AsdKB: A Chinese Knowledge Base for the Early Screening and Diagnosis of Autism Spectrum Disorder

To easily obtain the knowledge about autism spectrum disorder and help its early screening and diagnosis, we create AsdKB, a Chinese knowledge base on autism spectrum disorder. The knowledge base is built on top of various sources, including 1) the disease knowledge from SNOMED CT and ICD-10 clinical descriptions on mental and behavioural disorders, 2) the diagnostic knowledge from DSM-5 and different screening tools recommended by social organizations and medical institutes, and 3) the expert knowledge on professional physicians and hospitals from the Web. AsdKB contains both ontological and factual knowledge, and is accessible as Linked Data at https://w3id.org/asdkb/. The potential applications of AsdKB are question answering, auxiliary diagnosis, and expert recommendation, and we illustrate them with a prototype which can be accessed at http://asdkb.org.cn/.Comment: 17 pages, Accepted by the Resource Track of ISWC 202

Canonicalizing Knowledge Base Literals

Ontology-based knowledge bases (KBs) like DBpedia are very valuable resources, but their usefulness and usability is limited by various quality issues. One such issue is the use of string literals instead of semantically typed entities. In this paper we study the automated canonicalization of such literals, i.e., replacing the literal with an existing entity from the KB or with a new entity that is typed using classes from the KB. We propose a framework that combines both reasoning and machine learning in order to predict the relevant entities and types, and we evaluate this framework against state-of-the-art baselines for both semantic typing and entity matching

A comparative study of Chinese and European Internet companies' privacy policy based on knowledge graph

Privacy policy is not only a means of industry self-discipline, but also a way for users to protect their online privacy. The European Union (EU) promulgated the General Data Protection Regulation (GDPR) on May 25th, 2018, while China has no explicit personal data protection law. Based on knowledge graph, this thesis makes a comparative analysis of the Chinese and European Internet companies’ privacy policies, and combines with the relevant provisions of GDPR, puts forward suggestions on the privacy policy of Internet companies, so as to solve the problem of personal in-formation protection to a certain extent. Firstly, this thesis chooses the process and methods of knowledge graph construction and analysis. The process of constructing and analyzing the knowledge graph is: data preprocessing, entity extraction, storage in graph database and query. Data preprocessing includes word segmentation and part-of-speech tagging, as well as text format adjustment. Entity extraction is the core of knowledge graph construction in this thesis. Based on the principle of Conditional Random Fields (CRF), CFR++ toolkit is used for the entity extraction. Subsequently, the extracted entities are transformed into “.csv” format and stored in the graph database Neo4j, so the knowledge graph is generated. Cypher query statements can be used to query information in the graph database. The next part is about comparison and analysis of the Internet companies’ privacy policies in China and Europe. After sampling, the overall characteristics of the privacy policies of Chinese and European Internet companies are compared. According to the process of constructing knowledge graphs mentioned above, the “collected information” and “contact us” parts of the privacy policy are used to construct the knowledge graphs. Finally, combined with the relevant content of GDPR, the results of the comparative analysis are further discussed, and suggestions are proposed. Although Chinese Internet companies’ privacy policies have some merits, they are far inferior to those of European Internet companies. China also needs to enact a personal data protection law according to its national conditions. This thesis applies knowledge graph to the privacy policy research, and analyses Internet companies’ privacy policies from a comparative perspective. It also discusses the comparative results with GDPR and puts forward suggestions, and provides reference for the formulation of China's personal information protection law

Towards a Large Corpus of Richly Annotated Web Tables for Knowledge Base Population

Ell B, Hakimov S, Braukmann P, et al. Towards a Large Corpus of Richly Annotated Web Tables for Knowledge Base Population. Presented at the Fifth international workshop on Linked Data for Information Extraction (LD4IE) at ISWC2017, Vienna.Web Table Understanding in the context of Knowledge Base Population and the Semantic Web is the task of i) linking the content of tables retrieved from the Web to an RDF knowledge base, ii) of building hypotheses about the tables' structures and contents, iii) of extracting novel information from these tables, and iv) of adding this new information to a knowledge base. Knowledge Base Population has gained more and more interest in the last years due to the increased demand in large knowledge graphs which became relevant for Artificial Intelligence applications such as Question Answering and Semantic Search. In this paper we describe a set of basic tasks which are relevant for Web Table Understanding in the mentioned context. These tasks incrementally enrich a table with hypotheses about the table's content. In doing so, in the case of multiple interpretations, selecting one interpretation and thus deciding against other interpretations is avoided as much as possible. By postponing these decision, we enable table understanding approaches to decide by themselves, thus increasing the usability of the annotated table data. We present statistics from analyzing and annotating 1.000.000 tables from the Web Table Corpus 2015 and make this dataset as well as our code available online

Wiktionary: The Metalexicographic and the Natural Language Processing Perspective

Dictionaries are the main reference works for our understanding of language. They are used by humans and likewise by computational methods. So far, the compilation of dictionaries has almost exclusively been the profession of expert lexicographers. The ease of collaboration on the Web and the rising initiatives of collecting open-licensed knowledge, such as in Wikipedia, caused a new type of dictionary that is voluntarily created by large communities of Web users. This collaborative construction approach presents a new paradigm for lexicography that poses new research questions to dictionary research on the one hand and provides a very valuable knowledge source for natural language processing applications on the other hand. The subject of our research is Wiktionary, which is currently the largest collaboratively constructed dictionary project. In the first part of this thesis, we study Wiktionary from the metalexicographic perspective. Metalexicography is the scientific study of lexicography including the analysis and criticism of dictionaries and lexicographic processes. To this end, we discuss three contributions related to this area of research: (i) We first provide a detailed analysis of Wiktionary and its various language editions and dictionary structures. (ii) We then analyze the collaborative construction process of Wiktionary. Our results show that the traditional phases of the lexicographic process do not apply well to Wiktionary, which is why we propose a novel process description that is based on the frequent and continual revision and discussion of the dictionary articles and the lexicographic instructions. (iii) We perform a large-scale quantitative comparison of Wiktionary and a number of other dictionaries regarding the covered languages, lexical entries, word senses, pragmatic labels, lexical relations, and translations. We conclude the metalexicographic perspective by finding that the collaborative Wiktionary is not an appropriate replacement for expert-built dictionaries due to its inconsistencies, quality flaws, one-fits-all-approach, and strong dependence on expert-built dictionaries. However, Wiktionary's rapid and continual growth, its high coverage of languages, newly coined words, domain-specific vocabulary and non-standard language varieties, as well as the kind of evidence based on the authors' intuition provide promising opportunities for both lexicography and natural language processing. In particular, we find that Wiktionary and expert-built wordnets and thesauri contain largely complementary entries. In the second part of the thesis, we study Wiktionary from the natural language processing perspective with the aim of making available its linguistic knowledge for computational applications. Such applications require vast amounts of structured data with high quality. Expert-built resources have been found to suffer from insufficient coverage and high construction and maintenance cost, whereas fully automatic extraction from corpora or the Web often yields resources of limited quality. Collaboratively built encyclopedias present a viable solution, but do not cover well linguistically oriented knowledge as it is found in dictionaries. That is why we propose extracting linguistic knowledge from Wiktionary, which we achieve by the following three main contributions: (i) We propose the novel multilingual ontology OntoWiktionary that is created by extracting and harmonizing the weakly structured dictionary articles in Wiktionary. A particular challenge in this process is the ambiguity of semantic relations and translations, which we resolve by automatic word sense disambiguation methods. (ii) We automatically align Wiktionary with WordNet 3.0 at the word sense level. The largely complementary information from the two dictionaries yields an aligned resource with higher coverage and an enriched representation of word senses. (iii) We represent Wiktionary according to the ISO standard Lexical Markup Framework, which we adapt to the peculiarities of collaborative dictionaries. This standardized representation is of great importance for fostering the interoperability of resources and hence the dissemination of Wiktionary-based research. To this end, our work presents a foundational step towards the large-scale integrated resource UBY, which facilitates a unified access to a number of standardized dictionaries by means of a shared web interface for human users and an application programming interface for natural language processing applications. A user can, in particular, switch between and combine information from Wiktionary and other dictionaries without completely changing the software. Our final resource and the accompanying datasets and software are publicly available and can be employed for multiple different natural language processing applications. It particularly fills the gap between the small expert-built wordnets and the large amount of encyclopedic knowledge from Wikipedia. We provide a survey of previous works utilizing Wiktionary, and we exemplify the usefulness of our work in two case studies on measuring verb similarity and detecting cross-lingual marketing blunders, which make use of our Wiktionary-based resource and the results of our metalexicographic study. We conclude the thesis by emphasizing the usefulness of collaborative dictionaries when being combined with expert-built resources, which bears much unused potential

Sharing Semantic Resources

The Semantic Web is an extension of the current Web in which information, so far created for human consumption, becomes machine readable, “enabling computers and people to work in cooperation”. To turn into reality this vision several challenges are still open among which the most important is to share meaning formally represented with ontologies or more generally with semantic resources. This Semantic Web long-term goal has many convergences with the activities in the field of Human Language Technology and in particular in the development of Natural Language Processing applications where there is a great need of multilingual lexical resources. For instance, one of the most important lexical resources, WordNet, is also commonly regarded and used as an ontology. Nowadays, another important phenomenon is represented by the explosion of social collaboration, and Wikipedia, the largest encyclopedia in the world, is object of research as an up to date omni comprehensive semantic resource. The main topic of this thesis is the management and exploitation of semantic resources in a collaborative way, trying to use the already available resources as Wikipedia and Wordnet. This work presents a general environment able to turn into reality the vision of shared and distributed semantic resources and describes a distributed three-layer architecture to enable a rapid prototyping of cooperative applications for developing semantic resources

Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources

The manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi-)automatic ontology generation from input data such as domain text. This thesis proposes a novel approach for learning labels of non-taxonomic ontology relations. It combines corpus-based techniques with reasoning on Semantic Web data. Corpus-based methods apply vector space similarity of verbs co-occurring with labeled and unlabeled relations to calculate relation label suggestions from a set of candidates. A meta ontology in combination with Semantic Web sources such as DBpedia and OpenCyc allows reasoning to improve the suggested labels. An extensive formal evaluation demonstrates the superior accuracy of the presented hybrid approach