Yuri A. Tijerino

Towards Ontology Generation from Tables

At the heart of todays information-explosion problems are issues involving semantics, mutual understanding, concept matching, and interoperability. Ontologies and the Semantic Web are offered as a potential solution, but creating ontologies for real-world knowledge is nontrivial. If we could automate the process, we could significantly improve our chances of making the Semantic Web a reality. While understanding natural language is difficult, tables and other structured information make it easier to interpret new items and relations. In this paper we introduce an approach to generating ontologies based on table analysis. We thus call our approach TANGO (Table ANalysis for Generating Ontologies). Based on conceptual modeling extraction techniques, TANGO attempts to (i) understand a tables structure and conceptual content; (ii) discover the constraints that hold between concepts extracted from the table; (iii) match the recognized concepts with ones from a more general specification of related concepts; and (iv) merge the resulting structure with other similar knowledge representations. TANGO is thus a formalized method of processing the format and content of tables that can serve to incrementally build a relevant reusable conceptual ontology.

Ontology generation from tables

We often need to access and reorganize information available in multiple tables in diverse Web pages. To understand tables, we rely on acquired expertise, background information, and practice. Current computerized tools seldom consider the structure and content in the context of other tables with related information. This paper addresses the table processing issue by developing a new framework to table understanding that applies an ontology-based conceptual modeling extraction approach to: (i) understand a tables structure and conceptual content to the extent possible; (ii) discover the constraints that hold between concepts extracted from the table; (iii) match the recognized concepts with ones from a more general specification of related concepts; and (iv) merge the resulting structure with other similar knowledge representations for use in future situations. The result is a formalized method of processing the format and content of tables while incrementally building a relevant reusable conceptual ontology.

Multilingual ontologies for cross-language information extraction and semantic search

Valuable local information is often available on the web, but encoded in a foreign language that non-local users do not understand. Can we create a system to allow a user to query in language L1 for facts in a web page written in language L2? We propose a suite of multilingual extraction ontologies as a solution to this problem. We ground extraction ontologies in each language of interest, and we map both the data and the metadata among the language-specific extraction ontologies. The mappings are through a central, language-agnostic ontology that allows new languages to be added by only having to provide one mapping rather than one for each language pair. Results from an implemented early prototype demonstrate the feasibility of cross-language information extraction and semantic search. Further, results from an experimental evaluation of ontology-based query translation and extraction accuracy are remarkably good given the complexity of the problem and the complications of its implementation.

A Conceptual-Model-Based Computational Alembic for a Web of Knowledge

The current web is a web of linked pages. Frustrated users search for facts by guessing which keywords or keyword phrases might lead them to pages where they can find facts. Can we make it possible for users to search directly for facts embedded in web pages? Instead of a web of human-readable pages containing machine-inaccessible facts, can the web be a web of machine-accessible facts superimposed over a web of human-readable pages? Ultimately, can the web be a WoK (a Web of Knowledge) that can provide direct answers to factual questions and support these answers by referencing and highlighting relevant base facts embedded in source pages?

AcadeMix Juice- A Hybrid Web 2.0/Semantic Web Platform for Exchange of Academic Knowledge

In this paper, we introduce an academic support system with a participative architecture, which attempts to overcome the cultural hierarchical barriers that prevent communication, collaboration and exchange of academic materials in Japanese institutions of higher learning among undergraduates, graduates and faculty. This system, which we dubbed AcadeMix Juice, promotes participative interactions among its users through a set of functional characteristics that include: 1) social publication, 2) self-organizing interest groups, 3) resource-centered communication, 4) inter-field collaborative summarization, 5) personal academic space, and 6) semantic annotation for personal academic assistants. AcadeMix Juice also promotes interdisciplinary interaction and collaboration among its users through inter-field collaborative summarization. All materials in AcadeMix Juice are annotated and made available to members, visitors or semantic Web agents through an academic ontology, which is based on a extended learning object meta-data (LOM) ontology. This ontology supports extensional semantic evolution through integration with folksonomies and personal bookmarks

Ontology-Based Automatic Receipt Accounting System

Most computer software available today -- although capable of processing text, numbers and other symbols -- cannot process meaning or nuances as we people do. One such case, which is the focus of this work, is the processing of number-rich documents, such as receipts to fulfill business-specific constraints. This task currently is labor intensive in the sense that we manually analyze the receipts and file the information in the required categories of business expenses reports. The premises of this paper is that it should be possible to overcome many of the limitations present in manual, number-rich information filing applications, by further understanding the lexical and semantic relationships through extraction ontologies. This paper introduces one such approach and describes preliminary experimental results that support this hypothesis.

Semantic Wiki Where Human and Agents Collaborate

A Wiki is a collaborative Web page authoring system. Users collaborate to build a Web site by creating and updating Wiki pages through Web browsers. However, conventional Wikis easily lose the consistency of the contents because a number of anonymous users can participate in authoring them. By introducing information agents that understand the. Wiki contents, we can keep the consistency. The agents can automatically update Wiki contents, integrate other Web contents to them, and keep them consistent cooperating with the human users. We propose KawaWiki, which is a semantic Wiki system where human users and information agents can collaborate by utilizing the semantic Web technology. To make agents and users collaborate in authoring Wiki contents, we adopt the RDF as the common representation. It is not easy for novice users to author RDF data, and we introduce KawaWiki templates to generate a Wiki page with RDF data at one time. We also introduce KawaWiki queries to make agents retrieve information efficiently from the Wiki contents. Finally, we introduce an agent description language to specify agents behavior on the Wiki.

Bringing Web Principles to Services: Ontology-BasedWeb Services

Researchers are beginning to realize the potential of Web services that can use the Web as a place for information publication and access as opposed to the traditional Web- services paradigm that merely uses the Web as a transport medium. Traditional Web services can be difficult to discover, can have complex invocation APIs, and require strong coupling between communicating applications. In previous work, we presented ontology-based techniques in which users make service requests using free-form, natural- language-like specifications. This paper shows how we can use these ontological techniques to automatically create ontology-based Web services that (1) are easy for software agents to discover because they are created based on machine-processable formalisms (ontologies), (2) have invocation APIs requiring only simple read and write operations, and (3) require no a priori agreements regarding types and data formats between communicating applications. Experiments with our prototype implementation in several domains show that our approach can effectively create Web services with these characteristics.

Multilingual Extraction Ontologies

The growth of multilingual web content and increasing internationalization portends the need for cross-language query processing. We offer ML-OntoES (a MultiLingual Ontology-based Extraction System) as a solution for narrow-domain/data-rich applications. Based on language-independent extraction ontologies (Embley et al., Conceptual modeling foundations for a web of knowledge. In: Embley D, Thalheim B (eds) Handbook of conceptual modeling: theory, practice, and research challenges. Springer, Heidelberg, Germany, pp 477–516, 2011a), ML-OntoES enables semantic search over domain-specific, semistructured information. Key ideas of ML-OntoES include: (1) monolingual semantic indexing and query interpretation with extraction ontologies and (2) conceptual-level cross-language translation. A prototype implementation, along with experimental work showing good extraction accuracy in multiple languages, demonstrates the viability of the ML-OntoES approach of using multilingual extraction ontologies for cross-language query processing.

A Cross-language Approach for Semantic Recipe Annotation and Nutrition Annotation Augmentation Based on Extraction Ontologies and Culture-Relevant Policy Reasoning

This paper summarizes an approach for cross language annotation and querying of semi-structured data with the use of cross-language extraction ontologies. The paper describes the capabilities of the approach in the context of annotation of nutritional components and their respective values for ingredients found in English, Chinese and Japanese recipes. To this end, a cross-language, data-extraction ontology is designed to annotate the recipes, and then augments the annotations with nutrition information on a per-ingredient basis using nutritional information obtained from nutritional databases found in China, Japan and the USA. The relevant nutritional information is used to augment ingredient annotations, which can be used to respond to specific queries related to nutritional goals or to meet specific dietary regimens. The approach meets cross-cultural constraints by incorporating a cultural policy service subsystem to enable responses based on cultural policy ontologies. The approach allows cross-language queries by annotating recipes and parsing queries with a cross-language extraction ontology. For example, responses to an English or Chinese query might be obtained from a Japanese recipe and vice versa.