There are two main limitations in most of the existing Knowledge Graph Question Answering (KGQA) algorithms. First, the approaches depend heavily on the structure and cannot be easily adapted to other KGs. Second, the availability and amount of additional domain-specific data in structured or unstructured formats has also proven to be critical in many of these systems. Such dependencies limit the applicability of KGQA systems and make their adoption difficult. A novel algorithm is proposed, MuHeQA, that alleviates both limitations by retrieving the answer from textual content automatically generated from KGs instead of queries over them. This new approach (1) works on one or several KGs simultaneously, (2) does not require training data what makes it is domain-independent, (3) enables the combination of knowledge graphs with unstructured information sources to build the answer, and (4) reduces the dependency on the underlying schema since it does not navigate through structured content but only reads property values. MuHeQA extracts answers from textual summaries created by combining information related to the question from multiple knowledge bases, be them structured or not. Experiments over Wikidata and DBpedia show that our approach achieves comparable performance to other approaches in single-fact questions while being domain and KG independent. Results raise important questions for future work about how the textual content that can be created from knowledge graphs enables answer extraction.
{"title":"MuHeQA: Zero-shot question answering over multiple and heterogeneous knowledge bases","authors":"Carlos Badenes-Olmedo, Óscar Corcho","doi":"10.3233/sw-233379","DOIUrl":"https://doi.org/10.3233/sw-233379","url":null,"abstract":"There are two main limitations in most of the existing Knowledge Graph Question Answering (KGQA) algorithms. First, the approaches depend heavily on the structure and cannot be easily adapted to other KGs. Second, the availability and amount of additional domain-specific data in structured or unstructured formats has also proven to be critical in many of these systems. Such dependencies limit the applicability of KGQA systems and make their adoption difficult. A novel algorithm is proposed, MuHeQA, that alleviates both limitations by retrieving the answer from textual content automatically generated from KGs instead of queries over them. This new approach (1) works on one or several KGs simultaneously, (2) does not require training data what makes it is domain-independent, (3) enables the combination of knowledge graphs with unstructured information sources to build the answer, and (4) reduces the dependency on the underlying schema since it does not navigate through structured content but only reads property values. MuHeQA extracts answers from textual summaries created by combining information related to the question from multiple knowledge bases, be them structured or not. Experiments over Wikidata and DBpedia show that our approach achieves comparable performance to other approaches in single-fact questions while being domain and KG independent. Results raise important questions for future work about how the textual content that can be created from knowledge graphs enables answer extraction.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77148796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vasile Păis,, Maria Mitrofan, Carol Luca Gasan, Alexandru Ianov, Corvin Ghit,ă, Vlad Silviu Coneschi, Andrei Onut,
LegalNERo is a manually annotated corpus for named entity recognition in the Romanian legal domain. It provides gold annotations for organizations, locations, persons, time expressions and legal resources mentioned in legal documents. Furthermore, GeoNames identifiers are provided. The resource is available in multiple formats, including span-based, token-based and RDF. The Linked Open Data version is available for both download and querying using SPARQL.
{"title":"LegalNERo: A linked corpus for named entity recognition in the Romanian legal domain","authors":"Vasile Păis,, Maria Mitrofan, Carol Luca Gasan, Alexandru Ianov, Corvin Ghit,ă, Vlad Silviu Coneschi, Andrei Onut,","doi":"10.3233/sw-233351","DOIUrl":"https://doi.org/10.3233/sw-233351","url":null,"abstract":"LegalNERo is a manually annotated corpus for named entity recognition in the Romanian legal domain. It provides gold annotations for organizations, locations, persons, time expressions and legal resources mentioned in legal documents. Furthermore, GeoNames identifiers are provided. The resource is available in multiple formats, including span-based, token-based and RDF. The Linked Open Data version is available for both download and querying using SPARQL.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135702871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present a novel approach for learning embeddings of ALC knowledge base concepts. The embeddings reflect the semantics of the concepts in such a way that it is possible to compute an embedding of a complex concept from the embeddings of its parts by using appropriate neural constructors. Embeddings for different knowledge bases are vectors in a shared vector space, shaped in such a way that approximate subsumption checking for arbitrarily complex concepts can be done by the same neural network, called a reasoner head, for all the knowledge bases. To underline this unique property of enabling reasoning directly on embeddings, we call them reason-able embeddings. We report the results of experimental evaluation showing that the difference in reasoning performance between training a separate reasoner head for each ontology and using a shared reasoner head, is negligible.
{"title":"Reason-able embeddings: Learning concept embeddings with a transferable neural reasoner","authors":"Dariusz Max Adamski, Jedrzej Potoniec","doi":"10.3233/sw-233355","DOIUrl":"https://doi.org/10.3233/sw-233355","url":null,"abstract":"We present a novel approach for learning embeddings of ALC knowledge base concepts. The embeddings reflect the semantics of the concepts in such a way that it is possible to compute an embedding of a complex concept from the embeddings of its parts by using appropriate neural constructors. Embeddings for different knowledge bases are vectors in a shared vector space, shaped in such a way that approximate subsumption checking for arbitrarily complex concepts can be done by the same neural network, called a reasoner head, for all the knowledge bases. To underline this unique property of enabling reasoning directly on embeddings, we call them reason-able embeddings. We report the results of experimental evaluation showing that the difference in reasoning performance between training a separate reasoner head for each ontology and using a shared reasoner head, is negligible.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80504581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Juan Li, Xiangnan Chen, Hongtao Yu, Jiaoyan Chen, Wen Zhang
Knowledge graph reasoning (KGR) aims to infer new knowledge or detect noises, which is essential for improving the quality of knowledge graphs. Recently, various KGR techniques, such as symbolic- and embedding-based methods, have been proposed and shown strong reasoning ability. Symbolic-based reasoning methods infer missing triples according to predefined rules or ontologies. Although rules and axioms have proven effective, it is difficult to obtain them. Embedding-based reasoning methods represent entities and relations as vectors, and complete KGs via vector computation. However, they mainly rely on structural information and ignore implicit axiom information not predefined in KGs but can be reflected in data. That is, each correct triple is also a logically consistent triple and satisfies all axioms. In this paper, we propose a novel NeuRal Axiom Network (NeuRAN) framework that combines explicit structural and implicit axiom information without introducing additional ontologies. Specifically, the framework consists of a KG embedding module that preserves the semantics of triples and five axiom modules that encode five kinds of implicit axioms. These axioms correspond to five typical object property expression axioms defined in OWL2, including ObjectPropertyDomain, ObjectPropertyRange, DisjointObjectProperties, IrreflexiveObjectProperty and AsymmetricObjectProperty. The KG embedding module and axiom modules compute the scores that the triple conforms to the semantics and the corresponding axioms, respectively. Compared with KG embedding models and CKRL, our method achieves comparable performance on noise detection and triple classification and achieves significant performance on link prediction. Compared with TransE and TransH, our method improves the link prediction performance on the Hits@1 metric by 22.0% and 20.8% on WN18RR-10% dataset, respectively.
{"title":"Neural axiom network for knowledge graph reasoning","authors":"Juan Li, Xiangnan Chen, Hongtao Yu, Jiaoyan Chen, Wen Zhang","doi":"10.3233/sw-233276","DOIUrl":"https://doi.org/10.3233/sw-233276","url":null,"abstract":"Knowledge graph reasoning (KGR) aims to infer new knowledge or detect noises, which is essential for improving the quality of knowledge graphs. Recently, various KGR techniques, such as symbolic- and embedding-based methods, have been proposed and shown strong reasoning ability. Symbolic-based reasoning methods infer missing triples according to predefined rules or ontologies. Although rules and axioms have proven effective, it is difficult to obtain them. Embedding-based reasoning methods represent entities and relations as vectors, and complete KGs via vector computation. However, they mainly rely on structural information and ignore implicit axiom information not predefined in KGs but can be reflected in data. That is, each correct triple is also a logically consistent triple and satisfies all axioms. In this paper, we propose a novel NeuRal Axiom Network (NeuRAN) framework that combines explicit structural and implicit axiom information without introducing additional ontologies. Specifically, the framework consists of a KG embedding module that preserves the semantics of triples and five axiom modules that encode five kinds of implicit axioms. These axioms correspond to five typical object property expression axioms defined in OWL2, including ObjectPropertyDomain, ObjectPropertyRange, DisjointObjectProperties, IrreflexiveObjectProperty and AsymmetricObjectProperty. The KG embedding module and axiom modules compute the scores that the triple conforms to the semantics and the corresponding axioms, respectively. Compared with KG embedding models and CKRL, our method achieves comparable performance on noise detection and triple classification and achieves significant performance on link prediction. Compared with TransE and TransH, our method improves the link prediction performance on the Hits@1 metric by 22.0% and 20.8% on WN18RR-10% dataset, respectively.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135792310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Katherine Thornton, Kenneth Seals-Nutt, Mika Matsuzaki, Damion Dooley
We describe our work to integrate the FoodOn ontology with our knowledge base of food composition data, WikiFCD. WikiFCD is knowledge base of structured data related to food composition and food items. With a goal to reuse FoodOn identifiers for food items, we imported a subset of the FoodOn ontology into the WikiFCD knowledge base. We aligned the import via a shared use of NCBI taxon identifiers for the taxon names of the plants from which the food items are derived. Reusing FoodOn benefits WikiFCD by allowing us to leverage the food item groupings that FoodOn contains. This integration also has potential future benefits for the FoodOn community due to the fact that WikiFCD provides food composition data at the food item level, and that WikiFCD is mapped to Wikidata and contains a SPARQL endpoint that supports federated queries. Federated queries across WikiFCD and Wikidata allow us to ask questions about food items that benefit from the cross-domain information of Wikidata, greatly increasing the breadth of possible data combinations.
{"title":"Reuse of the FoodOn ontology in a knowledge base of food composition data","authors":"Katherine Thornton, Kenneth Seals-Nutt, Mika Matsuzaki, Damion Dooley","doi":"10.3233/sw-233207","DOIUrl":"https://doi.org/10.3233/sw-233207","url":null,"abstract":"We describe our work to integrate the FoodOn ontology with our knowledge base of food composition data, WikiFCD. WikiFCD is knowledge base of structured data related to food composition and food items. With a goal to reuse FoodOn identifiers for food items, we imported a subset of the FoodOn ontology into the WikiFCD knowledge base. We aligned the import via a shared use of NCBI taxon identifiers for the taxon names of the plants from which the food items are derived. Reusing FoodOn benefits WikiFCD by allowing us to leverage the food item groupings that FoodOn contains. This integration also has potential future benefits for the FoodOn community due to the fact that WikiFCD provides food composition data at the food item level, and that WikiFCD is mapped to Wikidata and contains a SPARQL endpoint that supports federated queries. Federated queries across WikiFCD and Wikidata allow us to ask questions about food items that benefit from the cross-domain information of Wikidata, greatly increasing the breadth of possible data combinations.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135791825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Caitlin Woods, Matt Selway, Tyler Bikaun, Markus Stumptner, Melinda Hodkiewicz
Maintenance of assets is a multi-million dollar cost each year for asset intensive organisations in the defence, manufacturing, resource and infrastructure sectors. These costs are tracked though maintenance work order (MWO) records. MWO records contain structured data for dates, costs, and asset identification and unstructured text describing the work required, for example ‘replace leaking pump’. Our focus in this paper is on data quality for maintenance activity terms in MWO records (e.g. replace, repair, adjust and inspect). We present two contributions in this paper. First, we propose a reference ontology for maintenance activity terms. We use natural language processing to identify seven core maintenance activity terms and their synonyms from 800,000 MWOs. We provide elucidations for these seven terms. Second, we demonstrate use of the reference ontology in an application-level ontology using an industrial use case. The end-to-end NLP-ontology pipeline identifies data quality issues with 55% of the MWO records for a centrifugal pump over 8 years. For the 33% of records where a verb was not provided in the unstructured text, the ontology can infer a relevant activity class. The selection of the maintenance activity terms is informed by the ISO 14224 and ISO 15926-4 standards and conforms to ISO/IEC 21838-2 Basic Formal Ontology (BFO). The reference and application ontologies presented here provide an example for how industrial organisations can augment their maintenance work management processes with ontological workflows to improve data quality.
{"title":"An ontology for maintenance activities and its application to data quality","authors":"Caitlin Woods, Matt Selway, Tyler Bikaun, Markus Stumptner, Melinda Hodkiewicz","doi":"10.3233/sw-233299","DOIUrl":"https://doi.org/10.3233/sw-233299","url":null,"abstract":"Maintenance of assets is a multi-million dollar cost each year for asset intensive organisations in the defence, manufacturing, resource and infrastructure sectors. These costs are tracked though maintenance work order (MWO) records. MWO records contain structured data for dates, costs, and asset identification and unstructured text describing the work required, for example ‘replace leaking pump’. Our focus in this paper is on data quality for maintenance activity terms in MWO records (e.g. replace, repair, adjust and inspect). We present two contributions in this paper. First, we propose a reference ontology for maintenance activity terms. We use natural language processing to identify seven core maintenance activity terms and their synonyms from 800,000 MWOs. We provide elucidations for these seven terms. Second, we demonstrate use of the reference ontology in an application-level ontology using an industrial use case. The end-to-end NLP-ontology pipeline identifies data quality issues with 55% of the MWO records for a centrifugal pump over 8 years. For the 33% of records where a verb was not provided in the unstructured text, the ontology can infer a relevant activity class. The selection of the maintenance activity terms is informed by the ISO 14224 and ISO 15926-4 standards and conforms to ISO/IEC 21838-2 Basic Formal Ontology (BFO). The reference and application ontologies presented here provide an example for how industrial organisations can augment their maintenance work management processes with ontological workflows to improve data quality.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135791829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Brewster, N. Kalatzis, B. Nouwt, Han Kruiger, J. Verhoosel
The agrifood system faces a great many economic, social and environmental challenges. One of the biggest practical challenges has been to achieve greater data sharing throughout the agrifood system and the supply chain, both to inform other stakeholders about a product and equally to incentivise greater environmental sustainability. In this paper, a data sharing architecture is described built on three principles (a) reuse of existing semantic standards; (b) integration with legacy systems; and (c) a distributed architecture where stakeholders control access to their own data. The system has been developed based on the requirements of commercial users and is designed to allow queries across a federated network of agrifood stakeholders. The Ploutos semantic model is built on an integration of existing ontologies. The Ploutos architecture is built on a discovery directory and interoperability enablers, which use graph query patterns to traverse the network and collect the requisite data to be shared. The system is exemplified in the context of a pilot involving commercial stakeholders in the processed fruit sector. The data sharing approach is highly extensible with considerable potential for capturing sustainability related data.
{"title":"Data sharing in agricultural supply chains: Using semantics to enable sustainable food systems","authors":"C. Brewster, N. Kalatzis, B. Nouwt, Han Kruiger, J. Verhoosel","doi":"10.3233/sw-233287","DOIUrl":"https://doi.org/10.3233/sw-233287","url":null,"abstract":"The agrifood system faces a great many economic, social and environmental challenges. One of the biggest practical challenges has been to achieve greater data sharing throughout the agrifood system and the supply chain, both to inform other stakeholders about a product and equally to incentivise greater environmental sustainability. In this paper, a data sharing architecture is described built on three principles (a) reuse of existing semantic standards; (b) integration with legacy systems; and (c) a distributed architecture where stakeholders control access to their own data. The system has been developed based on the requirements of commercial users and is designed to allow queries across a federated network of agrifood stakeholders. The Ploutos semantic model is built on an integration of existing ontologies. The Ploutos architecture is built on a discovery directory and interoperability enablers, which use graph query patterns to traverse the network and collect the requisite data to be shared. The system is exemplified in the context of a pilot involving commercial stakeholders in the processed fruit sector. The data sharing approach is highly extensible with considerable potential for capturing sustainability related data.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86453600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. C. Teze, José Paredes, Maria Vanina Martinez, Gerardo I. Simari
The role of explanations in intelligent systems has in the last few years entered the spotlight as AI-based solutions appear in an ever-growing set of applications. Though data-driven (or machine learning) techniques are often used as examples of how opaque (also called black box) approaches can lead to problems such as bias and general lack of explainability and interpretability, in reality these features are difficult to tame in general, even for approaches that are based on tools typically considered to be more amenable, like knowledge-based formalisms. In this paper, we continue a line of research and development towards building tools that facilitate the implementation of explainable and interpretable hybrid intelligent socio-technical systems, focusing on features that users can leverage to build explanations to their queries. In particular, we present the implementation of a recently-proposed application framework (and make available its source code) for developing such systems, and explore user-centered mechanisms for building explanations based both on the kinds of explanations required (such as counterfactual, contextual, etc.) and the inputs used for building them (coming from various sources, such as the knowledge base and lower-level data-driven modules). In order to validate our approach, we develop two use cases, one as a running example for detecting hate speech in social platforms and the other as an extension that also contemplates cyberbullying scenarios.
{"title":"Engineering user-centered explanations to query answers in ontology-driven socio-technical systems","authors":"J. C. Teze, José Paredes, Maria Vanina Martinez, Gerardo I. Simari","doi":"10.3233/sw-233297","DOIUrl":"https://doi.org/10.3233/sw-233297","url":null,"abstract":"The role of explanations in intelligent systems has in the last few years entered the spotlight as AI-based solutions appear in an ever-growing set of applications. Though data-driven (or machine learning) techniques are often used as examples of how opaque (also called black box) approaches can lead to problems such as bias and general lack of explainability and interpretability, in reality these features are difficult to tame in general, even for approaches that are based on tools typically considered to be more amenable, like knowledge-based formalisms. In this paper, we continue a line of research and development towards building tools that facilitate the implementation of explainable and interpretable hybrid intelligent socio-technical systems, focusing on features that users can leverage to build explanations to their queries. In particular, we present the implementation of a recently-proposed application framework (and make available its source code) for developing such systems, and explore user-centered mechanisms for building explanations based both on the kinds of explanations required (such as counterfactual, contextual, etc.) and the inputs used for building them (coming from various sources, such as the knowledge base and lower-level data-driven modules). In order to validate our approach, we develop two use cases, one as a running example for detecting hate speech in social platforms and the other as an extension that also contemplates cyberbullying scenarios.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88695048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shruthi Chari, O. Seneviratne, M. Ghalwash, Sola S. Shirai, Daniel Gruen, Pablo Meyer, P. Chakraborty, D. McGuinness
In the past decade, trustworthy Artificial Intelligence (AI) has emerged as a focus for the AI community to ensure better adoption of AI models, and explainable AI is a cornerstone in this area. Over the years, the focus has shifted from building transparent AI methods to making recommendations on how to make black-box or opaque machine learning models and their results more understandable by experts and non-expert users. In our previous work, to address the goal of supporting user-centered explanations that make model recommendations more explainable, we developed an Explanation Ontology (EO). The EO is a general-purpose representation that was designed to help system designers connect explanations to their underlying data and knowledge. This paper addresses the apparent need for improved interoperability to support a wider range of use cases. We expand the EO, mainly in the system attributes contributing to explanations, by introducing new classes and properties to support a broader range of state-of-the-art explainer models. We present the expanded ontology model, highlighting the classes and properties that are important to model a larger set of fifteen literature-backed explanation types that are supported within the expanded EO. We build on these explanation type descriptions to show how to utilize the EO model to represent explanations in five use cases spanning the domains of finance, food, and healthcare. We include competency questions that evaluate the EO’s capabilities to provide guidance for system designers on how to apply our ontology to their own use cases. This guidance includes allowing system designers to query the EO directly and providing them exemplar queries to explore content in the EO represented use cases. We have released this significantly expanded version of the Explanation Ontology at https://purl.org/heals/eo and updated our resource website, https://tetherless-world.github.io/explanation-ontology, with supporting documentation. Overall, through the EO model, we aim to help system designers be better informed about explanations and support these explanations that can be composed, given their systems’ outputs from various AI models, including a mix of machine learning, logical and explainer models, and different types of data and knowledge available to their systems.
{"title":"Explanation Ontology: A general-purpose, semantic representation for supporting user-centered explanations","authors":"Shruthi Chari, O. Seneviratne, M. Ghalwash, Sola S. Shirai, Daniel Gruen, Pablo Meyer, P. Chakraborty, D. McGuinness","doi":"10.3233/sw-233282","DOIUrl":"https://doi.org/10.3233/sw-233282","url":null,"abstract":"In the past decade, trustworthy Artificial Intelligence (AI) has emerged as a focus for the AI community to ensure better adoption of AI models, and explainable AI is a cornerstone in this area. Over the years, the focus has shifted from building transparent AI methods to making recommendations on how to make black-box or opaque machine learning models and their results more understandable by experts and non-expert users. In our previous work, to address the goal of supporting user-centered explanations that make model recommendations more explainable, we developed an Explanation Ontology (EO). The EO is a general-purpose representation that was designed to help system designers connect explanations to their underlying data and knowledge. This paper addresses the apparent need for improved interoperability to support a wider range of use cases. We expand the EO, mainly in the system attributes contributing to explanations, by introducing new classes and properties to support a broader range of state-of-the-art explainer models. We present the expanded ontology model, highlighting the classes and properties that are important to model a larger set of fifteen literature-backed explanation types that are supported within the expanded EO. We build on these explanation type descriptions to show how to utilize the EO model to represent explanations in five use cases spanning the domains of finance, food, and healthcare. We include competency questions that evaluate the EO’s capabilities to provide guidance for system designers on how to apply our ontology to their own use cases. This guidance includes allowing system designers to query the EO directly and providing them exemplar queries to explore content in the EO represented use cases. We have released this significantly expanded version of the Explanation Ontology at https://purl.org/heals/eo and updated our resource website, https://tetherless-world.github.io/explanation-ontology, with supporting documentation. Overall, through the EO model, we aim to help system designers be better informed about explanations and support these explanations that can be composed, given their systems’ outputs from various AI models, including a mix of machine learning, logical and explainer models, and different types of data and knowledge available to their systems.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77091552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Martin Glauer, A. Memariani, F. Neuhaus, T. Mossakowski, Janna Hastings
Reference ontologies provide a shared vocabulary and knowledge resource for their domain. Manual construction and annotation enables them to maintain high quality, allowing them to be widely accepted across their community. However, the manual ontology development process does not scale for large domains. We present a new methodology for automatic ontology extension for domains in which the ontology classes have associated graph-structured annotations, and apply it to the ChEBI ontology, a prominent reference ontology for life sciences chemistry. We train Transformer-based deep learning models on the leaf node structures from the ChEBI ontology and the classes to which they belong. The models are then able to automatically classify previously unseen chemical structures, resulting in automated ontology extension. The proposed models achieved an overall F1 scores of 0.80 and above, improvements of at least 6 percentage points over our previous results on the same dataset. In addition, the models are interpretable: we illustrate that visualizing the model’s attention weights can help to explain the results by providing insight into how the model made its decisions. We also analyse the performance for molecules that have not been part of the ontology and evaluate the logical correctness of the resulting extension.
{"title":"Interpretable ontology extension in chemistry","authors":"Martin Glauer, A. Memariani, F. Neuhaus, T. Mossakowski, Janna Hastings","doi":"10.3233/sw-233183","DOIUrl":"https://doi.org/10.3233/sw-233183","url":null,"abstract":"Reference ontologies provide a shared vocabulary and knowledge resource for their domain. Manual construction and annotation enables them to maintain high quality, allowing them to be widely accepted across their community. However, the manual ontology development process does not scale for large domains. We present a new methodology for automatic ontology extension for domains in which the ontology classes have associated graph-structured annotations, and apply it to the ChEBI ontology, a prominent reference ontology for life sciences chemistry. We train Transformer-based deep learning models on the leaf node structures from the ChEBI ontology and the classes to which they belong. The models are then able to automatically classify previously unseen chemical structures, resulting in automated ontology extension. The proposed models achieved an overall F1 scores of 0.80 and above, improvements of at least 6 percentage points over our previous results on the same dataset. In addition, the models are interpretable: we illustrate that visualizing the model’s attention weights can help to explain the results by providing insight into how the model made its decisions. We also analyse the performance for molecules that have not been part of the ontology and evaluate the logical correctness of the resulting extension.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87249569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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