Michael DeBellis - Academia.edu (original) (raw)

Papers by Michael DeBellis

Frontiers in artificial intelligence and applications, Dec 20, 2023

The DrMO ontology is a domain ontology that represents knowledge underlying the composition, char... more The DrMO ontology is a domain ontology that represents knowledge underlying the composition, characterization and standardization of different materials involved in the dental restoration procedure. It will assist dentists in selecting appropriate materials based on up-to-date scientific knowledge to satisfy a patient's specific requirements, without jeopardizing their clinical time. It reuses several ontologies from the OBO foundry, especially the Oral Health and Disease (OHD) Ontology. However, the dental restoration domain is complex and also requires concepts from materials science and engineering. Thus, DrMO also incorporates knowledge from the Devices, Experimental scaffolds, and Biomaterials (DEB) and Functionally Graded Materials (FGM) ontologies to provide more comprehensive knowledge of this area of dental material than previous ontologies. However, much of the terminology from FGM is different than that used in clinical dentistry. Thus, DrMO has changed the appropriate classes to make them consistent with terminology common in dentistry. DrMO also follows ontology design best practices by reusing meta-data properties from the Dublin Core vocabulary. It captures knowledge from a set of the most recent and influential papers in Dental Materials and related fields. Links to these papers are included in the ontology as meta-data defined with Dublin Core. It is implemented in OWL2 and was developed with the Protégé 5.6 ontology editor. The ontology was created using the Ontology Development 101 methodology by Noy et. al. Several domain experts in addition to Dr. Dutta also provided their expertise. The ontology is available on GitHub and licensed via an open source license. The GitHub project includes a corresponding file of SPARQL queries that answer the competency questions defined as part of the ontology development methodology.

India’s legions of hard-working non-profit organizations strive to reduce absolute and relative p... more India’s legions of hard-working non-profit organizations strive to reduce absolute and relative poverty and improve the capabilities of communities and individuals but struggle for resources. With the introduction of a legal mandate in a 2013 revision to her Companies Act, India became the first country to mandate Corporate Social Responsibility (CSR) expenditure by large firms. Both corporations and Non-Governmental Organizations (NGOs) still struggle to meet the act’s requirements. CSR funding is unevenly distributed or unspent, neglecting many issues and regions in need. In 2021, 45% of mandated corporations remained non-compliant and less than 1% of India’s NGOs had received funding through the CSR mandate. Small, local NGOs are often extremely effective in terms of impact and outcome. They have the connections, local knowledge and agility to generate and implement sustainable solutions but are excluded from consideration if they struggle to meet transparency requirements. The goal of DaanMatch is to use technology to overcome these and other challenges in development. We are developing a system to facilitate localization of the United Nations Sustainable Development Goals (SDGs) and improve outcomes for Corporate Social Responsibility. This paper describes the DaanMatch system and the innovative technology it utilizes to match CSR with NGOs in a way that showcases small NGOs and helps level the playing field for them. Although the initial emphasis is on India, our goal is to reinvent the process of NGO evaluation, monitoring, auditing, and reporting for charitable giving and global development.

Applied Ontology, 2024

Large Language Models have captured the imagination of the public and the technical community. As... more Large Language Models have captured the imagination of the public and the technical community. As powerful as they are they have problems that prohibit their use for highly skilled users. These issues are hallucinations, bias, black-box reasoning, and lack of domain depth. One of the most popular architectures to alleviate these problems is Retrieval Augmented Generation (RAG). In a RAG architecture the LLM is utilized to generate vectors and to parse and generate natural language. The knowledge base for a RAG architecture is typically a set of documents focused on a particular type of vertical (question answering) or horizontal (domain) set of use cases as opposed to the general knowledge base of an LLM. Typically, the corpus for the RAG knowledge base is stored in a relational database. This project investigates the use of an ontology and knowledge graph to form a domain specific knowledge base for RAG in order to leverage LLMs for specific domains without the four problems that typically make them inappropriate for mission and life critical domains. The domain is support of dental clinicians in India who face specific problems that can be significantly improved by better, timely, and easily accessible access to the latest knowledge on dental material products. We demonstrate that using an ontology and knowledge graph to implement RAG has several benefits such as rapid agile development and retrieval by reformulation browsing.

The Covid-19 pandemic created many opportunities for semantic data science. There was a deluge of... more The Covid-19 pandemic created many opportunities for semantic data science. There was a deluge of information about the spread of the virus. In addition, there were many opportunities to use semantic technology to analyze the virus and investigate potential treatments. The semantic data science community stepped up to this challenge and many researchers ignored bureaucratic boundaries and volunteered their time to rapidly collaborate and develop systems to combat the pandemic. This paper is an exploratory survey of these systems. Our emphasis was on systems that incorporated real world data and were utilized by actual users. We first describe our methodology for the survey. We then describe the various domains where semantic technology was applied and some of the most impressive systems developed in each domain. Finally, we conclude with some tentative conclusions for future research based on our survey.

International Journal of Web Information Systems, Oct 5, 2022

Purpose The purpose of this paper is to describe the CODO ontology (COviD-19 Ontology) that captu... more Purpose The purpose of this paper is to describe the CODO ontology (COviD-19 Ontology) that captures epidemiological data about the COVID-19 pandemic in a knowledge graph that follows the FAIR principles. This study took information from spreadsheets and integrated it into a knowledge graph that could be queried with SPARQL and visualized with the Gruff tool in AllegroGraph. Design/methodology/approach The knowledge graph was designed with the Web Ontology Language. The methodology was a hybrid approach integrating the YAMO methodology for ontology design and Agile methods to define iterations and approach to requirements, testing and implementation. Findings The hybrid approach demonstrated that Agile can bring the same benefits to knowledge graph projects as it has to other projects. The two-person team went from an ontology to a large knowledge graph with approximately 5 M triples in a few months. The authors gathered useful real-world experience on how to most effectively transform “from strings to things.” Originality/value This study is the only FAIR model (to the best of the authors’ knowledge) to address epidemiology data for the COVID-19 pandemic. It also brought to light several practical issues that generalize to other studies wishing to go from an ontology to a large knowledge graph. This study is one of the first studies to document how the Agile approach can be used for knowledge graph development.

The COviD-19 Ontology for cases and patient information (CODO) provides a model for the collectio... more The COviD-19 Ontology for cases and patient information (CODO) provides a model for the collection and analysis of data about the COVID-19 pandemic. The ontology provides a standards-based open source model that facilitates the integration of data from heterogenous data sources. The ontology was designed by analysing disparate COVID-19 data sources such as datasets, literature, services, etc. The ontology follows the best practices for vocabularies by re-using concepts from other leading vocabularies and by using the W3C standards RDF, OWL, SWRL, and SPARQL. The ontology already has one independent user and has incorporated real world data from the government of India. a https://sites.google.com/site/dutta2005/home b https://www.michaeldebellis.com/ Dutta, B. and DeBellis, M. (2020). CODO: an ontology for collection and analysis of COVID-19 data. Accepted for publication in the Proc. of 12 th Int. Conf. on Knowledge Engineering and Ontology Development (KEOD), 2-4 November 2020.

Communications in computer and information science, 2021

The CODO ontology was designed to capture data about the Covid-19 pandemic. The goal of the ontol... more The CODO ontology was designed to capture data about the Covid-19 pandemic. The goal of the ontology was to collect epidemiological data about the pandemic so that medical professionals could perform contact tracing and answer questions about infection paths based on information about relations between patients, geography, time, etc. We took information from various spreadsheets and integrated it into one consistent knowledge graph that could be queried with SPARQL and visualized with the Gruff tool in AllegroGraph. The ontology is published on Bioportal and has been used by two projects to date. This paper describes the process used to design the initial ontology and to develop transformations to incorporate data from the Indian government about the pandemic. We went from an ontology to a large knowledge graph with approximately 5M triples in a few months. Our experience demonstrates some common principles that apply to the process of scaling up from an ontology model to a knowledge graph with real-world data. © 2021, Springer Nature Switzerland AG.

Software Engineering and Knowledge Engineering, 1993

... Michael DeBellis, Kanth Miriyala, Sudin Bhat. ... Gilles Lafue, W. Michael Evangelist, Wojtek... more ... Michael DeBellis, Kanth Miriyala, Sudin Bhat. ... Gilles Lafue, W. Michael Evangelist, Wojtek Kozaczynski, Chunka Mui, Gui Cabral, Stan Letovsky, Gerry Williams, Steve Wagner, Gadi Friedman, Anoop Kumar, and Inessa ... and Professor Elliot Soloway of The University of Michigan. ...

This paper is meant to stimulate discussion on future directions that the Knowledge-Based Sofnvar... more This paper is meant to stimulate discussion on future directions that the Knowledge-Based Sofnvare Assistant (KBSA) program should take in order to encourage the tranger of its technology to industrial use. We have analyzed results of KBSA projects to date, and believe the most salient issues are KBSA&#x27;s lack of atten- tion to scalability and its human ana&#x27; organizational im- pact, as well as its weakness in the area of object-oriented design and reuse. To address these issues, we identify op- portunities to leverage technology from other related areas, such as CASE and object-oriented environments.

Software Engineering and Knowledge Engineering, 1993

CRC Press eBooks, Aug 18, 2022

International Journal of Production Research, 2023

Demand shocks and fluctuations underscore the need for new approaches to coordinate collaboration... more Demand shocks and fluctuations underscore the need for new approaches to coordinate collaboration between firms to scale up production. This paper proposes an approach to formalise product and process requirements via a collaboration ontology and applies semantic reasoning techniques for process formation. Our approach contributes to production research by providing flexibility in coordinating firms engaged in demand-driven collaboration. The proposed approach has four core dimensions: (1) The Collaboration ontology builds on a set of product assembly requirements, process steps, their input/output resources and semantic rules; (2) the ontology reasoner derives resource dependencies between the steps; (3) the java tool interprets resource dependencies as possible transitions in Business Process Management Notation (BPMN); (4) a workflow engine executes the generated product assembly process. The approach and the ontology were validated in an industrial aerospace tendering scenario demonstrating its practical relevance for firms seeking demand-driven collaborations to react to production changes. Finally, we position and explain our contributions to the body of knowledge in collaborative production engineering.

Google, Inc. (search). SIGN IN SIGN UP. BACKBORD: an implementation of specification by reformula... more Google, Inc. (search). SIGN IN SIGN UP. BACKBORD: an implementation of specification by reformulation. Authors: John Yen, Univ. of Southern California, Marina del Rey. Robert Neches, Univ. of Southern California, Marina del Rey. Michael Debellis, Univ. of Southern California, Marina del Rey. Pedro Szekely, Univ. of Southern California, Marina del Rey. Peter Aberg, Univ. of Southern California, Marina del Rey. 1991 Article. Bibliometrics. · Downloads (6 Weeks): n/a · Downloads (12 Months): n/a · Citation Count: 5. Published in: · Book. ...

SIGCHI bulletin, Jul 1, 1988

Several previou s systems have utilized a retrieval by reformulatio n paradigm for query-base d r... more Several previou s systems have utilized a retrieval by reformulatio n paradigm for query-base d retrieval from databases .

International Journal of Production Research, 2023

13TH CONFERENCE ON FORMAL ONTOLOGY IN INFORMATION SYSTEMS (FOIS 2023) Sherbrooke, Qc, Canada, 2023

Proceedings CAOS VII: Cognition and Ontologies, 9th Joint Ontology Workshops (JOWO 2023), co-located with FOIS 2023, 19-20 July, 2023, Sherbrooke, Québec, Canada, 2023

An important concept in Evolutionary Psychology is the cognitive module. Cognitive modules are hy... more An important concept in Evolutionary Psychology is the cognitive module. Cognitive modules are hypothesized to be innate in the human genome and form the foundation for basic cognitive functions. Examples include language, causality, contact mechanics, and morality. This is an extension of the work in Biolinguistics pioneered by Chomsky where an innate language faculty is hypothesized to exist and is an alternative approach to the "blank slate" model of psychology that hypothesizes all learning is based on a single general learning process such as Stimulus Response or Hebbian conditioning. Although there has been extensive research on these modules, no one has created a formal model of them. This paper describes an ontology that creates a model based on structures and processes that are commonly ascribed to cognitive modules in evolutionary psychology. This illustrates how OWL can be a powerful tool for cognitive science. One of the biggest obstacles to scientific theories in the "soft" sciences is the difficulty of defining rigorous, testable models. OWL provides a tool that is abstract enough that it can model such concepts while at the same time being rigorous enough that it clarifies issues that go unobserved without a formal model. In this paper I present an ontology that models cognitive modules and describe the research I utilized to define the modules. I present a simple example that models an example of Hunter Gatherer behavior and beliefs. I discuss whether formal methods can be used to study a topic as full of contradictions as the human mind. Specifically, I address anticipated criticism from cognitive scientists such as Lakoff that it is a fantasy to think the universe, much less the mind, can be modeled by formal methods. I conclude with a brief discussion of the implications of the current model.

International Workshop on Ontologies for Services and Society (OSS2023), July 17–20, 2023, Sherbrooke Ontario, Canada, 2023

Samer Sharani was the primary author. I helped him with technical details. Refugeehood is a multi... more Samer Sharani was the primary author. I helped him with technical details. Refugeehood is a multidimensional phenomenon and a complex challenge facing the world today. To equip policy-makers and civil organizations with knowledge tools that can improve their plans, programs, and evaluation, we developed an ontology of refugees' home return. Home return is a sub-field of refugee studies and one of the most elusive concepts. Modeling this sub-field, using the OntoClean method, helps us create a coherent whole, accounting for the complex relations between the various factors that construct home return. In addition, the ontology rigorously defines and (re)constructs the concepts from the literature on home return, providing clarity and rigor for scholars of refugee studies. We conclude with discussion of future plans to develop an online application that makes this ontology friendly for normal users.

Frontiers in artificial intelligence and applications, Dec 20, 2023

Applied Ontology, 2024

International Journal of Web Information Systems, Oct 5, 2022

Communications in computer and information science, 2021

Software Engineering and Knowledge Engineering, 1993

CRC Press eBooks, Aug 18, 2022

International Journal of Production Research, 2023

SIGCHI bulletin, Jul 1, 1988

International Journal of Production Research, 2023

13TH CONFERENCE ON FORMAL ONTOLOGY IN INFORMATION SYSTEMS (FOIS 2023) Sherbrooke, Qc, Canada, 2023

Proceedings CAOS VII: Cognition and Ontologies, 9th Joint Ontology Workshops (JOWO 2023), co-located with FOIS 2023, 19-20 July, 2023, Sherbrooke, Québec, Canada, 2023

International Workshop on Ontologies for Services and Society (OSS2023), July 17–20, 2023, Sherbrooke Ontario, Canada, 2023

Recent Advances in Computer Science and Communications, 2022

A suite of standards known as the Semantic Web is transforming the Internet to a semantic graph r... more A suite of standards known as the Semantic Web is transforming the Internet to a semantic graph rather than a graph of hypertext links. This paper will describe how various ideas and initiatives in artificial intelligence knowledge representation influenced its design. We begin with the seminal work by Alan Turing and Alonzo Church that led to the definition of Turing Machines, enabled digital computing, and provided the mathematical theory of computation which has been one of the determining factors for Artificial Intelligence knowledge representation. We then provide a brief history of artificial intelligence knowledge representation starting with groundbreaking researchers such as Newell and Simon, then to the first "AI boom" driven primarily by rule-based expert systems followed by major initiatives such as Cyc and the DARPA Knowledge Sharing Initiative. We will discuss how innovations from these initiatives affected standards that in turn led to the suite of standards known as the Semantic Web. We conclude with a brief overview of the most important issues currently facing those who wish to see widespread adoption of Semantic Web technology in industry.

This is a revised draft of my earlier paper. It includes many more scenarios, moral rules (e.g., ... more This is a revised draft of my earlier paper. It includes many more scenarios, moral rules (e.g., the Golden Rule and Kant's Categorical Imperative), and discussion. I've submitted an abbreviated version of this paper for publication. I'm posting this draft here for those seeking more detail on the SWRL rules, the additional ethical systems that wouldn't fit into the published version, etc.

This is a critique I wrote of Philippa Foot's Natural Goodness in October of 2017. We read the bo... more This is a critique I wrote of Philippa Foot's Natural Goodness in October of 2017. We read the book as part of a class I audited at UC Berkeley on the Philosophy of Ethics taught by Professor R. Jay Wallace.

Data Science with Semantic Technologies New Trends and Future Developments, 2023

In this chapter we will discuss interoperability frameworks and semantic technology. We will firs... more In this chapter we will discuss interoperability frameworks and semantic technology. We will first introduce the concept of a Data Fabric which is becoming the de facto standard framework for enterprise data interoperability. Next, we will discuss the main layers of a Data Fabric: Data Storage, Metadata and Semantics, Analysis and Coordination, Applications, and DataOps; and the role that semantic technology can play in each. Then we will discuss the new concept of a Data Mesh and how semantic technology also enables this type of interoperability architecture. Finally, we will provide a brief conclusion.

Semantic Web Technologies: Research and Applications (Computational Intelligence in Engineering Problem Solving), 2022

This chapter describes the ways Semantic Web Technology (SWT) is being used in Industry. This is ... more This chapter describes the ways Semantic Web Technology (SWT) is being used in Industry. This is a general overview. Other chapters will go into detail on specific application domains. I begin with a very quick description of my research methodology. Then, I describe the most important ways that SWT provides business value to big data. Next, I will discuss how the concept of a Data Fabric integrates the various concepts and technologies. I will then describe some of the most common types of applications that SWT is currently being utilized for such as Enterprise Data Models, Semantic Search, and Recommendation engines. I will provide examples of most application types. Finally, I will conclude with some issues that have been raised that may hinder adoption of SWT and some suggestions for those who want to learn more about SWT and begin to adopt it in their organization.