LinkedIn knowledge graph is a large knowledge base built upon "entities" on LinkedIn, such as members, jobs, titles, skills, companies, geographical locations, schools, etc. These entities and the relationships among them form the ontology of the professional world and are used by LinkedIn to enhance its recommender systems, search, monetization and consumer products, business and consumer analytics. Creating a large knowledge base is a big challenge. Web sites like Wikipedia and Freebase primarily rely on direct contributions from human volunteers. Other related work such as Google's Knowledge Vault and Microsoft's Satori focuses on automatically extracting facts from the Web by leveraging the data redundancy nature of big data for constructing knowledge bases.

Two years ago, our ambitious team of data scientists, engineers and visualisation experts set out to tackle the challenging problem of interconnecting the entire universe of research. Using this incredibly powerful knowledge graph, we aimed to provide breakthrough insights about the past and present of research, and by applying predictive techniques we sought to outline the future of research at a global scale. Using big data analytics, machine learning and artificial intelligence, our team worked determinedly for two years piecing together the world's most comprehensive and continuously updating knowledge graph. Today, we are excited to introduce wizdom.ai, Our goal is to utilise this powerful research graph, representing the collective knowledge of human civilisation to answer the most fundamental questions for researchers, research institutions, publishers, funding organisations, businesses and governments – explore the extensive range of questions addressed by our team on the wizdom.ai

GloVe is essentially a log-bilinear model with a weighted least-squares objective. The main intuition underlying the model is the simple observation that ratios of word-word co-occurrence probabilities have the potential for encoding some form of meaning. For example, consider the co-occurrence probabilities for target words ice and steam with various probe words from the vocabulary. As one might expect, ice co-occurs more frequently with solid than it does with gas, whereas steam co-occurs more frequently with gas than it does with solid. Both words co-occur with their shared property water frequently, and both co-occur with the unrelated word fashion infrequently.

SEO is a constant source of anxiety among business owners and marketers; especially in recent years, as Google has introduced and increased its focus on "contextual" search. The Knowledge Graph is one of the best examples, because it presents a tremendous opportunity for quicker and more detailed viewer engagement--but only if you know how it works and how to get yourself listed. With the above in mind, in this article we'll go over what the Knowledge Graph is, why it should matter to you, and how to best optimize your website for inclusion. The Knowledge Graph is a knowledge base used by Google. It was created in 2012 by Google so that it could better understand the world the way people do by using entity-based searches.

Despite the tremendous amount of information on Wikipedia, only a very small amount is structured. Most of the information is embedded in unstructured text and extracting it is a non trivial challenge. In this paper, we propose a full pipeline built on top of DeepDive to successfully extract meaningful relations from the Wikipedia text corpus. We evaluated the system by extracting company-founders and family relations from the text. As a result, we extracted more than 140,000 distinct relations with an average precision above 90%.

Sum-Product Networks (SPNs) are a class of expressive yet tractable hierarchical graphical models. LearnSPN is a structure learning algorithm for SPNs that uses hierarchical co-clustering to simultaneously identifying similar entities and similar features. The original LearnSPN algorithm assumes that all the variables are discrete and there is no missing data. We introduce a practical, simplified version of LearnSPN, MiniSPN, that runs faster and can handle missing data and heterogeneous features common in real applications. We demonstrate the performance of MiniSPN on standard benchmark datasets and on two datasets from Google's Knowledge Graph exhibiting high missingness rates and a mix of discrete and continuous features.

Methods for learning word representations using large text corpora have received much attention lately due to their impressive performancein numerous natural language processing (NLP) tasks such as, semantic similarity measurement, and word analogy detection.Despite their success, these data-driven word representation learning methods do not considerthe rich semantic relational structure between words in a co-occurring context. On the other hand, already much manual effort has gone into the construction of semantic lexicons such as the WordNetthat represent the meanings of words by defining the various relationships that exist among the words in a language.We consider the question, can we improve the word representations learnt using a corpora by integrating theknowledge from semantic lexicons?. For this purpose, we propose a joint word representation learning method that simultaneously predictsthe co-occurrences of two words in a sentence subject to the relational constrains given by the semantic lexicon.We use relations that exist between words in the lexicon to regularize the word representations learnt from the corpus.Our proposed method statistically significantly outperforms previously proposed methods for incorporating semantic lexicons into wordrepresentations on several benchmark datasets for semantic similarity and word analogy.

Knowledge graph embedding refers to projecting entities and relations in knowledge graph into continuous vector spaces. Current state-of-the-art models are translation-based model, which build embeddings by treating relation as translation from head entity to tail entity. However, previous models is too strict to model the complex and diverse entities and relations(e.g. symmetric/transitive/one-to-many/many-to-many relations). To address these issues, we propose a new principle to allow flexible translation between entity and relation vectors. We can design a novel score function to favor flexible translation for each translation-based models without increasing model complexity. To evaluate the proposed principle, we incorporate it into previous method and conduct triple classification on benchmark datasets. Experimental results show that the principle can remarkably improve the performance compared with several state-of-the-art baselines.

Knowledge Graphs with rich schemas can allow for complex querying. My thesis focuses on providing accessible Knowledge using Gricean notions of Cooperative Answering as a motivation. More specifically, using Query Reformulations, Data Awareness, and a Pragmatic Context, along with the results they can become more responsive to user requirements and user context.

Distributional hypothesis lies in the root of most existing word representation models by inferring word meaning from its external contexts. However, distributional models cannot handle rare and morphologically complex words very well and fail to identify some fine-grained linguistic regularity as they are ignoring the word forms. On the contrary, morphology points out that words are built from some basic units, i.e., morphemes. Therefore, the meaning and function of such rare words can be inferred from the words sharing the same morphemes, and many syntactic relations can be directly identified based on the word forms. However, the limitation of morphology is that it cannot infer the relationship between two words that do not share any morphemes. Considering the advantages and limitations of both approaches, we propose two novel models to build better word representations by modeling both external contexts and internal morphemes in a jointly predictive way, called BEING and SEING. These two models can also be extended to learn phrase representations according to the distributed morphology theory. We evaluate the proposed models on similarity tasks and analogy tasks. The results demonstrate that the proposed models can outperform state-of-the-art models significantly on both word and phrase representation learning.