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.

Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×

EKAW2016 Travel Report

From 19-24, November, I attended 20th International Conference on Knowledge Engineering and Knowledge Management at Bologna, Italy. It's a biannual conference on Knowledge Engineering along with the K-CAP conference.

There were around 150 participants from worldwide. Regarding submissions, there were 226 abstracts which resulted in 171 final submissions in total. 539 reviews were submitted for those papers and 42 out of 142 research papers have been accepted. Based on further quality assessment, the organizers also divided 42 papers into long presentations (17.3%) and short presentations for presentations during the conference.

Keynotes:

The first keynote was given by Chris Welty from Google research. He talked about how current AI systems are losing information with one label ground truth for training themselves (e.g, a song might be in different genres or not in the options you provided for getting ground truth data with a survey). He pointed out current simplified world for AI, which consists of black and white, while the reality is much complex. To achieve better ground truth labeling, he also introduced solutions such as using the wise crowd with diversity-enabled labeling for training AI systems.

The second keynote was given by Francesca Rossi from IBM research. She talked about AI has the capabilities to make sense of the huge volume of data (text, images, videos, etc.) that surrounds us in our everyday private and professional life, and to transform it into knowledge to be exploited to make better and more informed decisions that could help solving global societal problems such as those in healthcare, transportation, and climate. To achieve these goals, and in order to fully exploit the potential of AI, we need to build intelligent machines that behave ethically and create symbiotic partnerships with humans. So rather than considering/making AI for Decision Making Systems, we need to consider/make it as Decision Support Systems.

The conference sessions are very diverse, from data management to NLP as well as Entity Recognition, Crowdsourcing, ontology related topics etc.

My presentation:

I presented a User Modeling work considering different dimensions studied in the literature for investigating their synergetic effect on User Modeling.

EKAW2016 - Interest Representation, Enrichment, Dynamics, and Propagation: A Study of the Synergetic Effect of Different User Modeling Dimensions for Personalized Recommendations on Twitter from GUANGYUAN PIAO

The organizers also did a good job on proceedings, which were available before the conference:

• http://link.springer.com/book/10.1007/978-3-319-49004-5

Some thought from comparing search engines Google vs. Bing

By searching academic papers in two search engines, found totally different user experience. The first one is searching paper in Google, which will provide full paper name automatically before you input full title of the paper.

On the other hand, in bing, there's no suggestion for users at all.

Furthermore, in search result, you could see the citation information provided from Google with structured data "by author name" and "publish year".

Bing's result only provides the links to the paper without further structured information or citation information.

Definitely, Google's search experience is better than Bing considering both have academic papers' index system Google Scholar and Microsoft Academic Search separately. Google Scholar is more and more popular and getting better reputation over MAS even though MAS was better academic index system before, this is because more indexing and user experience are provided by Google Scholar nowadays (Some posts say that MAS is preparing new generation MAS). 

Back to the search experience, is this due to the Knowledge Graph initiated by Google at May 2012 ?? Is this the inclination of Google's better experience over Bing???

For the user's perspective, it is definitely better experience if search result contains more and more result that user seek about. The best case is that user could get all information at once with one search without follow the links to another website to get the information by themselves... For example, we could see the opening hours, contact, ratings and reviews while searching a restaurant with in search result. That means, Google is acting not only search engine but answering user's questions with background Knowledge Graph as well as information extracted from related websites.

Is it awesome, then what's the problem?? 

For the millions of companies who were paying bills for directing users to theirs websites, might be big concern with Google's better experience since it will reduce the clicks and reservation on many websites. Ben Gomes said the final goal is let users get the result they want immediately, no matter it is within Google or other websites. Even though, some vendors could be affected by it. Users now could use Google Map in smartphones with Uber App to call taxi but not provide links to other service provides with the same services like Lyft or Sidecar.

We could check the date of hotels on Google and even book rooms directly. This service is without any doubt is a threat to the service like Priceline or Expedia which is providing online booking service. Google was a gateway to provide tremendous traffics to many companies before, but it will be not simple relationships among Google and these companies anymore...

Reference:

谷歌上市10周年记：搜索结果的进化