Differences between Knolwedge Base, Knowledge Graphs, and Ontology

The terms "knowledge base" and "knowledge graphs" have gained a lot of popularity recent years, especially after Google's introduction about the Google Knowledge Graph. However, those terms have been used interchangeably and there has been lacking a good definition for distinguishing those terms. 

Recently, the SEMANTiCS paper Towards a Definition of Knowledge Graphs from Ehrlinger, Lisa [2] provides a quite comprehensive analysis on those terms and a good definition about knowledge graph to define the distinction and relationships between those terms, which are quite useful.

"The knowledge base is a dataset with formal semantics that can contain different kinds of knowledge, for example, rules, facts, axioms, definitions, statements, and primitives" [1]

 "A knowledge graph acquires and integrates information into an ontology (or knowledge base) and applies a reasoner to derive new knowledge."

More recently,   Auer, Sören et al. relaxed the definition a little bit by any method instead of a reasoner when deriving new knowledge. That is, 

 "A knowledge graph acquires and integrates information into an ontology (or knowledge base) and applies a reasoner or other computaitonal methods to derive new knowledge."

This definition aligns with the assumption that a knowledge graph is somehow superior and more complex than a knowledge base (e.g., an ontology) because it applies a reasoning engine to generate new knowledge and integrates one or more information sources. Consequently, a manually created knowledge graph that does not support integration aspects is a plain knowledge base or knowledge-based system if it provides reasoning capabilities. 

It is also interesting to note for me that an ontology consists not only of classes and properties (e.g., owl:ObjectProperty and owl:DatatypeProperty), but can also hold instances (i.e., the population of the ontology). 

[1]. J. Davies, R. Studer, and P. Warren. Semantic Web Technologies: Trends and Research in Ontology-based Systems. John Wiley & Sons, 2006.

[2]. Ehrlinger, Lisa and Wolfram Wöß. Towards a Definition of Knowledge Graphs. SEMANTiCS conference, 2016.

[3]. Auer, Sören et al. Towards a knowledge graph for science, International Conference on Web Intelligence, Mining and Semantics, 2018.

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SEMANTiCS 2016 Travel Report

Day-1: Tutorials & Workshops

I attended the afternoon session about Knowledge Engineering track using PoolParty from Semantic Web Company. I'm interested in how those Semantic Technologies being used in different enterprises, and what kind of solutions they need for soloving what kinds of problems. There were many industrial participants in Europe including Springer etc. As a researcher working closely on Semantic Technologies,  Some told they are already using PoolParty and some were attending for better understanding of using Semantic Technologies in Enterprise scenarios, and most of the cases were wondering about integration of heterogeneous data sources, taxonomies and ontologies.



Day-2: Main conference

Stats: This year's conference received 85 submissions with 18 full papers(21.2%) and 8 short papers.

The first keynote: "Linked data experience at Springer Nature" by Michele Pasin

Dr. Michele talked about a summary of Springer's experience with Linked Data & Semantic Technologies for enterprise metadata management at large scale. He also introduced scigraph.com - a upcoming LD platform: one place for their all linked data efforts towards liked science data.

The second keynote: "The semantics of human network" by Marie Wallace, IBM

Marie from IBM shared their experience of using human network which generated by their enterprise social networks using IBM connections for different applications and services. She stressed that capturing human context at a global level, which is happening thanks to the social networks and IoT enabled world, is really important to help human digital experience.

These social dashboards for each employee shows different factors such as activity, reaction etc. of your personal social status and can also provide some recommendations for your improvements in different aspects.

I presented my full paper: "Exploring Dynamics and Semantics of User Interests for User Modeling on Twitter for Link Recommendations" in the Knowledge Discovery session. It is impressive to see the room was full of audiences and had interesting discussion with some audiences. This work also won the best paper award at #semanticsconf.

Day-3: Main conference

The first keynote: "Learning with Memory Embeddings and its Application in the Digitalization of Healthcare" by Volker Tresp from SIEMENS

He talked about mapping of the knowledge graph to a tensor representation whose entries are predicted by models using latent representations of generalized entities, and extension of this approach for medical decision processes.

The second keynote: "Enriching Content with User Data and Semantic Information" by Cathy Dolbear from Oxford Press

She talked about combining human-authored semantic information with semantic tags and taxonomy classifications automatically extracted from our content. She also introduce the Oxford Global Languages project, which links lexical information from multiple global and also digitally under-represented  languages such as isiZulu and Urdu in a triple store.

It was a wonderful event which can meet industry people who are dealing with real-world problems with Semantic Technologies, as well as academic researchers. Hope to attend the conference again in the future:)