Automatic Ontology & Dialog Systems

Notes:

An automatic ontology is a type of ontology that is generated automatically, typically using computational methods. Ontologies are formal representations of knowledge and concepts, and are used to define and organize information in a domain.

Automatic ontologies are often generated from a large dataset or set of text documents, using techniques such as natural language processing (NLP) or machine learning (ML) to identify the concepts and relationships present in the data. The resulting ontology is a representation of the concepts and relationships that were identified in the data, and can be used to organize and classify the data, or to support other types of tasks or functions.

Automatic ontologies are often used in applications such as information retrieval, data integration, and knowledge management, where the ability to link and integrate data from multiple sources is important. They can also be used in natural language processing and machine learning applications, where they can help to define and organize the concepts and relationships that are relevant to a particular domain.

Automatic ontologies can be used in dialog systems to support the understanding and interpretation of user input. Dialog systems are computer systems that can engage in natural language conversations with humans, and are often used in applications such as chatbots, virtual assistants, and customer service systems.

In a dialog system, an automatic ontology can be used to represent the concepts and relationships that are relevant to the domain of the system. For example, an ontology for a chatbot that provides information about restaurants might include concepts such as “restaurant,” “location,” “cuisine,” and “rating,” as well as relationships between these concepts, such as “located in,” “serves,” and “rated.”

The automatic ontology can then be used to interpret user input and understand the concepts and relationships that the user is expressing. For example, if a user asks a chatbot “What are the best Italian restaurants in New York City?”, the chatbot could use the ontology to understand the concepts of “Italian,” “restaurant,” “New York City,” and “best,” and use this information to generate a response.

Automated ontologies and machine-generated ontologies are similar in that they are both types of ontologies that are created using computational methods, rather than being created manually by human experts. However, there are some key differences between the two types of ontologies:

Source of data: Automated ontologies are typically generated from a large dataset or set of text documents, using techniques such as natural language processing (NLP) or machine learning (ML) to identify the concepts and relationships present in the data. Machine-generated ontologies, on the other hand, are typically generated from structured data sources, such as databases or knowledge graphs.
Level of human intervention: Automated ontologies are typically generated with minimal human intervention, relying on computational methods to identify the concepts and relationships present in the data. Machine-generated ontologies, on the other hand, may involve some level of human intervention, such as the creation of templates or rules to guide the generation of the ontology.
Accuracy and completeness: Automated ontologies may be less accurate and complete than machine-generated ontologies, due to the challenges of extracting concepts and relationships from unstructured data sources. Machine-generated ontologies, on the other hand, may be more accurate and complete, as they are typically generated from structured data sources that are easier for computational methods to process.

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DOLCE ergo SUMO: On foundational and domain models in the SmartWeb Integrated Ontology (SWIntO) D Oberle, A Ankolekar, P Hitzler, P Cimiano… – Web Semantics: Science …, 2007 – Elsevier Increased availability of mobile computing, such as personal digital assistants (PDAs), creates the potential for constant and intelligent access to up-to-date,. Cited by 98 Related articles All 42 versions

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Autonomous Classification of Knowledge into an Ontology. ME Taylor, C Matuszek, B Klimt, MJ Witbrock – FLAIRS Conference, 2007 – aaai.org … We have argued why such a technique is necessary for automated ontology building via search and introspection. … Springer, 1995. Witbrock, M., Baxter, D., Curtis, J., et al. An Interactive Dialogue System for Knowledge Acquisition in Cyc. … Cited by 36 Related articles All 16 versions

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Ontology based user modeling for personalization of grid learning services. L Razmerita, S Antipolis, G Gouardères, E Conté… – ELeGI …, 2005 – www-sop.inria.fr … how to construct adaptive systems, how to tailor and filter information, how to personalize help and dialogue systems, how to … Recent research work include a considerable effort towards automatic ontology evolution based on usage data (Stojanovic and Motic, 2003; Park et al. … Cited by 20 Related articles All 5 versions

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[BOOK] Interactive ontology-based user knowledge acquisition: A case study L Aroyo, R Denaux, V Dimitrova, M Pye – 2006 – Springer … Technical report, REWERSE EU NoE (2004) 2. Kobsa, A.: User modeling in dialog systems: Potentials and hazards. … (2004) 31. Noy, NF, Musen, MA: PROMPT: Algorithm and tool for automated ontology merging and alignment. … Cited by 23 Related articles All 13 versions

Kernel Models for Affective Lexicon Creation. N Malandrakis, A Potamianos, E Iosif, SS Narayanan – INTERSPEECH, 2011 – sail.usc.edu … that domain-independent models perform very well for certain tasks, especially, for frustration detection in spoken dialogue systems. … Project Number 99637: “Unsupervised Semantic Relationship Acquisition by Humans and Machines: Application to Automatic Ontology Creation … Cited by 20 Related articles All 5 versions

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Creating ontologies for content representation—the OntoSeed suite EP Bontas, D Schlangen, T Schrader – On the Move to Meaningful Internet …, 2005 – Springer … a long time in many NLP applications, be that machine translation [20], text understanding [14], or dialogue systems (some recent … to evaluate the costs and the benefits of the OntoSeed approach, we ex- amined two subsequent semi-automatic ontology engineering experiments … Cited by 9 Related articles All 11 versions

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Ontology-based user modeling and e-portfolio grid learning services L Razmerita, G Gouardères, E Conté – Applied Artificial Intelligence, 2005 – Taylor & Francis … on: human-agent interaction, how to construct adaptive systems, how to tailor and filter information, how to personalize help and dialogue systems, how to … Recent research work includes a considerable effort towards automatic ontology evolution based on usage data (Park et al … Cited by 9 Related articles All 2 versions

Semi-automatic ontology extension using text mining I Novalija, D Mladenic – Conference on Data Mining and Data …, 2009 – ailab.ijs.si … 1995. [5] Liu, W., Weichselbraun, A., Scharl, A. ,Chang, E.: Semi- Automatic Ontology Extension Using … [8] Witbrock, M., Baxter, D., Curtis, J., Schneider, D., Kahlert, R., Miraglia, P., Wagner, P., Panton, K., Matthews, G., Vizedom, A.: An Interactive Dialogue System for Knowledge … Cited by 2 Related articles

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[BOOK] Knowledge Engineering: Practice and Patterns: 16th International Conference, EKAW 2008, Acitrezza, Sicily, Italy September 29-October 3, 2008, … A Gangemi, J Euzenat – 2008 – books.google.com … work focuses on natural language semantics, computational semantics, knowl- edge representation and inference, and spoken dialogue systems. … 229 Bassem Makni, Khaled Khelif, Rose Dieng-Kuntz, and Hacene Cher? OMEGA: An Automatic Ontology Metadata Generation … All 2 versions

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Perseus: Randomized Point-based Value Iteration for POMDPs MTJ Spaan, N Vlassis, M Helmert, C Domshlak… – J. Artif. Intell. Res.( …, 2011 – jair.org %% This BibTeX bibliography file was created using BibDesk. %% http://bibdesk. sourceforge.net/. %% Created for Ronen Brafman at 2011-10-06 09:31:59 +0200. %% Saved with string encoding Unicode (UTF-8). @article{SpaanV05,. … All 2 versions