Relational Knowledge Extraction from Attribute-Value Learners

نویسندگان

  • Manoel V. M. França
  • Artur S. d'Avila Garcez
  • Gerson Zaverucha
چکیده

Bottom Clause Propositionalization (BCP) is a recent propositionalization method which allows fast relational learning. Propositional learners can use BCP to obtain accuracy results comparable with Inductive Logic Programming (ILP) learners. However, differently from ILP learners, what has been learned cannot normally be represented in first-order logic. In this paper, we propose an approach and introduce a novel algorithm for extraction of first-order rules from propositional rule learners, when dealing with data propositionalized with BCP. A theorem then shows that the extracted first-order rules are consistent with their propositional version. The algorithm was evaluated using the rule learner RIPPER, although it can be applied on any propositional rule learner. Initial results show that the accuracies of both RIPPER and the extracted first-order rules can be comparable to those obtained by Aleph (a traditional ILP system), but our approach is considerably faster (obtaining speed-ups of over an order of magnitude), generating a compact rule set with at least the same representation power as standard ILP learners. 1998 ACM Subject Classification I.2.3 Deduction and Theorem Proving

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

How to Upgrade Propositional Learners to First Order Logic: A Case Study

We describe a methodology for upgrading existing attribute value learners towards rst order logic. This method has several advantages: one can proot from existing research on propositional learners (and inherit its eeciency and eeectiveness), relational learners (and inherit its expressiveness) and PAC-learning (and inherit its theoretical basis). Moreover there is a clear relationship between ...

متن کامل

Attribute Relation Extraction from Template-inconsistent Semi-structured Text by Leveraging Site-level Knowledge

A variety of methods have been proposed for attribute-value extraction from semistructured text with consistent templates (strict semi-text). However, when the templates in semi-structured text are inconsistent (weak semi-text), these methods will work poorly. To overcome the templateinconsistent problem, in this paper, we proposed a novel method to leverage sitelevel knowledge for attribute-va...

متن کامل

Attribute-oriented Induction in Ob Ject-oriented Databases

Knowledge discovery in databases is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data such that the extracted knowledge may facilitate deductive reasoning and query processing in database systems. This branch of study has been ranked among the most promising topics for database research for the 1990s. Due to the dominating influence of relat...

متن کامل

RMonto: Ontological Extension to RapidMiner

We present RMonto, an ontological extension to RapidMiner, that provides possibility of machine learning with formal ontologies. RMonto is an easily extendable framework, currently providing support for unsupervised clustering with kernel methods and (frequent) pattern mining in knowledge bases. One important feature of RMonto is that it enables working directly on structured, relational data. ...

متن کامل

Exploration of the Power of Attribute-oriented Induction in Data Mining

Attribute-oriented induction is a set-oriented database mining method which generalizes the task-relevant subset of data attribute-by-attribute, compresses it into a generalized relation, and extracts from it the general features of data. In this chapter, the power of attribute-oriented induction is explored for the extraction from relational databases of diierent kinds of patterns, including c...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2013