Heuristic ranking in tightly coupled probabilistic description logics
The Semantic Web effort has steadily been gaining traction in the recent years. In particular,Web search companies are recently realizing that their products need to evolve towards having richer semantic search capabilities. Description logics (DLs) have been adopted as the formal underpinnings for...
| Main Authors: | , , , |
|---|---|
| Format: | Conference item |
| Published: |
2012
|
| _version_ | 1797084146610208768 |
|---|---|
| author | Lukasiewicz, T Martinez, M Orsi, G Simari, G |
| author_facet | Lukasiewicz, T Martinez, M Orsi, G Simari, G |
| author_sort | Lukasiewicz, T |
| collection | OXFORD |
| description | The Semantic Web effort has steadily been gaining traction in the recent years. In particular,Web search companies are recently realizing that their products need to evolve towards having richer semantic search capabilities. Description logics (DLs) have been adopted as the formal underpinnings for Semantic Web languages used in describing ontologies. Reasoning under uncertainty has recently taken a leading role in this arena, given the nature of data found on theWeb. In this paper, we present a probabilistic extension of the DL EL++ (which underlies the OWL2 EL profile) using Markov logic networks (MLNs) as probabilistic semantics. This extension is tightly coupled, meaning that probabilistic annotations in formulas can refer to objects in the ontology. We show that, even though the tightly coupled nature of our language means that many basic operations are data-intractable, we can leverage a sublanguage of MLNs that allows to rank the atomic consequences of an ontology relative to their probability values (called ranking queries) even when these values are not fully computed. We present an anytime algorithm to answer ranking queries, and provide an upper bound on the error that it incurs, as well as a criterion to decide when results are guaranteed to be correct. |
| first_indexed | 2024-03-07T01:51:16Z |
| format | Conference item |
| id | oxford-uuid:9a29c1f2-c078-41bf-aa8f-c498d82def21 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T01:51:16Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | oxford-uuid:9a29c1f2-c078-41bf-aa8f-c498d82def212022-03-27T00:19:30ZHeuristic ranking in tightly coupled probabilistic description logicsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:9a29c1f2-c078-41bf-aa8f-c498d82def21Symplectic Elements at Oxford2012Lukasiewicz, TMartinez, MOrsi, GSimari, GThe Semantic Web effort has steadily been gaining traction in the recent years. In particular,Web search companies are recently realizing that their products need to evolve towards having richer semantic search capabilities. Description logics (DLs) have been adopted as the formal underpinnings for Semantic Web languages used in describing ontologies. Reasoning under uncertainty has recently taken a leading role in this arena, given the nature of data found on theWeb. In this paper, we present a probabilistic extension of the DL EL++ (which underlies the OWL2 EL profile) using Markov logic networks (MLNs) as probabilistic semantics. This extension is tightly coupled, meaning that probabilistic annotations in formulas can refer to objects in the ontology. We show that, even though the tightly coupled nature of our language means that many basic operations are data-intractable, we can leverage a sublanguage of MLNs that allows to rank the atomic consequences of an ontology relative to their probability values (called ranking queries) even when these values are not fully computed. We present an anytime algorithm to answer ranking queries, and provide an upper bound on the error that it incurs, as well as a criterion to decide when results are guaranteed to be correct. |
| spellingShingle | Lukasiewicz, T Martinez, M Orsi, G Simari, G Heuristic ranking in tightly coupled probabilistic description logics |
| title | Heuristic ranking in tightly coupled probabilistic description logics |
| title_full | Heuristic ranking in tightly coupled probabilistic description logics |
| title_fullStr | Heuristic ranking in tightly coupled probabilistic description logics |
| title_full_unstemmed | Heuristic ranking in tightly coupled probabilistic description logics |
| title_short | Heuristic ranking in tightly coupled probabilistic description logics |
| title_sort | heuristic ranking in tightly coupled probabilistic description logics |
| work_keys_str_mv | AT lukasiewiczt heuristicrankingintightlycoupledprobabilisticdescriptionlogics AT martinezm heuristicrankingintightlycoupledprobabilisticdescriptionlogics AT orsig heuristicrankingintightlycoupledprobabilisticdescriptionlogics AT simarig heuristicrankingintightlycoupledprobabilisticdescriptionlogics |