Research

Jump to: Review

19 pages, 3782 KiB

Open AccessArticle

A Quick Prototype for Assessing OpenIE Knowledge Graph-Based Question-Answering Systems

by Giuseppina Di Paolo, Diego Rincon-Yanez and Sabrina Senatore

Information 2023, 14(3), 186; https://doi.org/10.3390/info14030186 - 16 Mar 2023

Cited by 3 | Viewed by 2284

Due to the rapid growth of knowledge graphs (KG) as representational learning methods in recent years, question-answering approaches have received increasing attention from academia and industry. Question-answering systems use knowledge graphs to organize, navigate, search and connect knowledge entities. Managing such systems requires [...] Read more.

Due to the rapid growth of knowledge graphs (KG) as representational learning methods in recent years, question-answering approaches have received increasing attention from academia and industry. Question-answering systems use knowledge graphs to organize, navigate, search and connect knowledge entities. Managing such systems requires a thorough understanding of the underlying graph-oriented structures and, at the same time, an appropriate query language, such as SPARQL, to access relevant data. Natural language interfaces are needed to enable non-technical users to query ever more complex data. The paper proposes a question-answering approach to support end users in querying graph-oriented knowledge bases. The system pipeline is composed of two main modules: one is dedicated to translating a natural language query submitted by the user into a triple of the form <subject, predicate, object>, while the second module implements knowledge graph embedding (KGE) models, exploiting the previous module triple and retrieving the answer to the question. Our framework delivers a fast OpenIE-based knowledge extraction system and a graph-based answer prediction model for question-answering tasks. The system was designed by leveraging existing tools to accomplish a simple prototype for fast experimentation, especially across different knowledge domains, with the added benefit of reducing development time and costs. The experimental results confirm the effectiveness of the proposed system, which provides promising performance, as assessed at the module level. In particular, in some cases, the system outperforms the literature. Finally, a use case example shows the KG generated by user questions in a graphical interface provided by an ad-hoc designed web application. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and Its Applications)

► Show Figures

Figure 1

17 pages, 7684 KiB

Open AccessArticle

TeCre: A Novel Temporal Conflict Resolution Method Based on Temporal Knowledge Graph Embedding

by Jiangtao Ma, Chenyu Zhou, Yonggang Chen, Yanjun Wang, Guangwu Hu and Yaqiong Qiao

Information 2023, 14(3), 155; https://doi.org/10.3390/info14030155 - 01 Mar 2023

Cited by 3 | Viewed by 2046

Abstract

Since the facts in the knowledge graph (KG) cannot be updated automatically over time, some facts have temporal conflicts. To discover and eliminate the temporal conflicts in the KG, this paper proposes a novel temporal conflict resolution method based on temporal KG embedding [...] Read more.

Since the facts in the knowledge graph (KG) cannot be updated automatically over time, some facts have temporal conflicts. To discover and eliminate the temporal conflicts in the KG, this paper proposes a novel temporal conflict resolution method based on temporal KG embedding (named TeCre). Firstly, the predicate relation and timestamp information of time series are incorporated into the entity–relation embedding representation by leveraging the temporal KG embedding (KGE) method. Then, taking into account the chronological sequence of the evolution of the entity–relation representation over time, TeCre constrains the temporal relation in the KG according to the principles of time disjoint, time precedence, and time mutually exclusive constraints. Besides that, TeCre further considers the sequence vectorization of predicate relation to discover the temporal conflict facts in the KG. Finally, to eliminate the temporal conflict facts, TeCre deletes the tail entities of the temporal conflict facts, and employs the link prediction method to complete the missing tail entities according to the output of the score function based on the entity–relation embedding. Experimental results on four public datasets show that TeCre is significantly better than the state-of-the-art temporal KG conflict resolution model. The mean reciprocal ranking (MRR) and Hits@10 of TeCre are at least 5.46% and 3.2% higher than the baseline methods, respectively. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and Its Applications)

► Show Figures

Figure 1

32 pages, 517 KiB

Open AccessArticle

LPG-Based Knowledge Graphs: A Survey, a Proposal and Current Trends

by Davide Di Pierro, Stefano Ferilli and Domenico Redavid

Information 2023, 14(3), 154; https://doi.org/10.3390/info14030154 - 01 Mar 2023

Cited by 4 | Viewed by 3755

Abstract

A significant part of the current research in the field of Artificial Intelligence is devoted to knowledge bases. New techniques and methodologies are emerging every day for the storage, maintenance and reasoning over knowledge bases. Recently, the most common way of representing knowledge [...] Read more.

A significant part of the current research in the field of Artificial Intelligence is devoted to knowledge bases. New techniques and methodologies are emerging every day for the storage, maintenance and reasoning over knowledge bases. Recently, the most common way of representing knowledge bases is by means of graph structures. More specifically, according to the Semantic Web perspective, many knowledge sources are in the form of a graph adopting the Resource Description Framework model. At the same time, graphs have also started to gain momentum as a model for databases. Graph DBMSs, such as Neo4j, adopt the Labeled Property Graph model. Many works tried to merge these two perspectives. In this paper, we will overview different proposals aimed at combining these two aspects, especially focusing on possibility for them to add reasoning capabilities. In doing this, we will show current trends, issues and possible solutions. In this context, we will describe our proposal and its novelties with respect to the current state of the art, highlighting its current status, potential, the methodology, and our prospect. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and Its Applications)

► Show Figures

Figure 1

14 pages, 494 KiB

Open AccessArticle

Systematic Construction of Knowledge Graphs for Research-Performing Organizations

by David Chaves-Fraga, Oscar Corcho, Francisco Yedro, Roberto Moreno, Juan Olías and Alejandro De La Azuela

Information 2022, 13(12), 562; https://doi.org/10.3390/info13120562 - 30 Nov 2022

Cited by 5 | Viewed by 2162

Abstract

Research-Performing Organizations (e.g., research centers, universities) usually accumulate a wealth of data related to their researchers, the generated scientific results and research outputs, and publicly and privately-funded projects that support their activities, etc. Even though the types of data handled may look similar [...] Read more.

Research-Performing Organizations (e.g., research centers, universities) usually accumulate a wealth of data related to their researchers, the generated scientific results and research outputs, and publicly and privately-funded projects that support their activities, etc. Even though the types of data handled may look similar across organizations, it is common to see that each institution has developed its own data model to provide support for many of their administrative activities (project reporting, curriculum management, personnel management, etc.). This creates obstacles to the integration and linking of knowledge across organizations, as well as difficulties when researchers move from one institution to another. In this paper, we take advantage of the ontology network created by the Spanish HERCULES initiative to facilitate the construction of knowledge graphs from existing information systems, such as the one managed by the company Universitas XXI, which provides support to more than 100 Spanish-speaking research-performing organizations worldwide. Our effort is not just focused on following the modeling choices from that ontology, but also on demonstrating how the use of standard declarative mapping rules (i.e., R2RML) guarantees a systematic and sustainable workflow for constructing and maintaining a KG. We also present several real-world use cases in which the proposed workflow is adopted together with a set of lessons learned and general recommendations that may also apply to other domains. The next steps include researching in the automation of the creation of the mapping rules, the enrichment of the KG with external sources, and its exploitation though distributed environments. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and Its Applications)

► Show Figures

Figure 1

18 pages, 2434 KiB

Open AccessArticle

Multi-Microworld Conversational Agent with RDF Knowledge Graph Integration

by Gabriel Boroghina, Dragos Georgian Corlatescu and Mihai Dascalu

Information 2022, 13(11), 539; https://doi.org/10.3390/info13110539 - 15 Nov 2022

Viewed by 1928

Abstract

We live in an era where time is a scarce resource and people enjoy the benefits of technological innovations to ensure prompt and smooth access to information required for our daily activities. In this context, conversational agents start to play a remarkable role [...] Read more.

We live in an era where time is a scarce resource and people enjoy the benefits of technological innovations to ensure prompt and smooth access to information required for our daily activities. In this context, conversational agents start to play a remarkable role by mediating the interaction between humans and computers in specific contexts. However, they turn out to be laborious for cross-domain use cases or when they are expected to automatically adapt throughout user dialogues. This paper introduces a method to plug in multiple domains of knowledge for a conversational agent localized in Romanian in order to facilitate the extension of the agent’s area of expertise. Furthermore, the agent is intended to become more domain-aware and learn new information dynamically from user conversations by means of a knowledge graph acting as a network of facts and information. We ensure high capabilities for natural language understanding by proposing a novel architecture that takes into account RoBERT-contextualized embeddings alongside syntactic features. Our approach leads to improved intent classification performance (F1 score = 82.6) when compared with a basic pipeline relying only on features extracted from the agent’s training data. Moreover, the proposed RDF knowledge representation is confirmed to provide flexibility in storing and retrieving natural language entities, values, and factoid relations between them in the context of each microworld. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and Its Applications)

► Show Figures

Figure 1

22 pages, 914 KiB

Open AccessArticle

Building Knowledge Graphs from Unstructured Texts: Applications and Impact Analyses in Cybersecurity Education

by Garima Agrawal, Yuli Deng, Jongchan Park, Huan Liu and Ying-Chih Chen

Information 2022, 13(11), 526; https://doi.org/10.3390/info13110526 - 04 Nov 2022

Cited by 15 | Viewed by 7552

Abstract

Knowledge graphs gained popularity in recent years and have been useful for concept visualization and contextual information retrieval in various applications. However, constructing a knowledge graph by scraping long and complex unstructured texts for a new domain in the absence of a well-defined [...] Read more.

Knowledge graphs gained popularity in recent years and have been useful for concept visualization and contextual information retrieval in various applications. However, constructing a knowledge graph by scraping long and complex unstructured texts for a new domain in the absence of a well-defined ontology or an existing labeled entity-relation dataset is difficult. Domains such as cybersecurity education can harness knowledge graphs to create a student-focused interactive and learning environment to teach cybersecurity. Learning cybersecurity involves gaining the knowledge of different attack and defense techniques, system setup and solving multi-facet complex real-world challenges that demand adaptive learning strategies and cognitive engagement. However, there are no standard datasets for the cybersecurity education domain. In this research work, we present a bottom-up approach to curate entity-relation pairs and construct knowledge graphs and question-answering models for cybersecurity education. To evaluate the impact of our new learning paradigm, we conducted surveys and interviews with students after each project to find the usefulness of bot and the knowledge graphs. Our results show that students found these tools informative for learning the core concepts and they used knowledge graphs as a visual reference to cross check the progress that helped them complete the project tasks. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and Its Applications)

► Show Figures

Figure 1

28 pages, 1855 KiB

Open AccessArticle

Automated GDPR Contract Compliance Verification Using Knowledge Graphs

by Amar Tauqeer, Anelia Kurteva, Tek Raj Chhetri, Albin Ahmeti and Anna Fensel

Information 2022, 13(10), 447; https://doi.org/10.3390/info13100447 - 24 Sep 2022

Cited by 5 | Viewed by 2964

Abstract

In the past few years, the main research efforts regarding General Data Protection Regulation (GDPR)-compliant data sharing have been focused primarily on informed consent (one of the six GDPR lawful bases for data processing). In cases such as Business-to-Business (B2B) and Business-to-Consumer (B2C) [...] Read more.

In the past few years, the main research efforts regarding General Data Protection Regulation (GDPR)-compliant data sharing have been focused primarily on informed consent (one of the six GDPR lawful bases for data processing). In cases such as Business-to-Business (B2B) and Business-to-Consumer (B2C) data sharing, when consent might not be enough, many small and medium enterprises (SMEs) still depend on contracts—a GDPR basis that is often overlooked due to its complexity. The contract’s lifecycle comprises many stages (e.g., drafting, negotiation, and signing) that must be executed in compliance with GDPR. Despite the active research efforts on digital contracts, contract-based GDPR compliance and challenges such as contract interoperability have not been sufficiently elaborated on yet. Since knowledge graphs and ontologies provide interoperability and support knowledge discovery, we propose and develop a knowledge graph-based tool for GDPR contract compliance verification (CCV). It binds GDPR’s legal basis to data sharing contracts. In addition, we conducted a performance evaluation in terms of execution time and test cases to validate CCV’s correctness in determining the overhead and applicability of the proposed tool in smart city and insurance application scenarios. The evaluation results and the correctness of the CCV tool demonstrate the tool’s practicability for deployment in the real world with minimum overhead. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and Its Applications)

► Show Figures

Figure 1

25 pages, 775 KiB

Open AccessArticle

ProtoE: Enhancing Knowledge Graph Completion Models with Unsupervised Type Representation Learning

by Yuxun Lu and Ryutaro Ichise

Information 2022, 13(8), 354; https://doi.org/10.3390/info13080354 - 25 Jul 2022

Cited by 2 | Viewed by 1755

Abstract

Knowledge graph completion (KGC) models are a feasible approach for manipulating facts in knowledge graphs. However, the lack of entity types in current KGC models results in inaccurate link prediction results. Most existing type-aware KGC models require entity type annotations, which are not [...] Read more.

Knowledge graph completion (KGC) models are a feasible approach for manipulating facts in knowledge graphs. However, the lack of entity types in current KGC models results in inaccurate link prediction results. Most existing type-aware KGC models require entity type annotations, which are not always available and expensive to obtain. We propose ProtoE, an unsupervised method for learning implicit type and type constraint representations. ProtoE enhances type-agnostic KGC models by relation-specific prototype embeddings. Our method does not rely on entity type annotations to capture the type and type constraints of entities. Unlike existing unsupervised type representation learning methods, which have only a single representation for entity-type and relation-type constraints, our method can capture multiple type constraints in relations. Experimental results show that our method can improve the performance of both bilinear and translational KGC models in the link prediction task. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and Its Applications)

► Show Figures

Figure 1

25 pages, 4727 KiB

Open AccessArticle

An Interactive Virtual Home Navigation System Based on Home Ontology and Commonsense Reasoning

by Alan Schalkwijk, Motoki Yatsu and Takeshi Morita

Information 2022, 13(6), 287; https://doi.org/10.3390/info13060287 - 06 Jun 2022

Cited by 1 | Viewed by 2002

Abstract

In recent years, researchers from the fields of computer vision, language, graphics, and robotics have tackled Embodied AI research. Embodied AI can learn through interaction with the real world and virtual environments and can perform various tasks in virtual environments using virtual robots. [...] Read more.

In recent years, researchers from the fields of computer vision, language, graphics, and robotics have tackled Embodied AI research. Embodied AI can learn through interaction with the real world and virtual environments and can perform various tasks in virtual environments using virtual robots. However, many of these are one-way tasks in which the interaction is interrupted only by answering questions or requests to the user. In this research, we aim to develop a two-way interactive navigation system by introducing knowledge-based reasoning to Embodied AI research. Specifically, the system obtains guidance candidates that are difficult to identify with existing common-sense reasoning alone by reasoning with the constructed home ontology. Then, we develop a two-way interactive navigation system in which the virtual robot can guide the user to the location in the virtual home environment that the user needs while repeating multiple conversations with the user. We evaluated whether the proposed system was able to present appropriate guidance locations as candidates based on users’ speech input about their home environment. For the evaluation, we extracted the speech data from the corpus of daily conversation, the speech data created by the subject, and the correct answer data for each data and calculated the precision, recall, and F-value. As a result, the F-value was 0.47 for the evaluation data extracted from the daily conversation corpus, and the F-value was 0.49 for the evaluation data created by the subject. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and Its Applications)

► Show Figures

Figure 1

16 pages, 2367 KiB

Open AccessArticle

Medical Knowledge Graph Completion Based on Word Embeddings

by Mingxia Gao, Jianguo Lu and Furong Chen

Information 2022, 13(4), 205; https://doi.org/10.3390/info13040205 - 18 Apr 2022

Cited by 8 | Viewed by 3287

Abstract

The aim of Medical Knowledge Graph Completion is to automatically predict one of three parts (head entity, relationship, and tail entity) in RDF triples from medical data, mainly text data. Following their introduction, the use of pretrained language models, such as Word2vec, BERT, [...] Read more.

The aim of Medical Knowledge Graph Completion is to automatically predict one of three parts (head entity, relationship, and tail entity) in RDF triples from medical data, mainly text data. Following their introduction, the use of pretrained language models, such as Word2vec, BERT, and XLNET, to complete Medical Knowledge Graphs has become a popular research topic. The existing work focuses mainly on relationship completion and has rarely solved entities and related triples. In this paper, a framework to predict RDF triples for Medical Knowledge Graphs based on word embeddings (named PTMKG-WE) is proposed, for the specific use for the completion of entities and triples. The framework first formalizes existing samples for a given relationship from the Medical Knowledge Graph as prior knowledge. Second, it trains word embeddings from big medical data according to prior knowledge through Word2vec. Third, it can acquire candidate triples from word embeddings based on analogies from existing samples. In this framework, the paper proposes two strategies to improve the relation features. One is used to refine the relational semantics by clustering existing triple samples. Another is used to accurately embed the expression of the relationship through means of existing samples. These two strategies can be used separately (called PTMKG-WE-C and PTMKG-WE-M, respectively), and can also be superimposed (called PTMKG-WE-C-M) in the framework. Finally, in the current study, PubMed data and the National Drug File-Reference Terminology (NDF-RT) were collected, and a series of experiments was conducted. The experimental results show that the framework proposed in this paper and the two improvement strategies can be used to predict new triples for Medical Knowledge Graphs, when medical data are sufficiently abundant and the Knowledge Graph has appropriate prior knowledge. The two strategies designed to improve the relation features have a significant effect on the lifting precision, and the superposition effect becomes more obvious. Another conclusion is that, under the same parameter setting, the semantic precision of word embedding can be improved by extending the breadth and depth of data, and the precision of the prediction framework in this paper can be further improved in most cases. Thus, collecting and training big medical data is a viable method to learn more useful knowledge. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and Its Applications)

► Show Figures

Figure 1

18 pages, 310 KiB

Open AccessArticle

Using Knowledge Graph Structures for Semantic Interoperability in Electronic Health Records Data Exchanges

by Shelly Sachdeva and Subhash Bhalla

Information 2022, 13(2), 52; https://doi.org/10.3390/info13020052 - 21 Jan 2022

Cited by 7 | Viewed by 3740

Abstract

Information sharing across medical institutions is restricted to information exchange between specific partners. The lifelong electronic health records (EHR) structure and content require standardization efforts. The existing standards such as openEHR, HL7, and CEN TC251 EN 13606 (Technical committee on Health Informatics of [...] Read more.

Information sharing across medical institutions is restricted to information exchange between specific partners. The lifelong electronic health records (EHR) structure and content require standardization efforts. The existing standards such as openEHR, HL7, and CEN TC251 EN 13606 (Technical committee on Health Informatics of the European Committee for Standardization) aim to achieve data independence along with semantic interoperability. This study aims to discover knowledge representation to achieve semantic health data exchange. OpenEHR and CEN TC251 EN 13606 use archetype-based technology for semantic interoperability. The HL7 Clinical Document Architecture is on its way to adopting this through HL7 templates. Archetypes are the basis for knowledge-based systems as these are means to define clinical knowledge. The paper examines a set of formalisms for the suitability of describing, representing, and reasoning about archetypes. Each of the information exchange technologies such as XML, Web Ontology Language, Object Constraint Language, and Knowledge Interchange Format is evaluated as a part of the knowledge representation experiment. These examine the representation of Archetypes as described by Archetype Definition Language. The evaluation maintains a clear focus on the syntactic and semantic transformations among different EHR standards. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and Its Applications)

► Show Figures

Figure 1

Review

Jump to: Research

10 pages, 346 KiB

Open AccessReview

Knowledge Graphs and Explainable AI in Healthcare

by Enayat Rajabi and Somayeh Kafaie

Information 2022, 13(10), 459; https://doi.org/10.3390/info13100459 - 28 Sep 2022

Cited by 11 | Viewed by 6507

Abstract

Building trust and transparency in healthcare can be achieved using eXplainable Artificial Intelligence (XAI), as it facilitates the decision-making process for healthcare professionals. Knowledge graphs can be used in XAI for explainability by structuring information, extracting features and relations, and performing reasoning. This [...] Read more.

Building trust and transparency in healthcare can be achieved using eXplainable Artificial Intelligence (XAI), as it facilitates the decision-making process for healthcare professionals. Knowledge graphs can be used in XAI for explainability by structuring information, extracting features and relations, and performing reasoning. This paper highlights the role of knowledge graphs in XAI models in healthcare, considering a state-of-the-art review. Based on our review, knowledge graphs have been used for explainability to detect healthcare misinformation, adverse drug reactions, drug-drug interactions and to reduce the knowledge gap between healthcare experts and AI-based models. We also discuss how to leverage knowledge graphs in pre-model, in-model, and post-model XAI models in healthcare to make them more explainable. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and Its Applications)

► Show Figures

Figure 1

17 pages, 503 KiB

Open AccessReview

Knowledge Graphs: A Practical Review of the Research Landscape

by Mayank Kejriwal

Information 2022, 13(4), 161; https://doi.org/10.3390/info13040161 - 23 Mar 2022

Cited by 18 | Viewed by 14916

Abstract

Knowledge graphs (KGs) have rapidly emerged as an important area in AI over the last ten years. Building on a storied tradition of graphs in the AI community, a KG may be simply defined as a directed, labeled, multi-relational graph with some form [...] Read more.

Knowledge graphs (KGs) have rapidly emerged as an important area in AI over the last ten years. Building on a storied tradition of graphs in the AI community, a KG may be simply defined as a directed, labeled, multi-relational graph with some form of semantics. In part, this has been fueled by increased publication of structured datasets on the Web, and well-publicized successes of large-scale projects such as the Google Knowledge Graph and the Amazon Product Graph. However, another factor that is less discussed, but which has been equally instrumental in the success of KGs, is the cross-disciplinary nature of academic KG research. Arguably, because of the diversity of this research, a synthesis of how different KG research strands all tie together could serve a useful role in enabling more ‘moonshot’ research and large-scale collaborations. This review of the KG research landscape attempts to provide such a synthesis by first showing what the major strands of research are, and how those strands map to different communities, such as Natural Language Processing, Databases and Semantic Web. A unified framework is suggested in which to view the distinct, but overlapping, foci of KG research within these communities. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and Its Applications)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Knowledge Graph Technology and Its Applications

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (13 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI