Rule-based Reasoning in Semantic Text Analysis Ivan Rygaev Laboratory of Computational Linguistics Kharkevich Institute for Information Transmission Problems RAS, Moscow, Russia RuleML+RR London, July 2017 Ivan Rygaev | RuleML+RR 2017

SemETAP Semantic Text Analyzer Using Winograd Schemas for Evaluation of Implicit Information Extraction Systems SemETAP Semantic Text Analyzer ETAP-3 is a powerful linguistic processor Syntactic parser Machine translation Paraphrasing, UNL etc. SemETAP semantic analyzer is a part of ETAP-3 Translates an original sentence to a language-independent semantic representation in a formal language. Applies logical rules (concept definitions) to infer new knowledge. Ivan Rygaev | RuleML+RR 2017

Ivan Rygaev | RuleML+RR 2017 Using Winograd Schemas for Evaluation of Implicit Information Extraction Systems Text Understanding The ultimate goal to achieve near-human understanding of the text How can we measure understanding? The amount of inferences that can be made out of the text How can we test inferences? Questioning SemETAP is able to answer questions for which there is no direct answer in the original text Ivan Rygaev | RuleML+RR 2017

Ivan Rygaev | RuleML+RR 2017 Using Winograd Schemas for Evaluation of Implicit Information Extraction Systems An example Input sentence: John sold an umbrella to Peter Questions (easy for humans): Who bought the umbrella? (Peter) What did John give to Peter? (the umbrella) What did John get? (money) Who owns the umbrella? (Peter) Where is the knowledge? In the meaning of the words sell, buy, give, get and own. Ivan Rygaev | RuleML+RR 2017

Semantic Analysis Steps Using Winograd Schemas for Evaluation of Implicit Information Extraction Systems Semantic Analysis Steps Syntactic Tree Basic Semantic Structure Words are translated to semantic concepts and syntactic relations to semantic roles (roughly) Enhanced Semantic Structure Concept definitions applied to extend the semantic graph Ivan Rygaev | RuleML+RR 2017

Basic Semantic Structure Using Winograd Schemas for Evaluation of Implicit Information Extraction Systems Basic Semantic Structure hasGivenName (Human_1, "John") hasAgent (Selling_1, Human_1) hasAgent2 (Selling_1, Human_2) hasGivenName (Human_2, "Peter") hasObject (Selling_1, Umbrella_1) Ivan Rygaev | RuleML+RR 2017

Concept Definition Example Using Winograd Schemas for Evaluation of Implicit Information Extraction Systems Concept Definition Example Rule Selling: Selling(?selling) Implication: hasAgent(?selling, ?seller) & Agent(?seller) & hasAgent2(?selling, ?buyer) & Agent(?buyer) & hasObject(?selling, ?thing) & hasPrice(?selling, ?money) & CurrencyMeasure(?money) & Buying(?buying) & hasAgent(?buying, ?buyer) & hasAgent2(?buying, ?seller) & hasObject(?buying, ?thing) & hasPrice(?buying, ?money) Ivan Rygaev | RuleML+RR 2017

Ivan Rygaev | RuleML+RR 2017 Using Winograd Schemas for Evaluation of Implicit Information Extraction Systems Rules Logic Selling is Buying with swapped arguments Buying is Exchange for money Exchange is two mutual Givings Giving implies change of the owner Getting is Giving with slightly different argument roles. Ivan Rygaev | RuleML+RR 2017

Ivan Rygaev | RuleML+RR 2017 Using Winograd Schemas for Evaluation of Implicit Information Extraction Systems Rule Features Conjunctive existential rules compatible with Datalog± Almost necessarily contain new variables in the rule head Applied in the forward chaining manner (chase) A number of techniques to guarantee termination Restricted chase: do not add exactly the same subgraph that already exists Functional relations simplify checking for existence Depth limit: do not apply the rule if no variables from the rule head would map to the original individuals from the text Ivan Rygaev | RuleML+RR 2017

Question Processing Steps Using Winograd Schemas for Evaluation of Implicit Information Extraction Systems Question Processing Steps Basic semantic structure of the question is built Individuals corresponding to wh-words are marked in a special way The semantic structure is used as a pattern for SPARQL query The query is run against the enhanced semantic structure of the text Returned wh-word individuals are displayed to the user Ivan Rygaev | RuleML+RR 2017

Ivan Rygaev | RuleML+RR 2017 Using Winograd Schemas for Evaluation of Implicit Information Extraction Systems Implementation Originally we used Virtuoso DB to store the semantic graphs SPARQL insert queries to apply the rules Easy to write but not efficient Too much overhead in the query execution Almost no control on the query performance optimization Recently we switched to RDFox reasoner (Oxford) Very efficient in-memory RDF storage and reasoner Optimized for chase Meets all the requirements mentioned above Ivan Rygaev | RuleML+RR 2017

Ivan Rygaev | RuleML+RR 2017 Using Winograd Schemas for Evaluation of Implicit Information Extraction Systems Desired Features Probabilistic uncertainty Supported: on the level of individuals – epistemic modality Desired: on the level of propositions/atoms/triples Disjunctive uncertainty Desired: allow disjunction in the rule head Negation Supported: on the level of individuals – Negation concept Universal quantifier Desired: at least in the context of negation Ivan Rygaev | RuleML+RR 2017

Ivan Rygaev | RuleML+RR 2017 Using Winograd Schemas for Evaluation of Implicit Information Extraction Systems Future Work Restricted chase is not enough Depends on the sequence of rule application Sometimes can create duplicates Solution: need to look for a noncontradicting subgraph rather than exactly the same one (coreference problem) Depth limit might be too strong Can potentially eliminate useful inferences Solution: use query rewriting to increase the depth Benchmarking Such rules can be used to test the reasoners performance Ivan Rygaev | RuleML+RR 2017

Ivan Rygaev | RuleML+RR 2017 Using Winograd Schemas for Evaluation of Implicit Information Extraction Systems Thank you! Ivan Rygaev | RuleML+RR 2017