A different perspective on canonicity

One of the most interesting aspects of Conceptual Structures Theory is the notion of canonicity. It is also one of the most neglected: Sowa seems to have abandoned it in the new version of the theory, and most of what has been written on canonicity focuses on the generalization hierarchy of conceptual graphs induced by the canonical formation rules. Although there is a common intuition that a graph is canonical if it is "meaningful'', the original theory is somewhat unclear about what that actually means, in particular how canonicity is related to logic. This paper argues that canonicity should be kept a first-class notion of Conceptual Structures Theory, provides a detailed analysis of work done so far, and proposes new definitions of the conformity relation and the canonical formation rules that allow a clear separation between canonicity and truth

Conceptual graphs and first-order logic

Conceptual Structures (CS) Theory is a logic-based knowledge representation formalism. To show that conceptual graphs have the power of first-order logic, it is necessary to have a mapping between both formalisms. A proof system, i.e. axioms and inference rules, for conceptual graphs is also useful. It must be sound (no false statement is derived from a true one) and complete (all possible tautologies can be derived from the axioms). This paper shows that Sowa's original definition of the mapping is incomplete, incorrect, inconsistent, and unintuitive, and the proof system is incomplete too. To overcome these problems a new translation algorithm is given and a complete proof system is presented. Furthermore, the framework is extended for higher-order types

Information extraction

In this paper we present a new approach to extract relevant information by knowledge graphs from natural language text. We give a multiple level model based on knowledge graphs for describing template information, and investigate the concept of partial structural parsing. Moreover, we point out that expansion of concepts plays an important role in thinking, so we study the expansion of knowledge graphs to use context information for reasoning and merging of templates

Goal driven theorem proving using conceptual graphs and Peirce logic

The thesis describes a rational reconstruction of Sowa's theory of Conceptual Graphs. The reconstruction produces a theory with a firmer logical foundation than was previously the case and which is suitable for computation whilst retaining the expressiveness of the original theory. Also, several areas of incompleteness are addressed. These mainly concern the scope of operations on conceptual graphs of different types but include extensions for logics of higher orders than first order. An important innovation is the placing of negation onto a sound representational basis. A comparison of theorem proving techniques is made from which the principles of theorem proving in Peirce logic are identified. As a result, a set of derived inference rules, suitable for a goal driven approach to theorem proving, is developed from Peirce's beta rules. These derived rules, the first of their kind for Peirce logic and conceptual graphs, allow the development of a novel theorem proving approach which has some similarities to a combined semantic tableau and resolution methodology. With this methodology it is shown that a logically complete yet tractable system is possible. An important result is the identification of domain independent heuristics which follow directly from the methodology. In addition to the theorem prover, an efficient system for the detection of selectional constraint violations is developed. The proof techniques are used to build a working knowledge base system in Prolog which can accept arbitrary statements represented by conceptual graphs and test their semantic and logical consistency against a dynamic knowledge base. The same proof techniques are used to find solutions to arbitrary queries. Since the system is logically complete it can maintain the integrity of its knowledge base and answer queries in a fully automated manner. Thus the system is completely declarative and does not require any programming whatever by a user with the result that all interaction with a user is conversational. Finally, the system is compared with other theorem proving systems which are based upon Conceptual Graphs and conclusions about the effectiveness of the methodology are drawn

An X-Windows Toolkit for knowledge acquisition and representation based on conceptual structures

This paper describes GET (Graph Editor and Tools), a tool based on Sowa's conceptual structures, which can be used for generic knowledge acquisition and representation. The system enabled the acquisition of semantic information (restrictions) for a lexicon used by a semantic interpreter for Portuguese sentences featuring some deduction capabilities. GET also enables the graphical representation of conceptual relations by incorporating an X-Windows based editor

From Information Overload to Knowledge Graphs: An Automatic Information Process Model

Continuously increasing text data such as news, articles, and scientific papers from the Internet have caused the information overload problem. Collecting valuable information as well as coding the information efficiently from enormous amounts of unstructured textual information becomes a big challenge in the information explosion age. Although many solutions and methods have been developed to reduce information overload, such as the deduction of duplicated information, the adoption of personal information management strategies, and so on, most of the existing methods only partially solve the problem. What’s more, many existing solutions are out of date and not compatible with the rapid development of new modern technology techniques. Thus, an effective and efficient approach with new modern IT (Information Technology) techniques that can collect valuable information and extract high-quality information has become urgent and critical for many researchers in the information overload age. Based on the principles of Design Science Theory, the paper presents a novel approach to tackle information overload issues. The proposed solution is an automated information process model that employs advanced IT techniques such as web scraping, natural language processing, and knowledge graphs. The model can automatically process the full cycle of information flow, from information Search to information Collection, Information Extraction, and Information Visualization, making it a comprehensive and intelligent information process tool. The paper presents the model capability to gather critical information and convert unstructured text data into a structured data model with greater efficiency and effectiveness. In addition, the paper presents multiple use cases to validate the feasibility and practicality of the model. Furthermore, the paper also performed both quantitative and qualitative evaluation processes to assess its effectiveness. The results indicate that the proposed model significantly reduces the information overload and is valuable for both academic and real-world research

Entity-centric knowledge discovery for idiosyncratic domains

Technical and scientific knowledge is produced at an ever-accelerating pace, leading to increasing issues when trying to automatically organize or process it, e.g., when searching for relevant prior work. Knowledge can today be produced both in unstructured (plain text) and structured (metadata or linked data) forms. However, unstructured content is still themost dominant formused to represent scientific knowledge. In order to facilitate the extraction and discovery of relevant content, new automated and scalable methods for processing, structuring and organizing scientific knowledge are called for. In this context, a number of applications are emerging, ranging fromNamed Entity Recognition (NER) and Entity Linking tools for scientific papers to specific platforms leveraging information extraction techniques to organize scientific knowledge. In this thesis, we tackle the tasks of Entity Recognition, Disambiguation and Linking in idiosyncratic domains with an emphasis on scientific literature. Furthermore, we study the related task of co-reference resolution with a specific focus on named entities. We start by exploring Named Entity Recognition, a task that aims to identify the boundaries of named entities in textual contents. We propose a newmethod to generate candidate named entities based on n-gram collocation statistics and design several entity recognition features to further classify them. In addition, we show how the use of external knowledge bases (either domain-specific like DBLP or generic like DBPedia) can be leveraged to improve the effectiveness of NER for idiosyncratic domains. Subsequently, we move to Entity Disambiguation, which is typically performed after entity recognition in order to link an entity to a knowledge base. We propose novel semi-supervised methods for word disambiguation leveraging the structure of a community-based ontology of scientific concepts. Our approach exploits the graph structure that connects different terms and their definitions to automatically identify the correct sense that was originally picked by the authors of a scientific publication. We then turn to co-reference resolution, a task aiming at identifying entities that appear using various forms throughout the text. We propose an approach to type entities leveraging an inverted index built on top of a knowledge base, and to subsequently re-assign entities based on the semantic relatedness of the introduced types. Finally, we describe an application which goal is to help researchers discover and manage scientific publications. We focus on the problem of selecting relevant tags to organize collections of research papers in that context. We experimentally demonstrate that the use of a community-authored ontology together with information about the position of the concepts in the documents allows to significantly increase the precision of tag selection over standard methods

A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail