3 ms·
I tested OpenCYC three years ago while working for a startup that did semantic tagging & recommendation of text-based content. Essentially, we would take somet
by tbenst 12y ago
I tested OpenCYC three years ago while working for a startup that did semantic tagging & recommendation of text-based content.
Essentially, we would take something like:
Gov. Rick Perry has said he will no longer wear cowboy boots, which some believe is part of an attempt to soften his gunslinging image as he considers another run for president.
And map it to something more machine-readable:
"Gov." -> http://dbpedia.org/page/State_(polity) (10% confidence)[1]
"Rick Perry" -> http://dbpedia.org/page/Rick_Perry (95% confidence)
"Rick Perry" -> http://sw.opencyc.org/concept/Mx4rM7N6iOeUSpGar4HaqXF3zg (95% confidence)
"Cowboy boots" -> http://dbpedia.org/page/Cowboy_boot (75% confidence)[2]
"President" ->http://dbpedia.org/page/President (90% confidence)[3]
Let's dive into what you get from OpenCyc vs DBpedia (ontology sourced from Wikipedia).
DBpedia:
Tons of machine-readable information like Party, Alma Mater, birthday, spouse, etc. Extensive categorical links (dcterms:subject) like category:United_States_presidential_candidates,_2012.
OpenCyc:
Knows he's a politician affiliated with "Republican Party," "Democratic Party," "The Republican Party," and "The Democratic Party"
I highlight only one example, but this is all over the place. Opencyc has duplicate terms where dbpedia/wikipedia does not. Opencyc has far less information. Opencyc has more incorrect information.
This is inevitable when you consider the two approaches. Wikipedia has tens of thousands of people making connections and updating the resource, where opencyc relies more on scripts. Opencyc, and quite possibly Cyc, is already antiquated by Wikipedia.
[1] Note that the confidence was estimated from heuristics I wrote based on how the ontologies were put together
[2] OpenCyc typically does not have mappings for plurals, while wikipedia has a very convenient redirect system for string mappings
[3] OpenCyc matches President with 60 concepts. Too much noise to do anything with
- jfdixon 12y agoI am not surprised by your results. However, the system is more robust than your experience would indicate. OpenCyc is a subset of ResearchCyc, which itself is a subset of (Full)Cyc. OpenCyc is primarily used for mapping between ontologies. It contains 239k concepts from ResearchCyc, but only the basic rules for definitional relationships between them. These relationships include part/whole, disjointness, etc. You mention DBPedia as being superior for your purpose, but I would counter that the two are complementary. There is a mapping between DBPedia and OpenCyc within the Linking Open Data cloud. In fact, it was one of the first ontologies contributed to the W3C's LOD initiative[1][2]. The concepts in OpenCyc are rigorously organized from most general (e.g. Thing) to more specific (e.g. board game). Each concept may have specific instances (e.g. Yahtzee, Trivial Pursuit, Scrabble, etc.) These primitives all live within a custom Lisp, where they may be reasoned over. DBPedia's structure arises naturally from user activity. It is organized primarily by Wikipedia's category system and includes individual pages. Unlike Wikipedia, the Cyc project does not aim to contain every instance of a concept. The relationships between concepts are what matter. Once one knows that something belongs to a given Cyc concept, one can leverage the system's knowledge to reason about it. OpenCyc's reasoning capability is limited by a lack of assertions (facts and rules) -- ResearchCyc's is not. ResearchCyc contains over 5 million assertions not present in OpenCyc. (Things like: water is wet, a dog is a mammal, mammals have hair, etc.) It also contains Natural Language tools not present in OpenCyc: parsers, taggers and more. With these tools, one can go from natural language to a formal logic representation. Or, given a formal representation generate natural language. These capabilities exist today in real world applications[3][4]. [1] http://lod-cloud.net http://lod-cloud.net [2] http://lod-cloud.net/versions/2007-10-08/lod-cloud.png http://lod-cloud.net/versions/2007-10-08/lod-cloud.png [3] http://videolectures.net/coinplanetdataschool2011_witbrock_cyc/ http://videolectures.net/coinplanetdataschool2011_witbrock_c... [4] http://videolectures.net/coinactivess2010_witbrock_lkc/ http://videolectures.net/coinactivess2010_witbrock_lkc/