In this paper, we introduce a novel interpreting framework that learns an interpretable model based on an ontology-based sampling technique to explain agnostic prediction models. Different from existing approaches, our algorithm considers contextual correlation among words, described in domain knowledge ontologies, to generate semantic explanations. To narrow down the search space for explanations, which is a major problem of long and complicated text data, we design a learnable anchor algorithm, to better extract explanations locally. A set of regulations is further introduced, regarding combining learned interpretable representations with anchors to generate comprehensible semantic explanations. An extensive experiment conducted on two real-world datasets shows that our approach generates more precise and insightful explanations compared with baseline approaches.
We propose a system for automated essay grading using ontologies and textual entailment. The process of textual entailment is guided by hypotheses, which are extracted from a domain ontology. Textual entailment checks if the truth of the hypothesis follows from a given text. We enact textual entailment to compare students answer to a model answer obtained from ontology. We validated the solution against various essays written by students in the chemistry domain.
King's College London（伦敦国王学院）；University of Sussex（苏塞克斯大学）；The University of Manchester（曼彻斯特大学）
Ontology development is a non-trivial task requiring expertise in the chosen ontological language. We propose a method for making the content of ontologies more transparent by presenting, through the use of natural language generation, naturalistic descriptions of ontology classes as textual paragraphs. The method has been implemented in a proof-of- concept system, OntoVerbal, that automatically generates paragraph-sized textual descriptions of ontological classes expressed in OWL. OntoVerbal has been applied to ontologies that can be loaded into Protégé and been evaluated with SNOMED CT, show…
Automatic reasoning about textual information is a challenging task in modern Natural Language Processing (NLP) systems. In this work we describe our proposal for representing and reasoning about Portuguese documents by means of Linked Data like ontologies and thesauri. Our approach resorts to a specialized pipeline of natural language processing (part-of-speech tagger, named entity recognition, semantic role labeling) to populate an ontology for the domain of criminal investigations. The provided architecture and ontology are language independent. Although some of the NLP modules are language…
The conceptual modelling built from text is rarely an ontology. As a matter of fact, such a conceptualization is corpus-dependent and does not offer the main properties we expect from ontology. Furthermore, ontology extracted from text in general does not match ontology defined by expert using a formal language. It is not surprising since ontology is an extra-linguistic conceptualization whereas knowledge extracted from text is the concern of textual linguistics. Incompleteness of text and using rhetorical figures, like ellipsis, modify the perception of the conceptualization we may have. Onto…
Orange Labs（奥兰奇实验室）；University of Versailles（凡尔赛大学）
The construction of a reference ontology for a large domain still remains an hard human task. The process is sometimes assisted by software tools that facilitate the information extraction from a textual corpus. Despite of the great use of XML Schema files on the internet and especially in the B2B domain, tools that offer a complete semantic analysis of XML schemas are really rare. In this paper we introduce Janus, a tool for automatically building a reference knowledge base starting from XML Schema files. Janus also provides different useful views to simplify B2B application integration.
Institute of Mathematics and Computer Science, University of Latvia（拉脱维亚大学数学与计算机科学研究所）
Ontologies are one of the core foundations of the Semantic Web. To participate in Semantic Web projects, domain experts need to be able to understand the ontologies involved. Visual notations can provide an overview of the ontology and help users to understand the connections among entities. However, the users first need to learn the visual notation before they can interpret it correctly. Controlled natural language representation would be readable right away and might be preferred in case of complex axioms, however, the structure of the ontology would remain less apparent. We propose to combi…
Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
Authoring of OWL-DL ontologies is intellectually challenging and to make this process simpler, many systems accept natural language text as input. A text-based ontology authoring approach can be successful only when it is combined with an effective method for extracting ontological axioms from text. Extracting axioms from unrestricted English input is a substantially challenging task due to the richness of the language. Controlled natural languages (CNLs) have been proposed in this context and these tend to be highly restrictive. In this paper, we propose a new CNL called TEDEI (TExtual DEscri…
In this paper we present a textual description, in terms of Description Logics, of the BPMN Ontology, which provides a clear semantic formalisation of the structural components of the Business Process Modelling Notation (BPMN), based on the latest stable BPMN specifications from OMG [BPMN Version 1.1 -- January 2008]. The development of the ontology was guided by the description of the complete set of BPMN Element Attributes and Types contained in Annex B of the BPMN specifications.
To present the biodiversity information, a semantic model is required that connects all kinds of data about living creatures and their habitats. The model must be able to encode human knowledge for machines to be understood. Ontology offers the richest machine-interpretable (rather than just machine-processable) and explicit semantics that are being extensively used in the biodiversity domain. Various ontologies are developed for the biodiversity domain however a review of the current landscape shows that these ontologies are not capable to define the Indian biodiversity information though Ind…
Business ontology can enhance the successful development of complex enterprise system; this is being achieved through knowledge sharing and the ease of communication between every entity in the domain. Through human semantic interaction with the web resources, machines to interpret the data published in a machine interpretable form under web. However, the theoretical practice of business ontology in eCommerce domain is quite a few especially in the section of electronic transaction, and the various techniques used to obtain efficient communication across spheres are error prone and are not alw…
Ho Chi Minh City University of Technology（胡志明市理工大学）
Traditional information retrieval systems represent documents and queries by keyword sets. However, the content of a document or a query is mainly defined by both keywords and named entities occurring in it. Named entities have ontological features, namely, their aliases, classes, and identifiers, which are hidden from their textual appearance. Besides, the meaning of a query may imply latent named entities that are related to the apparent ones in the query. We propose an ontology-based generalized vector space model to semantic text search. It exploits ontological features of named entities a…
Most definitions of ontology, viewed as a "specification of a conceptualization", agree on the fact that if an ontology can take different forms, it necessarily includes a vocabulary of terms and some specification of their meaning in relation to the domain's conceptualization. And as domain knowledge is mainly conveyed through scientific and technical texts, we can hope to extract some useful information from them for building ontology. But is it as simple as this? In this article we shall see that the lexical structure, i.e. the network of words linked by linguistic relationships, does not n…
Wikontic: Constructing Wikidata-Aligned, Ontology-Aware Knowledge Graphs with Large Language Models
Cognitive AI Systems Lab（认知人工智能系统实验室）；Moscow Independent Research Institute of Artificial Intelligence（莫斯科独立人工智能研究所）；London Institute for Mathematical Sciences（伦敦数学科学研究所）
Learning continuous representations from unlabeled textual data has been increasingly studied for benefiting semi-supervised learning. Although it is relatively easier to interpret discrete representations, due to the difficulty of training, learning discrete representations for unlabeled textual data has not been widely explored. This work proposes TIGAN that learns to encode texts into two disentangled representations, including a discrete code and a continuous noise, where the discrete code represents interpretable topics, and the noise controls the variance within the topics. The discrete …
Tunis El Manar University（突尼斯埃尔马纳尔大学）；Faculty of Sciences of Tunis（突尼斯理学院）
Today is the era of intelligence in machines. With the advances in Artificial Intelligence, machines have started to impersonate different human traits, a chatbot is the next big thing in the domain of conversational services. A chatbot is a virtual person who is capable to carry out a natural conversation with people. They can include skills that enable them to converse with the humans in audio, visual, or textual formats. Artificial intelligence conversational entities, also called chatbots, conversational agents, or dialogue system, are an excellent example of such machines. Obtaining the r…
University at Buffalo（布法罗大学）；National Center for Ontological Research（国家本体研究中心）；Institute for Artificial Intelligence and Data Science（人工智能与数据科学研究所）；Alabama Commission on Higher Education（阿拉巴马州高等教育委员会）；University of Michigan Medical School（密歇根大学医学院）；University of Florida（佛罗里达大学）；Medical College of Wisconsin（威斯康星医学院）
Université de technologie de Compiègne（贡比涅技术大学）；CNRS（法国国家科学研究中心）；Vivocaz
Measurement of the semantic relatedness or likeness between terms, words, or text data plays an important role in different applications dealing with textual data such as knowledge acquisition, recommender system, and natural language processing. Over the past few years, many ontologies have been developed and used as a form of structured representation of knowledge bases for information systems. The calculation of semantic similarity from ontology has developed and depending on the context is complemented by other similarity calculation methods. In this paper, we propose and carry on an appro…
Embedding Ontologies via Incorporating Extensional and Intensional Knowledge
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Department of Computer Science, The University of Manchester（曼彻斯特大学计算机科学系）；China Mobile Research Institute（中国移动研究院）
Curtin University of Technology（科廷科技大学）；University of Western Australia（西澳大学）
The need for domain ontologies in mission critical applications such as risk management and hazard identification is becoming more and more pressing. Most research on ontology learning conducted in the academia remains unrealistic for real-world applications. One of the main problems is the dependence on non-incremental, rare knowledge and textual resources, and manually-crafted patterns and rules. This paper reports work in progress aiming to address such undesirable dependencies during ontology construction. Initial experiments using a working prototype of the system revealed promising poten…
Can we discover dialog structure by dividing utterances into labelled clusters. Can these labels be generated from the data. Typically for dialogs we need an ontology and use that to discover structure, however by using unsupervised classification and self-labelling we are able to intuit this structure without any labels or ontology. In this paper we apply SCAN (Semantic Clustering using Nearest Neighbors) to dialog data. We used BERT for pretext task and an adaptation of SCAN for clustering and self labeling. These clusters are used to identify transition probabilities and create the dialog s…
From Prompts to Context: An Ontology-Driven Framework for Human-Generative AI Collaboration
Gamaizer；Université de technologie de Compiègne, CNRS, Heudiasyc（法国图尔学院、CNRS、Heudiasyc）；Sorbonne Université, CNRS UMR 7585, LPMHE（索邦大学、CNRS UMR 7585、LPMHE）
As part of the SMILK Joint Lab, we studied the use of Natural Language Processing to: (1) enrich knowledge bases and link data on the web, and conversely (2) use this linked data to contribute to the improvement of text analysis and the annotation of textual content, and to support knowledge extraction. The evaluation focused on brand-related information retrieval in the field of cosmetics. This article describes each step of our approach: the creation of ProVoc, an ontology to describe products and brands; the automatic population of a knowledge base mainly based on ProVoc from heterogeneous …
Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；The Jackson Laboratory（杰克逊实验室）；University of Arizona（亚利桑那大学）；Simon Fraser University（西蒙弗雷泽大学）；The Jackson Laboratory for Genomic Medicine（杰克逊基因组医学实验室）；University at Buffalo（布法罗大学）；University of Florida（佛罗里达大学）；Northwestern University（西北大学）；SIB Swiss Institute of Bioinformatics（SIB瑞士生物信息学研究所）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；University of São Paulo（圣保罗大学）；University of Colorado Anschutz Medical Campus（科罗拉多大学安舒茨医学中心）；Sanger Institute（桑格研究所）；European Bioinformatics Institute (EMBL-EBI)（欧洲生物信息研究所（EMBL-EBI））；Phoenix Bioinformatics（菲尼克斯生物信息学公司）；Stowers Institute for Medical Research（斯托尔斯医学研究所）；IC-FOODS；Rothamsted Research（洛桑研究所）；National Center for Advancing Translational Sciences（国家推进转化科学中心）；INRAE, French National Research Institute for Agriculture, Food and Environment（法国国家农业、食品与环境研究院（INRAE））；University of Cambridge（剑桥大学）
While deep learning has achieved great success in many fields, one common criticism about deep learning is its lack of interpretability. In most cases, the hidden units in a deep neural network do not have a clear semantic meaning or correspond to any physical entities. However, model interpretability and explainability are crucial in many biomedical applications. To address this challenge, we developed the Factor Graph Neural Network model that is interpretable and predictable by combining probabilistic graphical models with deep learning. We directly encode biological knowledge such as Gene …
Imperial College London（伦敦帝国学院）；IBM Research（IBM研究院）
Data exploration is an important step of every data science and machine learning project, including those involving textual data. We provide a novel language tool, in the form of a publicly available Python library for extracting patterns from textual data. The library integrates a first public implementation of the existing GrASP algorithm. It allows users to extract patterns using a number of general-purpose built-in linguistic attributes (such as hypernyms, part-of-speech tags, and syntactic dependency tags), as envisaged for the original algorithm, as well as domain-specific custom attribu…
Amazon（亚马逊）；Flipkart（弗利普卡特）；Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校）
Systematically discovering semantic relationships in text is an important and extensively studied area in Natural Language Processing, with various tasks such as entailment, semantic similarity, etc. Decomposability of sentence-level scores via subsequence alignments has been proposed as a way to make models more interpretable. We study the problem of aligning components of sentences leading to an interpretable model for semantic textual similarity. In this paper, we introduce a novel pointer network based model with a sentinel gating function to align constituent chunks, which are represented…
User acceptance of artificial intelligence agents might depend on their ability to explain their reasoning, which requires adding an interpretability layer that fa- cilitates users to understand their behavior. This paper focuses on adding an in- terpretable layer on top of Semantic Textual Similarity (STS), which measures the degree of semantic equivalence between two sentences. The interpretability layer is formalized as the alignment between pairs of segments across the two sentences, where the relation between the segments is labeled with a relation type and a similarity score. We present …
