Perform logical reasoning: traverse the graphs in a path to make logical connections (A’s father is B and B’s father is C, hence C is the grandfather of A) A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E.R. Hruschka, T.M. Mitchell, Toward an architecture for never-ending language learning, in Proceedings of the 24th Conference on Artificial Intelligence (AAAI2010), 11–15 July 2010 (AAAI Press, Atlanta) Hoffart, J., Suchanek, F.M., Berberich, K., Weikum, G.: YAGO2: a spatially and temporally enhanced knowledge base from Wikipedia. Artif. Intell. J. (2012)

H. Paulheim, Machine learning with and for Semantic Web knowledge graphs, ed. by C. d’Amato, M. Theobald, in Proceedings of the 14th International Summer School 2018: Reasoning Web. Learning, Uncertainty, Streaming, and Scalability: Tutorial Lectures, Esch-sur-Alzette, Luxembourg, 22–26 September 2018a. Springer LNCS, vol. 11078 Knowledge graph immediately appeared as the best option, which would lead me to additional insights and gain wisdom. Facts creation: this is the first step where we parse the text (sentence by sentence) and extract facts in triplet format like . As we are processing text, we can leverage pre-processing steps like tokenization, stemming, or lemmatization, etc to clean the text. Next, we want to extract the entities and relations (facts) from the text. For entities, we can use Named entity recognition (NER) algorithms. For relation, we can use sentence dependency parsing techniques to find the relationship between any pair of entities. Example article with code. There are two types of databases that can be used to store graphical information. The first is “property graphs” like Neo4j and OrientDB that does not support RDF file (out of the box) and have their own custom query language. On the other hand, we have “RDF triplet stores”, that support RDF files and support query language like SPARQL that is universally used to query KG. Some of the most famous ones are (with open source version),S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, Z.G. Ives, DBpedia: a nucleus for a web of open data, in Proceedings of the 6th International Semantic Web Conference (ISWC2007), 2nd Asian Semantic Web Conference, (ASWC2007), Busan, Korea, 11–15 November 2007. Springer LNCS, vol. 4825 If you’re a developer, you will normally build applications that consume the information in the knowledge graph. For you, a knowledge graph is a database with which you’ll interact through some form of API offering you structural primitives, like “For a given item A, retrieve all other items related to it,” “Is there a direct or indirect connection between items A and B? If so, how many different ones exist?” or “What is the most significant path connecting items A and B?” Richer knowledge graphs will offer pattern-based query languages like Cypher, GQL, or SPARQL, but simpler ones may offer more basic interfaces, for example, a method returning all related items for a given one.

E.F. Codd, A relational model of data for large shared data banks. Commun. ACM 13(6), 377–387 (1970) With the increasing interest in knowledge graph over the years, several approaches have been proposed for building knowledge graphs. Most of the recent approaches involve using semi-structured sources such as Wikipedia or information crawled from the web using a combination of extraction methods and Natural Language Processing (NLP) techniques. In most cases, these approaches tend to make a compromise between accuracy and completeness. In our ongoing work, we examine a technique for building a knowledge graph over the increasing volume of open data published on the web. The rationale for this is two-fold. First, we intend to provide a foundation for making existing open datasets searchable through keywords similar to how information is sought on the web. The second reason is to generate logically consistent facts from usually inaccurate and inconsistent open datasets. Our approach to knowledge graph development will compute the confidence score of every relationship elicited from underpinning open data in the knowledge graph. Our method will also provide a scheme for extending coverage of a knowledge graph by predicting new relationships that are not in the knowledge graph. In our opinion, our work has major implications for truly opening up access to the hitherto untapped value in open datasets not directly accessible on the World Wide Web today. Keywords I started out with a simple set of concepts. It grew over time and is still growing. I started to get into the actual products that companies sell and into the financials that gives me a perspective of how profitable some of these drugs are. Then I also wanted to get into the biology and the chemistry more, and started adding bioprocess or biological structure, in addition to the target, the molecule, and the disease. So the graph is still growing as it is, as more data becomes available and as additional questions may come to the foreground. In this section, we will introduce KG by asking some simple but intuitive questions about KG. In fact, we will cover the what, why, and how of the knowledge graph. We will also go through some real-world examples. What is a Knowledge graph?D. Fensel, M.A. Musen, The Semantic Web: a brain for humankind. IEEE Intell. Syst. 16(2), 24–25 (2001) Dong, X., et al.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2014) World Travel & Tourism Council, Travel & Tourism Economic Impact 2018 World (2018). https://www.wttc.org/-/media/files/reports/economic-impact-research/regions-2018/world2018.pdf Which companies are working on therapeutic molecules that act on a specific molecular target, and which diseases are they targeting? J. Z. Pan, G. Vetere, J. M. Gómez-Pérez, H. Wu (eds.), Exploiting Linked Data and Knowledge Graphs in Large Organisations (Springer, Cham, 2017b)

PDF / EPUB File Name: Building_Knowledge_Graphs_-_Jesus_Barrasa.pdf, Building_Knowledge_Graphs_-_Jesus_Barrasa.epub M.K. Bergman, A Knowledge Representation Practionary—Guidelines Based on Charles Sanders Peirce (Springer, Cham, 2018)H. Ehrig, C. Ermel, U. Golas, F. Hermann, Graph and Model Transformation: General Framework and Applications (Springer, Berlin, 2015) R. Angles, C. Gutiérrez, Querying RDF data from a graph database perspective, in Proceedings of the 2nd European Semantic Web Conference (ESWC2005), Heraklion, Greece, 29 May–1 June 2005. Springer LNCS, vol. 3532 Avoiding drowning in this ever-increasing data deluge is a serious challenge, but not an insurmountable one, according to a new book, Building Knowledge Graphs: A Practitioner’s Guide, published by our good friends O’Reilly. The tome’s authors, Jesús Barrasa and Dr. Jim Webber, argue that “all is not lost” because a new category of technology, based on graphs, can help extract real value from what would otherwise be an unmanageable data tsunami. This is the most general definition I could think of, and because of its generality, it will probably leave you unsatisfied, so here are some more refined ones depending on who you are: P. Hayes, The Logic of Frames, Readings in Artificial Intelligence (Morgan Kaufmann, Los Altos, CA, 1981)