Matching HTML Tables to DBpedia

In recent years, several large-scale knowledge bases (KBs) have been constructed, such as YAGO, DBpedia, and Google Knowledge Graph. Although automatic extractio techniques that extract facts and rules from the Web is necessary for constructing such large-scale KBs, incorporation of noisy, unreliable knowledge cannot be unavoidable. Thus, Google Knowledge Vault assigns extracted knowledge with confidence scores based on consistency with the existing KBs. In this paper, we propose a new approach for associating confidence scores with knowledge based on a large amount of raw data for domains, where there is no existing KB. We first construct knowledge in a specific domain as a semantic network, and then design a probabilistic network, that corresponds to the semantic network. To associate the confidence scores with the semantic network, we train the probabilistic network with a large amount of open data, provided by the Osaka central wholesale market in Japan. We also confirm the validity of the confidence scores with the accuracy of reasoning on the probabilistic network. A semantic network associated with confidence scores, that is, a weighted labeled graph is advantageous not only for reducing the noisy, unreliable knowledge with low confidence, but also for making retrieval results ranking on the KB. In the future, probabilistic reasoning on semantic networks may also be possible.

TAPON-MT: A versatile framework for semantic labelling

In this tutorial, we wish to cover the foundational, methodological, and system development aspects of translating structured data (such as data in tabular form) and knowledge bases (such as knowledge graphs) into natural language. The attendees of the tutorial will be able to take away from this tutorial, (1) the basic ideas around how modern NLP and NLG techniques could be applied to describe and summarize textual data in format that is non-linguistic in nature or has some structure, and (2) a few interesting open-ended questions, which could lead to significant research contributions in future. The tutorial aims to convey challenges and nuances in structured data translation, data representation techniques, and domain adaptable solutions for translation of the data into natural language form. Various solutions, starting from traditional rule based/heuristic driven and modern data-driven and ultra-modern deep-neural style architectures will be discussed, followed by a brief discussion on evaluation and quality estimation. A significant portion of the tutorial will be dedicated towards unsupervised, scalable, and adaptable solutions, given that systems for such an important task will never naturally enjoy sustainable large scale domain independent labeled (parallel) data.

Big data represents a new technology for managing data with high velocity, volume, variety and contributes to creating value for companies. As quoted in1, capturing all queries made on the company website or from customer support calls, emails or chat lines, regardless of their outcome, may have significant value in identifying emerging trends. The Big Data Era has largely contributed in accelerating the development strategic plans issued from governments and research organisms, coving the management, exploitation and analysis of these data by taking into account the different V's of Big Data. Among these plans, we can cite for instance the development of: (a) large-scale platforms (ex. data-clusters, distributed data clusters), (b) Software Defined Environments (SDE) (ex. IBM SDE), (c) advanced programming paradigms (ex. map-reduce, Spark, etc.), Data Analytics Tools (Rapid Miner, Google Fusion Tables, Solver), (d) Visualization tools (Google Chart, Tableau, Oracle Visual Analyzer), and (h) high quality and valuable Knowledge Bases (KB), constructed either by academicians (e.g., Cvc, DBpedia, Freebase, and YAGO) and industrials (e.g., Google Knowledge Graph, Facebook Knowledge Graph, Amazon Knowledge Graph, Credit Rating Agencies, Enterprise Knowledge Base, etc.). In this talk, we would like to foster the creation of a think tank dedicated to getting the best from Big Data V's and the efforts related to it to revisit our research activities without compromising them. In this talk, we would like to share the experience conducted with our Model and Data Engineering Team of the LIAS Laboratory at ISAE-ENSMA, which aims at the design of data warehousing applications. Based on the literature, this design is based on two main approaches: (i) a supply-driven approach (also called data-driven) that starts with an analysis of operational data sources in order to identify all the available data and (ii) a user-driven approach (also known as requirement-driven or goal-orientated) which stems from the determination of the information requirements of different business users. Several studies and experiments show that resorting to these two approaches entails a high risk for companies, since some functional requirements cannot be satisfied. This is due to the lack of relevant data in sources. In parallel, reference studies have identified the crucial role of knowledge bases (KB) for analytical tasks, by offering analysts more entities (people, places, products, etc.). The availability of a huge, high quality valuable KB is an asset for data warehousing designers and decision-makers to construct/exploit a valuable data warehouse. So, faced with this situation, we here present a value-driven approach that revisits the traditional life cycle of the design of data warehouses, by considering KB as an external resource. These different phases are illustrated via the YAGO KB.

Knowledge graph technology is a critical part of artificial intelligence research. It establishes a knowledge base with the capacity of semantic processing and open interconnection in order to provide intelligent information service, such as search, question-answering, personalized recommendation, and so on. This article first presents a comprehensive study on definitions and architectures of knowledge graphs. Then we summarizes recent advances in knowledge graphs, including knowledge extraction, knowledge representation, knowledge fusion, and knowledge reasoning, with typical applications. Finally, this article concludes with future challenges of knowledge graphs.

Entity Alignment is a research topic that has drawn a lot of attention from practitioners and researchers from many different areas in the last years. Also referred to as Entity Resolution or Instance Matching, it involves aligning different entity representations that refer to the same real-world entity. However, it is still a challenging solution and existing works are based on iterative methods, blocking schemes and learning approaches. Among these latter, recent works have been using embedding models, focusing only on structural information. In this paper, we present an approach for entity alignment that enrich entity embeddings with different literal information. To be able to express complex relationships between properties and involving multiple entities we employ FrameBase Schema, which is based on linguistic frames to create complex mappings from external knowledge bases to a unique schema. Preliminary experiments showed competitive results to the performance of entity alignment task.

With the development of Semantic Web, the automatic construction of large scale knowledge bases (KBs) has been receiving increasing attention in recent years. Although these KBs are very large, they are still often incomplete. Many existing approaches to KB completion focus on performing inference over a single KB and suffer from the feature sparsity problem. Moreover, traditional KB completion methods ignore complementarity which exists in various KBs implicitly. In this paper, we treat KBs completion as a large matrix completion task and integrate different KBs to infer new facts simultaneously. We present two improvements to the quality of inference over KBs. First, in order to reduce the data sparsity, we utilize the type consistency constraints between relations and entities to initialize negative data in the matrix. Secondly, we incorporate the similarity of relations between different KBs into matrix factorization model to take full advantage of the complementarity of various KBs. Experimental results show that our approach performs better than methods that consider only existing facts or only a single knowledge base, achieving significant accuracy improvements in binary relation prediction.

Modern knowledge bases contain and organize knowledge from many different topic areas. Apart from specific entity information, they also store information about their relationships amongst each other. Combining this information results in a knowledge graph that can be particularly helpful in cases where relationships are of central importance. Among other applications, modern risk assessment in the financial sector can benefit from the inherent network structure of such knowledge graphs by assessing the consequences and risks of certain events, such as corporate insolvencies or fraudulent behavior, based on the underlying network structure. As public knowledge bases often do not contain the necessary information for the analysis of such scenarios, the need arises to create and maintain dedicated domain-specific knowledge bases. This thesis investigates the process of creating domain-specific knowledge bases from structured and unstructured data sources. In particular, it addresses the topics of named entity recognition (NER), duplicate detection, and knowledge validation, which represent essential steps in the construction of knowledge bases. As such, we present a novel method for duplicate detection based on a Siamese neural network that is able to learn a dataset-specific similarity measure which is used to identify duplicates. Using the specialized network architecture, we design and implement a knowledge transfer between two deduplication networks, which leads to significant performance improvements and a reduction of required training data. Furthermore, we propose a named entity recognition approach that is able to identify company names by integrating external knowledge in the form of dictionaries into the training process of a conditional random field classifier. In this context, we study the effects of different dictionaries on the performance of the NER classifier. We show that both the inclusion of domain knowledge as well as the generation and use of alias names results in significant performance improvements. For the validation of knowledge represented in a knowledge base, we introduce Colt, a framework for knowledge validation based on the interactive quality assessment of logical rules. In its most expressive implementation, we combine Gaussian processes with neural networks to create Colt-GP, an interactive algorithm for learning rule models. Unlike other approaches, Colt-GP uses knowledge graph embeddings and user feedback to cope with data quality issues of knowledge bases. The learned rule model can be used to conditionally apply a rule and assess its quality. Finally, we present CurEx, a prototypical system for building domain-specific knowledge bases from structured and unstructured data sources. Its modular design is based on scalable technologies, which, in addition to processing large datasets, ensures that the modules can be easily exchanged or extended. CurEx offers multiple user interfaces, each tailored to the individual needs of a specific user group and is fully compatible with the Colt framework, which can be used as part of the system. We conduct a wide range of experiments with different datasets to determine the strengths and weaknesses of the proposed methods. To ensure the validity of our results, we compare the proposed methods with competing approaches.

Knowledge graphs (KGs) remain an underutilized tool in the field of psychiatric research. In the broader biomedical field KGs are already a significant tool mainly used as knowledge database or for novel relation detection between biomedical entities. This review aims to outline how KGs would further research in the field of psychiatry in the age of Artificial Intelligence (AI) and Large Language Models (LLMs). We conducted a thorough literature review across a spectrum of scientific fields ranging from computer science and knowledge engineering to bioinformatics. The literature reviewed was taken from PubMed, Semantic Scholar and Google Scholar searches including terms such as "Psychiatric Knowledge Graphs", "Biomedical Knowledge Graphs", "Knowledge Graph Machine Learning Applications", "Knowledge Graph Applications for Biomedical Sciences". The resulting publications were then assessed and accumulated in this review regarding their possible relevance to future psychiatric applications. A multitude of papers and applications of KGs in associated research fields that are yet to be utilized in psychiatric research was found and outlined in this review. We create a thorough recommendation for other computational researchers regarding use-cases of these KG applications in psychiatry. This review illustrates use-cases of KG-based research applications in biomedicine and beyond that may aid in elucidating the complex biology of psychiatric illness and open new routes for developing innovative interventions. We conclude that there is a wealth of opportunities for KG utilization in psychiatric research across a variety of application areas including biomarker discovery, patient stratification and personalized medicine approaches.

The development of a computer-aided design (CAD) system for neutralizing emissions from energy source enterprises and production enterprises requires the creation of an appropriate knowledge base. This article discusses the construction of such a knowledge base and its application in CAD system for neutralizing emissions from energy source enterprises and production enterprises. Using development tools such as Protégé (Java), PyCharm (Python) and Navicat (MySQL), a knowledge base and a client platform for the CAD system for neutralizing emissions from energy source enterprises and production enterprises were developed. A key technical framework for building the CAD system for neutralizing emissions from energy source enterprises and production enterprises knowledge base is developed, covering the main aspects of knowledge acquisition, representation and dynamic updating. The accuracy and efficiency of knowledge retrieval were significantly enhanced through ontological modeling, which enabled the analysis of associations within diverse datasets. During the development of the knowledge base, logical inference rules were created and transformed into a knowledge graph model. This approach, combined with the dynamic characteristics of the knowledge graph, facilitates continuous updating of the knowledge base. The practical value of applying the knowledge base in complex scenarios was validated through its implementation in the system for neutralizing emissions from energy source enterprises and production enterprises. The developed knowledge base is an integral component of the CAD system for neutralizing emissions from energy source enterprises and production enterprises, aimed at addressing emission-related challenges. The CAD system for neutralizing emissions from energy source enterprises and production enterprises knowledge base solves the key problems of knowledge fusion, data migration and reasoning validity, and provides theoretical support and technical path for building an intelligent and scalable CAD system for neutralizing emissions from energy source enterprises and production enterprises knowledge service system.

Open knowledge platform can provide a purified knowledge base. Thus, we build a platform for several application areas in a cloud computing that supports APIs for various data based on a knowledge utilization model. The goal of this platform is to maximize the utilization of the knowledge base. In order to achieve this goal, we designed the structure of this platform as an open knowledge platform. The targets of the design are to maximize the utilization of data linkage, to expand it to national common knowledge and to increase its usability by providing services with knowledge graphs. In order to design the platform we identified users, information sources, and infrastructures. In the process, we found it is crucial to specify roles and services to the users of the platform. The requirements are induced from a utilization model and scenario of the service based on the knowledge graph. With the service scenario, stakeholders of the platform started narrow down function modules needed to support the service. One of the modules is a national common knowledge in the knowledge base, which provide essential connected knowledge to support solving regional problems of government such as earthquake, flooding. To increase the usability of data scattered by departments and agencies, data linkage, and knowledge between fragmented data sets is included in this platform. Subsequently, we designed modules to support the effective utilization of this knowledge information. Also, we found that a cloud infrastructure instead of in-house hardware and software could provide flexible and compatible services for the platform. Moreover, the cloud system has advantages on big data analysis and distributed system interconnection. Utilization model and scenario-based process modeling provide a systematic approach to design an open knowledge platform that supports many required components enabling interoperability, compatibility, and connectivity among other knowledge bases..

Derived from knowledge bases, knowledge graphs represent knowledge expressions in graphs, which utilize nodes and edges to denote entities and relations conceptually. Knowledge graph can be described in textual triple form, consisting of head entities, tail entities and relations between entities. In order to represent elements in knowledge graphs, knowledge graph embedding techniques are proposed to map entities and relations into continuous vector spaces as numeric vectors for computational efficiency. Convolution-based knowledge graph embedding models have promising performance for knowledge graph representation learning. However, the input of those neural network-based models is frequently in handmade forms and may suffer from low efficiency in feature extraction procedure of the models. In this paper, a convolutional autoencoder is proposed for knowledge graph representation learning with entity pairs as input, aiming to obtain corresponding hidden relation representation. In addition, a bi-directional relation encoding network is utilized to represent semantic of entities in different directional relation patterns, as an encoder to output representation for initialization of the convolutional autoencoder. Experiments are conducted on standard datasets including, WN18RR, Kinship, NELL-995 and FB15k-237 as a link prediction task. Besides, input embedding matrix composed of different ingredients is designed to evaluate performances of the convolutional autoencoder. The results demonstrate that our model is effective in learning representation from entity feature interactions.

Linking named entities occurring in text to their corresponding entity in a Knowledge Base (KB) is challenging, especially when dealing with historical texts. In this work, we introduce Musical Heritage named Entities Recognition, Classification and Linking (mhercl), a novel benchmark consisting of manually annotated sentences extrapolated from historical periodicals of the music domain. mhercl contains named entities under-represented or absent in the most famous KBs. We experiment with several State-of-the-Art models on the Entity Linking (EL) task and show that mhercl is a challenging dataset for all of them. We propose a novel unsupervised EL model and a method to extend supervised entity linkers by using Knowledge Graphs (KGs) to tackle the main difficulties posed by historical documents. Our experiments reveal that relying on unsupervised techniques and improving models with logical constraints based on KGs and heuristics to predict NIL entities (entities not represented in the KB of reference) results in better EL performance on historical documents.

Nowadays knowledge base KB has been viewed as one of the important infrastructures for many web search applications and NLP tasks. However, in practice the availability of KB data varies from language to language, which greatly limits potential usage of knowledge base. In this paper, we propose a novel method to construct or enrich a knowledge base by entity translation with help of another KB but compiled in a different language. In our work, we concentrate on two key tasks: 1 collecting translation candidates with as good coverage as possible from various sources such as web or lexicon; 2 building an effective disambiguation algorithm based on collective inference approach over knowledge graph to find correct translation for entities in the source knowledge base. We conduct experiments on movie domain of our in-house knowledge base from English to Chinese, and the results show the proposed method can achieve very high translation precision compared with classical translation methods, and significantly increase the volume of Chinese knowledge base in this domain.

Indoor fire is a sudden and frequent disaster that severely threatens the safety of indoor people worldwide. Indoor fire emergency evacuation is crucial to reducing losses involving various objects and complex relations. However, traditional studies only rely on numerical simulation, which cannot provide adequate support for decision-making in indoor fire scenarios. The knowledge graph is a knowledge base that can fully utilize massive heterogeneous data to form a sound knowledge system; however, it has not been effectively applied in the fire emergency domain. This study is a preliminary attempt to construct a knowledge graph for indoor fire emergency evacuation. We constructed the indoor fire domain ontology and proposed a four-tuple knowledge representation model. A knowledge graph was constructed with 1852 nodes and 2364 relations from 25 indoor fire events. The proposed method was tested for the case study of Henan Pingdingshan ‘5.25’ Fire Accident in China. Results show that the proposed knowledge representation model and the corresponding knowledge graph can represent complicated indoor fire events and support indoor fire emergency evacuation.