Projects

Projects – Software – Datasets

The Hexlite solver is a lightweight alternative to dlvhex for logic programs with a restricted set of external computations.

The solver was created with lightweightness as a principle, using Python as the only programming language and delegating as much as possible to the backend solver which is currently Clingo.

The software is publicly available at github.com/hexhex/hexlite and includes a lightweight Acthex implementation.

The dlvhex Solver is a C++ software for computing answer sets of logic programs with external sources. During my PhD, I performed a rewrite of the software to make it more efficient. After my PhD, Christoph Redl continued to improve the software in his PhD.

The core of dlvhex is available at github.com/hexhex/core.

Several plugins are available at github.com/hexhex/.

For details please see The homepage of dlvhex.

In the Inspire project we studied methods of Answer Set Programming on applications that are related with Interpreting Natural Language.

Key Facts:

Principal Investigator (Project Manager): Peter Schüller
Duration: April 2015 to September 2017
Funding: Scientific and Technological Research Council of Turkey (TÜBİTAK) Program 3501
English Title: Interpreting Natural Language using Answer Set Programming, Inconsistency Management, and Relevance Theory
Turkish Title: Çözüm Kümesi Programlaması, Tutarsızlık Yönetimi, ve Bağıntı Kuramıyla Doğal Dilin Yorumlaması

Project Overview:

Natural language is a very efficient form of communication: humans leave out many details when they use language because other humans can easily fill these details. As an example, ‘morning coffee’ means ‘coffee drunk in the morning’ while ‘morning newspaper’ means ‘newspaper read in the morning’, and humans understand that without effort although neither ‘drunk’ nor ‘read’ is visible in the text. This underspecification, often combined with a high ambiguity of natural language, is a big challenge for NLU systems. The Inspire project aims to advance scientific methods that allow computers to interpret natural language text with the goal of recovering its intended meaning.

An existing approach in that direction is the usage of background knowledge bases (WordNet, FrameNet) together with abductive reasoning. The idea of abduction is to find the best explanation for a given observed text input. In this project we want to build an improved NLU formalism based on Answer Set Programming (ASP).

ASP is a general purpose logic programming formalism that supports comfortable representation of knowledge and nonmonotonic reasoning (such as abduction). In an ASP program we describe a set of potential solutions and relationships/constraints between concepts. Based on such a representation, an ASP solver (a software tool) computes solutions that respect all specified relationships and constraints. ASP can handle circular knowledge without problems and allows for an efficient integration of external knowledge such as WordNet and FrameNet.

The project has been concluded successfully in 2017.

Results:

J  Journal Article  C  Conference Paper  E  Editorship  O  Other  

Publications 2018:

J  and . Best-Effort Inductive Logic Programming via Fine-grained Cost-based Hypothesis Generation. Machine Learning 107 (7), pages 1141-1169, , DOI: 10.1007/s10994-018-5708-2. [  ]

J . Answer Set Programming applied to Coreference Resolution and Semantic Similarity. KI - Künstliche Intelligenz 32 (2), pages 207-208, , DOI: 10.1007/s13218-018-0539-7. [  ]

J . Adjudication of Coreference Annotations via Answer Set Optimization. Journal of Experimental & Theoretical Artificial Intelligence 30 (4), pages 525-546, , DOI: 10.1080/0952813X.2018.1456793. [  ]

Publications 2017:

J , , , and . Constraints, Lazy Constraints, or Propagators in ASP Solving: An Empirical Analysis. Theory and Practice of Logic Programming 17 (5-6), pages 780-799, , Presented at ICLP 2017, DOI: 10.1017/S1471068417000254. [  ] [  ]

C , , , , and . Answer Set Programming with External Sources. In: Reasoning Web International Summer School 10370 LNCS, pages 204-275, , DOI: 10.1007/978-3-319-61033-7_7. [  ]

C . Adjudication of Coreference Annotations via Answer Set Optimization. In: Logic Programming and Nonmonotonic Reasoning (LPNMR), volume 10377 of Lecture Notes in Computer Science, pages 343-357, , Best Application Paper, DOI: 10.1007/978-3-319-61660-5_31.

O . Learning Logic Rules from Text using Statistical Methods for Natural Lan guage Processing. PhD Thesis, Sabanci University, . [  ]

J , , and . Improving Scalability of Inductive Logic Programming via Pruning and Best-Effort Optimisation. Expert Systems With Applications 87, pages 291-303, , DOI: 10.1016/j.eswa.2017.06.013. [  ] [  ]

J , , and . Applications of Non-monotonic Reasoning to Automotive Product Configuration using Answer Set Programming. Journal of Intelligent Manufacturing, , DOI: 10.1007/s10845-017-1333-3. [  ] [  ]

O . Inspire at Inductive Logic Programming Competition: Fine-grained Cost-based Hypothesis Generation. , Short technical note. [  ]

Publications 2016:

J . Modeling Variations of First-Order Horn Abduction in Answer Set Programming. Fundamenta Informaticae 149 (1-2), pages 159-207, , Available as arXiv:1512.08899 [cs.AI], DOI: 10.3233/FI-2016-1446. [  ] [  ]

C , , and . External Propagators in WASP: Preliminary Report. In: International Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion (RCRA), volume 1745 of CEUR Workshop Proceedings, pages 1-9, . [  ]

C , , and . ASP for Abduction in Natural Language Understanding made more efficient using External Propagators. In: Proceedings of the 8th International Workshop on Logic Programming with Constraints for Language Processing (CSLP), pages 19-21, . [  ] [  ]

C  and . Inspire at SemEval 2016 Task 2: Interpretable Semantic Textual Similarity Alignment based on Answer Set Programming. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval 2016), pages 1109-1115, . [  ]

C , , , , and . SteM at SemEval-2016 Task 4A: Applying Active Learning to Improve Sentiment Classification. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval 2016), pages 64-70, . [  ]

Publications 2015:

O . Modeling Variations of First-Order Horn Abduction in Answer Set Programming. Technical Report, Computer Engineering Department, Faculty of Engineering, Marmara University, , arXiv:1512.08899 [cs.AI]. [  ]

C . Modeling Abduction over Acyclic First-Order Logic Horn Theories in Answer Set Programming: Preliminary Experiments. In: International Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion (RCRA), volume 1451 of CEUR Workshop Proceedings, pages 76-90, . [  ]

C  and . Using Semantic Web Resources for Solving Winograd Schemas: Sculptures, Shelves, Envy, and Success. In: Posters&Demos@SEMANTiCS 2015 and DSci15 Workshop, volume 1481 of CEUR Workshop Proceedings, pages 22-25, . [  ]

C , , and . On Structural Analysis of Non-Ground Answer-Set Programs. In: International Conference on Logic Programming (ICLP), Technical Communications, volume 1433 of CEUR Workshop Proceedings, . [  ]

/ Project, Research Project

The Marmara Turkish Coreference Resolution Corpus was created over the course of two years at Marmara University in Istanbul, Turkey.

The corpus is a layer on top of the METU-Sabanci Turkish Treebank.

The corpus is available at bitbucket.org/knowlp/marmara-turkish-coreference-corpus.

The coreference annotation manual used for preparing the corpus is available at bitbucket.org/knowlp/turkish-coreference-annotation-guide.

The CaspR Coreference Resolution Adjudication Tool was used to create this Corpus.

This tool was created to enable building the Marmara Turkish Coreference Corpus, where we we had to adjudicate up to ten independent annotations of the same document into one gold standard.

The tool permits a fully automatic adjudication mode with four possible objective functions (which are described in the accompanying journal article). Moreover, part of the output can be specified by the human adjudicater and the tool will create the best possible solution that fits the specifications of the human, making the tool a semi-automatic support for human adjudication. CaspR runs on the command line, uses the popular CoNLL format for Coreference Annotations, and is based on Answer Set Programming and Python.

The tool is publicly available at github.com/knowlp/caspr-coreference-tool.

The accompanying Coreference Adjudication Benchmark Dataset is available at bitbucket.org/knowlp/asp-coreference-benchmark.

In the OmSieve project we applied methods of Answer Set Programming to Coreference Resolution.

Key Facts:

Principal Investigator (Project Manager): Peter Schüller
Duration: January 2015 to December 2016
Funding: Scientific and Technological Research Council of Turkey (TÜBİTAK) Program 3001
English Title: Open-Minded Coreference Resolution Sieve Based on Answer Set Programming
Turkish Title: Çözüm Kümesi Programlama Tabanlı Muhakeme Edilen Eşgönderge Sieve Çözümlenmesi

Project Overview:

Answer Set Programming (ASP) is a general purpose logic programming formalism that supports comfortable representation of knowledge, non-monotonic reasoning processes, and reasoning with hybrid knowledge bases. In an ASP logic program we describe (i) a set of potential solutions, (ii) relationships between concepts in the solution, and (iii) constraints on solutions. Given such a representation an ASP solver (a software tool) computes those solutions that adhere to the specified relationships and constraints. ASP solvers can find all solutions to such problems and they are engineered to find these solutions efficiently. Moreover ASP supports hybrid reasoning which means that some relationships between concepts can be described outside the ASP logic, for example in a Python program.

Coreference Resolution is the Computer Linguistics task of finding out which phrases of a natural language discourse refer to the same entity in the world. For example in the sentence “He said to the people: ‘I need your help’” the task is to find out that “he” and “I” refers to the same entity (the speaker), furthermore “the people” and “your” refers to the same entity (the listeners). Coreference Resolution is challenging: noun phrases can refer to the same entity for various reasons, they can be synonyms, hypernyms, or hyponyms, or they can be coreferent because of background knowledge and discourse information (e.g., “my brother”, “John”, and “the king” can be coreferent due to contextual information).

The OmSieve project was successfully concluded in early 2017.

Results:

Publications:

J  Journal Article  C  Conference Paper  E  Editorship  O  Other  

Publications 2018:

O , , , , , , and . Marmara Turkish Coreference Corpus and Coreference Resolution Baseline. Technical Report, Marmara University & TU Wien, , Version 2. [  ]

J . Answer Set Programming applied to Coreference Resolution and Semantic Similarity. KI - Künstliche Intelligenz 32 (2), pages 207-208, , DOI: 10.1007/s13218-018-0539-7. [  ]

J . Adjudication of Coreference Annotations via Answer Set Optimization. Journal of Experimental & Theoretical Artificial Intelligence 30 (4), pages 525-546, , DOI: 10.1080/0952813X.2018.1456793. [  ]

Publications 2017:

C . Adjudication of Coreference Annotations via Answer Set Optimization. In: Logic Programming and Nonmonotonic Reasoning (LPNMR), volume 10377 of Lecture Notes in Computer Science, pages 343-357, , Best Application Paper, DOI: 10.1007/978-3-319-61660-5_31.

O . Coreference Resolution Sieve based on Answer Set Programming. Masters Thesis, Marmara University, . [  ]

Publications 2016:

O , , , and . Turkish Coreference Annotation Manual (V2). . [  ]

C . Adjudication of Coreference Annotations via Finding Optimal Repairs of Equivalence Relations. In: International Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion (RCRA), volume 1745 of CEUR Workshop Proceedings, pages 57-71, . [  ]

J , , , , , and . A Model Building Framework for Answer Set Programming with External Computations. Theory and Practice of Logic Programming 16 (04), pages 418-464, , arXiv:1507.01451 [cs.AI], DOI: 10.1017/S1471068415000113. [  ] [  ]

J , , and . A systematic analysis of levels of integration between high-level task planning and low-level feasibility checks. AI Communications 29 (2), pages 319-349, , DOI: 10.3233/AIC-150697. [  ] [  ]

C , , and . Problem Solving Using the HEX Family. In: Computational Models of Rationality - Essays dedicated to Gabriele Kern-Isberner on the occasion of her 60th birthday, pages 150-174, . [  ] [  ]

Publications 2015:

O , , and . Problem Solving Using the HEX Family. Technical Report INFSYS RR-1843-15-07, Institut für Informationssysteme, TU Wien, , Favoritenstraße 9-11, A-1040 Vienna. [  ]

C  and . Answer Set Application Programming: a Case Study on Tetris. In: International Conference on Logic Programming (ICLP), Technical Communications, volume 1433 of CEUR Workshop Proceedings, . [  ] [  ]

O  and . Turkish Coreference Annotation Manual (V1). . [  ]

/ Project, Research Project

This project, which led to several publications, implements abductive reasoning with costs in First Order Horn logic using Answer Set Programming.

The specific knowledge base format and reasoning task that are supported by the framework are those in the ACCEL benchmark (Ng & Mooney, 1992), plus two further abductive cost objective functions from other papers.

Work on this project pioneered an approach for on-demand constraints in Answer Set Solving to manage reasoning in large theories. Follow-up work led to several further projects (in particular the Hexlite Solver can be counted as follow-up work) and collaborations (in particular with Francesco Ricca, Carmine Dodaro, and Bernardo Cuteri).

The tool is available at Bitbucket: bitbucket.org/knowlp/asp-fo-abduction.

AspCcgTk is the “Answer Set Programming Combinatory Categorial Grammar Toolkit”. AspCcgTk is a parser based on Combinatory Categorial Grammar (CGC) developed using the declarative programming paradigm Answer Set Programming.

AspCcgTk implements wide-coverage CCG parsing by utilizing the CCG postagger and supertagger of the C&C tool. Importantly, the tool produces all semantically distinct parse trees for a given sentence.

For details see the dedicated project homepage: AspCcgTk – the Answer Set Programming Combinatory Categorial Grammar Toolkit.

This tool was created as part of a publication at the International Conference on Principles of Knowledge Representation and Reasoning (KR 2014).

The tool is based on Clingo and features Graphviz output.

The detailed description of the tool and the approach can be found on the project homepage: Tackling Winograd Schemas by Formalizing Relevance Theory in Knowledge Graphs.

 

The MCS-IE Example Workbench is a Web Frontend for making lightweight experiments with the Multi-Context Systems Inconsistency Explainer tool. It was created as an adaptation of the ASP Tutoring Web System by Giovambattista Ianni.

The MCS-IE system is a plugin for the dlvhex Solver. MCS-IE allows to explain reasons for inconsistency in Multi-Context Systems (MCS).

For details please see the official homepage of the MCS-IE system.

The system was created during the IncMan Project and is available at github.com/hexhex/mcsieplugin.