Knowledge Graphs at Elsevier

Pecha Kucha slides for 2015 World Wide Web Conference Industry Track panel on knowledge graphs in industry

Presentation of the Semantic Knowledge Graph research paper at the 2016 IEEE 3rd International Conference on Data Science and Advanced Analytics (Montreal, Canada - October 18th, 2016) Abstract—This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain.

Structured Data & the Future of Educational Material

Sources of Change in Modern Knowledge Organization Systems

Building and Using a Knowledge Graph to Combat Human Trafficking

Craig Knoblock

Enterprise knowledge graphs

Sören Auer

Searching on Intent: Knowledge Graphs, Personalization, and Contextual Disamb...

Trey Grainger

Search engines frequently miss the mark when it comes to understanding user intent. This talk will walk through some of the key building blocks necessary to turn a search engine into a dynamically-learning "intent engine", able to interpret and search on meaning, not just keywords. We will walk through CareerBuilder's semantic search architecture, including semantic autocomplete, query and document interpretation, probabilistic query parsing, automatic taxonomy discovery, keyword disambiguation, and personalization based upon user context/behavior. We will also see how to leverage an inverted index (Lucene/Solr) as a knowledge graph that can be used as a dynamic ontology to extract phrases, understand and weight the semantic relationships between those phrases and known entities, and expand the query to include those additional conceptual relationships. As an example, most search engines completely miss the mark at parsing a query like (Senior Java Developer Portland, OR Hadoop). We will show how to dynamically understand that "senior" designates an experience level, that "java developer" is a job title related to "software engineering", that "portland, or" is a city with a specific geographical boundary (as opposed to a keyword followed by a boolean operator), and that "hadoop" is the skill "Apache Hadoop", which is also related to other terms like "hbase", "hive", and "map/reduce". We will discuss how to train the search engine to parse the query into this intended understanding and how to reflect this understanding to the end user to provide an insightful, augmented search experience. Topics: Semantic Search, Apache Solr, Finite State Transducers, Probabilistic Query Parsing, Bayes Theorem, Augmented Search, Recommendations, Query Disambiguation, NLP, Knowledge Graphs

Enterprise Knowledge Graph

Lukas Masuch

The Enterprise Knowledge Graph is a disruptive platform that combines emerging Big Data and Graph technologies to reinvent knowledge management inside organizations. This platform aims to organize and distribute the organization’s knowledge, and making it centralized and universally accessible to every employee. The Enterprise Knowledge Graph is a central place to structure, simplify and connect the knowledge of an organization. By removing complexity, the knowledge graph brings more transparency, openness and simplicity into organizations. That leads to democratized communications and empowers individuals to share knowledge and to make decisions based on comprehensive knowledge. This platform can change the way we work, challenge the traditional hierarchical approach to get work done and help to unleash human potential!

We live in an era where the world is more connected than ever before and the trajectory is such that data relationships will only continue to increase with no signs of slowing down. Connected data is the key to your business succeeding and growing in today’s connected world. Leading enterprises will be the ones that utilize relationship-centric technologies to leverage connections from their internal operations and supply chain to their customer and user interactions. This ability to utilize connected data to understand all the nuanced relationships within their organization will propel them forward as they act on more holistic insights. Every organization needs a knowledge graph because connected data is an essential foundation to advancing business. Additional reading on connected can be found here: https://www.graphgrid.com/why-connected-data-is-more-useful/

Altmetrics: painting a broader picture of impact

"Don't Publish, Release" - Revisited

Transparency in the Data Supply Chain

Domains such as drug discovery, data science, and policy studies increasing rely on the combination of complex analysis pipelines with integrated data sources to come to conclusions. A key question then arises is what are these conclusions based upon? Thus, there is a tension between integrating data for analysis and understanding where that data comes from (its provenance). In this talk, I describe recent work that is attempting to facilitate transparency by combining provenance tracked within databases with the data integration and analytics pipelines that feed them. I discuss this with respect to use cases from public policy as well as drug discovery. Given at: http://ccct.uva.nl/content/ccct-seminar-21-february-2014

Data Integration vs Transparency: Tackling the tension

Telling your research story with (alt)metrics

Data for Science: How Elsevier is using data science to empower researchers

Each month 12 million people use Elsevier’s ScienceDirect platform. The Mendeley social network has 4.6 million registered users. 3500 institutions make use of ClinicalKey to bring the latest in medical research to doctors and nurses. How can we help these users be more effective? In this talk, I give an overview of how Elsevier is employing data science to improve its services from recommendation systems, to natural language processing and analytics. While data science is changing how Elsevier serves researchers, it’s also changing research practice itself. In that context, I discuss the impact that large amounts of open research data are having and the challenges researchers face in making use of it, in particular, in terms of data integration and reuse. We are at just beginning to see of how technology and data is changing science correspondingly this impacts how best to empower those who practice it.

Information architecture at Elsevier

Decoupling Provenance Capture and Analysis from Execution

Research Data Sharing: A Basic Framework

Open PHACTS API Walkthrough

Tradeoffs in Automatic Provenance Capture

Provenance for Data Munging Environments

Data munging is a crucial task across domains ranging from drug discovery and policy studies to data science. Indeed, it has been reported that data munging accounts for 60% of the time spent in data analysis. Because data munging involves a wide variety of tasks using data from multiple sources, it often becomes difficult to understand how a cleaned dataset was actually produced (i.e. its provenance). In this talk, I discuss our recent work on tracking data provenance within desktop systems, which addresses problems of efficient and fine grained capture. I also describe our work on scalable provence tracking within a triple store/graph database that supports messy web data. Finally, I briefly touch on whether we will move from adhoc data munging approaches to more declarative knowledge representation languages such as Probabilistic Soft Logic. Presented at Information Sciences Institute - August 13, 2015

The Narrative Structure of Research Articles, or, Why Science is Like a Fairy...

Charleston Conference 2016

Sumeet vij enterprise_knowledge_graph

Open Analytics

Freebase and the semantic web

spencermountain

Aleph

Bekas for cognitive_speaker_series

TAO: Facebook's Distributed Data Store for the Social Graph

Adrian-Tudor Panescu

To Graph or Not to Graph Knowledge Graph Architectures and LLMs

Data Curation and Debugging for Data Centric AI

It is increasingly recognized that data is a central challenge for AI systems - whether training an entirely new model, discovering data for a model, or applying an existing model to new data. Given this centrality of data, there is need to provide new tools that are able to help data teams create, curate and debug datasets in the context of complex machine learning pipelines. In this talk, I outline the underlying challenges for data debugging and curation in these environments. I then discuss our recent research that both takes advantage of ML to improve datasets but also uses core database techniques for debugging in such complex ML pipelines. Presented at DBML 2022 at ICDE - https://www.wis.ewi.tudelft.nl/dbml2022

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More from Paul Groth

To Graph or Not to Graph Knowledge Graph Architectures and LLMs

Data Curation and Debugging for Data Centric AI

Content + Signals: The value of the entire data estate for machine learning

Content-centric organizations have increasingly recognized the value of their material for analytics and decision support systems based on machine learning. However, as anyone involved in machine learning projects will tell you the difficulty is not in the provision of the content itself but in the production of annotations necessary to make use of that content for ML. The transformation of content into training data often requires manual human annotation. This is expensive particularly when the nature of the content requires subject matter experts to be involved. In this talk, I highlight emerging approaches to tackling this challenge using what's known as weak supervision - using other signals to help annotate data. I discuss how content companies often overlook resources that they have in-house to provide these signals. I aim to show how looking at a data estate in terms of signals can amplify its value for artificial intelligence.

Data Communities - reusable data in and outside your organization.

Description Data is a critical both to facilitate an organization and as a product. How can you make that data more usable for both internal and external stakeholders? There are a myriad of recommendations, advice, and strictures about what data providers should do to facilitate data (re)use. It can be overwhelming. Based on recent empirical work (analyzing data reuse proxies at scale, understanding data sensemaking and looking at how researchers search for data), I talk about what practices are a good place to start for helping others to reuse your data. I put this in the context of the notion data communities that organizations can use to help foster the use of data both within your organization and externally.

Minimal viable-datareuse-czi

The literature contains a myriad of recommendations, advice, and strictures about what data providers should do to facilitate data reuse. It can be overwhelming. Based on recent empirical work (analyzing data reuse proxies at scale, understanding data sensemaking and looking at how researchers search for data), I talk about what practices are a good place to start for helping others to reuse your data.

Knowledge Graph Maintenance

Presentation for NEC Lab Europe. Knowledge graphs are increasingly built using complex multifaceted machine learning-based systems relying on a wide of different data sources. To be effective these must constantly evolve and thus be maintained. I present work on combining knowledge graph construction (e.g. information extraction) and refinement (e.g. link prediction) in end to end systems. In particular, I will discuss recent work on using inductive representations for link predication. I then discuss the challenges of ongoing system maintenance, knowledge graph quality and traceability.

Knowledge Graph Futures

Knowledge Graph Maintenance

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The Challenge of Deeper Knowledge Graphs for Science

Over the past 5 years, we have seen multiple successes in the development of knowledge graphs for supporting science in domains ranging from drug discovery to social science. However, in order to really improve scientific productivity, we need to expand and deepen our knowledge graphs. To do so, I believe we need to address two critical challenges: 1) dealing with low resource domains; and 2) improving quality. In this talk, I describe these challenges in detail and discuss some efforts to overcome them through the application of techniques such as unsupervised learning; the use of non-experts in expert domains, and the integration of action-oriented knowledge (i.e. experiments) into knowledge graphs.

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Diversity and Depth: Implementing AI across many long tail domains

Presentation at the IJCAI 2018 Industry Day Elsevier serves researchers, doctors, and nurses. They have come to expect the same AI based services that they use in everyday life in their work environment, e.g.: recommendations, answer driven search, and summarized information. However, providing these sorts of services over the plethora of low resource domains that characterize science and medicine is a challenging proposition. (For example, most of the shelf NLP components are trained on newspaper corpora and exhibit much worse performance on scientific text). Furthermore, the level of precision expected in these domains is quite high. In this talk, we overview our efforts to overcome this challenge through the application of four techniques: 1) unsupervised learning; 2) leveraging of highly skilled but low volume expert annotators; 2) designing annotation tasks for non-experts in expert domains; and 4) transfer learning. We conclude with a series of open issues for the AI community stemming from our experience.

Progressive Provenance Capture Through Re-computation

Provenance capture relies upon instrumentation of processes (e.g. probes or extensive logging). The more instrumentation we can add to processes the richer our provenance traces can be, for example, through the addition of comprehensive descriptions of steps performed, mapping to higher levels of abstraction through ontologies, or distinguishing between automated or user actions. However, this instrumentation has costs in terms of capture time/overhead and it can be difficult to ascertain what should be instrumented upfront. In this talk, I'll discuss our research on using record-replay technology within virtual machines to incrementally add additional provenance instrumentation by replaying computations after the fact.

From Text to Data to the World: The Future of Knowledge Graphs

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