm to check for some logic problems in a rule base. IDEA also has the ability to draw the rule tree on a computer screen. This report discusses the problems of inconsistency and incompleteness in detail, including the influence of introducing confidence factors. Some problems identified in this report are not reported before. The rule base builder of IDEA uses some techniques that can greatly reduce errors in a rule base during its input phase and greatly speed up the input work. First, the builder uses templates for rule input to eliminate the possible syntax errors of rules. Second, it uses an O-AV file for declared O-A-V triples. By checking the O-A-V file and the O-A-V triples used in the rule base, the problems of illegal O-A-V triples and unreferenced O-A-V triples can be identified. Finally, it has the ability to automatically generate the ask-part of the rule base, so that no errors will exists in the ask-part and the input work can be efficient. Grinding chattering is a kind of abnormal phenomenon in a grinding process, which greatly degrades the surface quality of the workpiece ground. The grinding chattering diagnosis rule base gives complete answers for the causes of chattering for external cylindrical grinding. This report describes the rule base development work and its related grinding knowledge

Training People to Think in Opposites Facilitates the Falsification Process in Wason’s Rule Discovery Task

With reference to Wason’s 2-4-6 rule discovery task, this study investigated the effects of a simple training session that prompted participants to “think in opposites”. The results showed a significant improvement in performance under the training condition when compared to the control condition, both in terms of the proportion of participants who discovered the correct rule and how quickly it was discovered. An analysis of whether or not participant submitted test triples formed of descending numbers showed that fewer participants under the control condition considered ascending/descending to represent a critical dimension and, in any case, this occurred later (that is, after more test triples) than in the training condition. These results are discussed in relation to previous literature showing improvements in performance that were prompted by strategies involving “contrast” as a critical factor. The limitations of the study are discussed, as well as the benefits of a training program like this, which is non-content related

Hypothesis Falsification in the 2-4-6 Number Sequence Test: Introducing Imaginary Counterparts

Two main cognitive theories predict that people find refuting evidence that falsifies their theorising difficult, if not impossible to consider, even though such reasoning may be pivotal to grounding their everyday thoughts in reality (i.e., Poletiek, 1996; Klayman & Ha, 1987). In the classic 2-4-6 number sequence task devised by psychologists to test such reasoning skills in a simulated environment – people fail the test more often than not. In the 2-4-6 task participants try to discover what rule the number triple 2-4-6 conforms to. The rule is ‘ascending numbers’, but it is tricky to discover this rule. Participants tend to generate hypotheses with the properties of the 2-4-6 triple, for example, ‘even numbers ascending in twos’. They must search for evidence to test whether their hypothesis is the rule. But experimental evidence has shown that they tend to generate confirming triples that they expect to be consistent with their hypothesis rather than inconsistent falsifying triples. Counter to the two main hypothesis testing theories this paper demonstrates that falsification is possible in five 2-4-6 task experiments when participants consider an Imaginary Participant’s hypothesis. Experiment 1 and 2 show that competition with an opponent hypothesis tester facilitates falsification. Experiments 3 to 5 show that the consideration of an alternative hypothesis helps this falsification of hypotheses lead to rule discovery. The implications of the results for theories of hypothesis testing and reasoning are discussed