Skolem preprocessing using WordNet and lexicon in building effective knowledge representation

We are in the information intensive environment in which various forms of digital contents have been growing exponentially. In this era of digital data, knowledge representation has been considered as a crucial component of any information retrieval system. It is also considered as a major problem especially in representing the content of unstructured text in an effective way. Although the mission remains impossible to achieve 100% accuracy, many researchers are indulging themselves in documenting these data in many different techniques so that it can be communicated effectively and easily. Indexing is an important element that determines the success of retrieval. Since we are dealing with multiple documents, preprocessing of data is needed before the data gets indexed. Thus, this paper presents an approach on the preprocessing technique. The semantic data which have been represented in skolem clauses will be preprocessed with the help of automatic lexicon generator output and WordNet. This preprocessing plays an important role in getting rid of redundant data before it gets indexed into the semantic matrix. Besides redundancy, it also helps in dealing with common problem that exists in indexing multiple documents in which similar sentences with more or less the same meaning but have been constructed by using different sets of words. As a conclusion, the integration of WordNet and lexicon leads to better result in terms of building effective knowledge representation

A Survey of Paraphrasing and Textual Entailment Methods

Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.Comment: Technical Report, Natural Language Processing Group, Department of Informatics, Athens University of Economics and Business, Greece, 201

Proceedings of the Conference on Natural Language Processing 2010

This book contains state-of-the-art contributions to the 10th conference on Natural Language Processing, KONVENS 2010 (Konferenz zur Verarbeitung natürlicher Sprache), with a focus on semantic processing. The KONVENS in general aims at offering a broad perspective on current research and developments within the interdisciplinary field of natural language processing. The central theme draws specific attention towards addressing linguistic aspects ofmeaning, covering deep as well as shallow approaches to semantic processing. The contributions address both knowledgebased and data-driven methods for modelling and acquiring semantic information, and discuss the role of semantic information in applications of language technology. The articles demonstrate the importance of semantic processing, and present novel and creative approaches to natural language processing in general. Some contributions put their focus on developing and improving NLP systems for tasks like Named Entity Recognition or Word Sense Disambiguation, or focus on semantic knowledge acquisition and exploitation with respect to collaboratively built ressources, or harvesting semantic information in virtual games. Others are set within the context of real-world applications, such as Authoring Aids, Text Summarisation and Information Retrieval. The collection highlights the importance of semantic processing for different areas and applications in Natural Language Processing, and provides the reader with an overview of current research in this field

Interpretation of Natural-language Robot Instructions: Probabilistic Knowledge Representation, Learning, and Reasoning

A robot that can be simply told in natural language what to do -- this has been one of the ultimate long-standing goals in both Artificial Intelligence and Robotics research. In near-future applications, robotic assistants and companions will have to understand and perform commands such as set the table for dinner'', make pancakes for breakfast'', or cut the pizza into 8 pieces.'' Although such instructions are only vaguely formulated, complex sequences of sophisticated and accurate manipulation activities need to be carried out in order to accomplish the respective tasks. The acquisition of knowledge about how to perform these activities from huge collections of natural-language instructions from the Internet has garnered a lot of attention within the last decade. However, natural language is typically massively unspecific, incomplete, ambiguous and vague and thus requires powerful means for interpretation. This work presents PRAC -- Probabilistic Action Cores -- an interpreter for natural-language instructions which is able to resolve vagueness and ambiguity in natural language and infer missing information pieces that are required to render an instruction executable by a robot. To this end, PRAC formulates the problem of instruction interpretation as a reasoning problem in first-order probabilistic knowledge bases. In particular, the system uses Markov logic networks as a carrier formalism for encoding uncertain knowledge. A novel framework for reasoning about unmodeled symbolic concepts is introduced, which incorporates ontological knowledge from taxonomies and exploits semantically similar relational structures in a domain of discourse. The resulting reasoning framework thus enables more compact representations of knowledge and exhibits strong generalization performance when being learnt from very sparse data. Furthermore, a novel approach for completing directives is presented, which applies semantic analogical reasoning to transfer knowledge collected from thousands of natural-language instruction sheets to new situations. In addition, a cohesive processing pipeline is described that transforms vague and incomplete task formulations into sequences of formally specified robot plans. The system is connected to a plan executive that is able to execute the computed plans in a simulator. Experiments conducted in a publicly accessible, browser-based web interface showcase that PRAC is capable of closing the loop from natural-language instructions to their execution by a robot

Efficient reasoning procedures for complex first-order theories

The complexity of a set of first-order formulas results from the size of the set and the complexity of the problem described by its formulas. Decision Procedures for Ontologies This thesis presents new superposition based decision procedures for large sets of formulas. The sets of formulas may contain expressive constructs like transitivity and equality. The procedures decide the consistency of knowledge bases, called ontologies, that consist of several million formulas and answer complex queries with respect to these ontologies. They are the first superposition based reasoning procedures for ontologies that are at the same time efficient, sound, and complete. The procedures are evaluated using the well-known ontologies YAGO, SUMO and CYC. The results of the experiments, which are presented in this thesis, show that these procedures decide the consistency of all three above-mentioned ontologies and usually answer queries within a few seconds. Reductions for General Automated Theorem Proving Sophisticated reductions are important in order to obtain efficient reasoning procedures for complex, particularly undecidable problems because they restrict the search space of theorem proving procedures. In this thesis, I have developed a new powerful reduction rule. This rule enables superposition based reasoning procedures to find proofs in sets of complex formulas. In addition, it increases the number of problems for which superposition is a decision procedure.Die Komplexität einer Formelmenge für einen automatischen Theorembeweiser in Prädikatenlogik 1. Stufe ergibt sich aus der Anzahl der zu betrachtenden Formeln und aus der Komplexität des durch die Formeln beschriebenen Problems. Entscheidungsprozeduren für Ontologien Diese Arbeit entwickelt effiziente auf Superposition basierende Beweisprozeduren für sehr große entscheidbare Formelmengen, die ausdrucksstarke Konstrukte, wie Transitivität und Gleichheit, enthalten. Die Prozeduren ermöglichen es Wissenssammlungen, sogenannte Ontologien, die aus mehreren Millionen Formeln bestehen, auf Konsistenz hin zu überprüfen und Antworten auf komplizierte Anfragen zu berechnen. Diese Prozeduren sind die ersten auf Superposition basierten Beweisprozeduren für große, ausdrucksstarke Ontologien, die sowohl korrekt und vollständig, als auch effizient sind. Die entwickelten Prozeduren werden anhand der weit bekannten Ontologien YAGO, SUMO und CYC evaluiert. Die Experimente zeigen, dass diese Prozeduren die Konsistenz aller untersuchten Ontologien entscheiden und Anfragen in wenigen Sekunden beantworten. Reduktionen für allgemeines Theorembeweisen Um effiziente Prozeduren für das Beweisen in sehr schwierigen und insbesondere in unentscheidbaren Formelmengen zu erhalten, sind starke Reduktionsregeln, die den Beweisraum einschränken, von essentieller Bedeutung. Diese Arbeit entwickelt eine neue mächtige Reduktionsregel, die es Superposition ermöglicht Beweise in sehr schwierigen Formelmengen zu finden und erweitert die Menge von Problemen, für die Superposition eine Entscheidungsprozedur ist