Semantic Networks

The Open Data Institute (ODI) is one of the leaders in using data in the public sector. Sir Nigel Shadbolt from the ODI has been working on AI for many decades. Now that AI is in the limelight, let's not forget all the long years of foundational research and the effort that went into accumulating data that facilitates AI. In his keynote, Sir Nigel Shadbolt emphasized the importance of Linked Data as critical infrastructure.

Knowledge graph is a kind of valuable knowledge base which would benefit lots of AI-related applications. Up to now, lots of large-scale knowledge graphs have been built. However, most of them are non-Chinese and designed for general purpose. In this work, we introduce TechKG, a large scale Chinese knowledge graph that is technology-oriented. It is built automatically from massive technical papers that are published in Chinese academic journals of different research domains. Some carefully designed heuristic rules are used to extract high quality entities and relations. Totally, it comprises of over 260 million triplets that are built upon more than 52 million entities which come from 38 research domains. Our preliminary ex-periments indicate that TechKG has high adaptability and can be used as a dataset for many diverse AI-related applications. We released TechKG at: http://www.techkg.cn.

Best conceived of as a "company brain," this knowledge graph focuses on integrating an organization's assortment of people, skills, experiences, materials, essential company databases, and projects, which greatly improves its self-knowledge and thereby yields competitive advantage. Compiled from combing through myriad databases, including those for human resources, emails, and manifold other sources, this knowledge graph provides the foundation for a rapid, detailed assessment of what knowledge and skills a company has at its disposal--and their relation to one another. This graph is designed to create better services and is extremely specific to an organization's industry, line of business, and area of specialization. For example, Google's and Yahoo's search engine endeavors mandate that they collect knowledge about every entity or subject in the world, so they can offer the most relevant, revealing information to their users. LinkedIn's knowledge graph, on the other hand, details people's professions, resumes, and career opportunities.1 Again, the relationships between these nodes are paramount.

Concepts, which represent a group of different instances sharing common properties, are essential information in knowledge representation. Most conventional knowledge embedding methods encode both entities (concepts and instances) and relations as vectors in a low dimensional semantic space equally, ignoring the difference between concepts and instances. In this paper, we propose a novel knowledge graph embedding model named TransC by differentiating concepts and instances. Specifically, TransC encodes each concept in knowledge graph as a sphere and each instance as a vector in the same semantic space. We use the relative positions to model the relations between concepts and instances (i.e., instanceOf), and the relations between concepts and sub-concepts (i.e., subClassOf). We evaluate our model on both link prediction and triple classification tasks on the dataset based on YAGO. Experimental results show that TransC outperforms state-of-the-art methods, and captures the semantic transitivity for instanceOf and subClassOf relation. Our codes and datasets can be obtained from https:// github.com/davidlvxin/TransC.

With the increasing speed of technological advancements, the pharmaceutical and healthcare industry needs to break up departmental data silos with Knowledge Graphs and AI to understand the value of their data. At PhUSE EU Connect 2018 we will introduce the innovative approach of PoolParty Semantic Suite to make the most out of your data with semantic data integration. Our partner Findwise, global experts in search-driven solutions for the pharmaceutical and healthcare industry, presents a series of four blog posts to help you understand how knowledge graphs and AI can leverage your data-driven innovation and improve healthcare outcome. We face grand societal challenges pinned down in the 17 UN sustainability goals and specifically number 3 Good Health and Well-being. Humans live a longer life, which shifts the population pyramid.

In our previous post of this blog post series about knowledge graphs and AI in the pharmaceutical and healthcare industry, you got an overview of the challenges knowledge-intensive organizations face to be able to support data-driven innovation and improve healthcare outcome. In this blog post, you will learn by the hand of a use case about how connecting your siloed departmental data with external authoritative resources will leverage the value of your content assets. Visit us at PhUSE EU Connect 2018, where we will introduce the innovative approach of PoolParty Semantic Suite to make the most out of your data with semantic data integration. Stay tuned for upcoming blog posts that will help you understand how knowledge graphs and AI can leverage your organization. Our partner Findwise, global experts in search-driven solutions for the pharmaceutical and healthcare industry, are bringing all their expertise in information management and knowledge engineering into this blog post series.

Knowledge graph embedding aims at modeling entities and relations with low-dimensional vectors. Most previous methods require that all entities should be seen during training, which is unpractical for real-world knowledge graphs with new entities emerging on a daily basis. Recent efforts on this issue suggest training a neighborhood aggregator in conjunction with the conventional entity and relation embeddings, which may help embed new entities inductively via their existing neighbors. However, their neighborhood aggregators neglect the unordered and unequal natures of an entity's neighbors. To this end, we summarize the desired properties that may lead to effective neighborhood aggregators. We also introduce a novel aggregator, namely, Logic Attention Network (LAN), which addresses the properties by aggregating neighbors with both rules- and network-based attention weights. By comparing with conventional aggregators on two knowledge graph completion tasks, we experimentally validate LAN's superiority in terms of the desired properties.

This paper introduces TakeFive, a new semantic role labeling method that transforms a text into a frame-oriented knowledge graph. It performs dependency parsing, identifies the words that evoke lexical frames, locates the roles and fillers for each frame, runs coercion techniques, and formalises the results as a knowledge graph. This formal representation complies with the frame semantics used in Framester, a factual-linguistic linked data resource. The obtained precision, recall and F1 values indicate that TakeFive is competitive with other existing methods such as SEMAFOR, Pikes, PathLSTM and FRED. We finally discuss how to combine TakeFive and FRED, obtaining higher values of precision, recall and F1. Keywords: Semantic Role Labeling, Frame Semantics, Framester, Dependency Parsing, Role Oriented Knowledge Graphs 1. Introduction Most knowledge in linked data and knowledge graphs is of a relational nature: people participating in events, products having prices, artifacts with parts, works of art produced by artists, beers sold at a bar, etc. For that reason, a good part of integration and interoperability ends up consisting in aligning relations among heterogeneous schemas and data. This limit makes interoperability difficult.

Many knowledge graph embedding methods operate on triples and are therefore implicitly limited by a very local view of the entire knowledge graph. We present a new framework MOHONE to effectively model higher order network effects in knowledge-graphs, thus enabling one to capture varying degrees of network connectivity (from the local to the global). Our framework is generic, explicitly models the network scale, and captures two different aspects of similarity in networks: (a) shared local neighborhood and (b) structural role-based similarity. First, we introduce methods that learn network representations of entities in the knowledge graph capturing these varied aspects of similarity. We then propose a fast, efficient method to incorporate the information captured by these network representations into existing knowledge graph embeddings. We show that our method consistently and significantly improves the performance on link prediction of several different knowledge-graph embedding methods including TRANSE, TRANSD, DISTMULT, and COMPLEX(by at least 4 points or 17% in some cases).

Knowledge graph (KG) completion aims to fill the missing facts in a KG, where a fact is represented as a triple in the form of $(subject, relation, object)$. Current KG completion models compel two-thirds of a triple provided (e.g., $subject$ and $relation$) to predict the remaining one. In this paper, we propose a new model, which uses a KG-specific multi-layer recurrent neutral network (RNN) to model triples in a KG as sequences. It outperformed several state-of-the-art KG completion models on the conventional entity prediction task for many evaluation metrics, based on two benchmark datasets and a more difficult dataset. Furthermore, our model is enabled by the sequential characteristic and thus capable of predicting the whole triples only given one entity. Our experiments demonstrated that our model achieved promising performance on this new triple prediction task.