Aggregating semantic annotators
A growing number of resources are available for enriching documents with semantic annotations. While originally focused on a few standard classes of annotations, the ecosystem of annotators is now becoming increasingly diverse. Although annotators often have very different vocabularies, with both hi...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
2013
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| _version_ | 1797087422351147008 |
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| author | Chen, L Ortona, S Orsi, G Benedikt, M |
| author_facet | Chen, L Ortona, S Orsi, G Benedikt, M |
| author_sort | Chen, L |
| collection | OXFORD |
| description | A growing number of resources are available for enriching documents with semantic annotations. While originally focused on a few standard classes of annotations, the ecosystem of annotators is now becoming increasingly diverse. Although annotators often have very different vocabularies, with both high-level and specialist concepts, they also have many semantic interconnections. We will show that both the overlap and the diversity in annotator vocabularies motivate the need for semantic annotation integration: middleware that produces a unified annotation on top of diverse semantic annotators. On the one hand, the diversity of vocabulary allows applications to benefit from the much richer vocabulary available in an integrated vocabulary. On the other hand, we present evidence that the most widely-used annotators on the web suffer from serious accuracy deficiencies: the overlap in vocabularies from individual annotators allows an integrated annotator to boost accuracy by exploiting inter-annotator agreement and disagreement. The integration of semantic annotations leads to new challenges, both compared to usual data integration scenarios and to standard aggregation of machine learning tools. We overview an approach to these challenges that performs ontology-aware aggregation. We introduce an approach that requires no training data, making use of ideas from database repair. We experimentally compare this with a supervised approach, which adapts maximal entropy Markov models to the setting of ontology-based annotations. We further experimentally compare both these approaches with respect to ontologyunaware supervised approaches, and to individual annotators. © 2013 VLDB Endowment. |
| first_indexed | 2024-03-07T02:35:30Z |
| format | Journal article |
| id | oxford-uuid:a8a3ff0c-5006-4d19-8236-e148184ae296 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T02:35:30Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | oxford-uuid:a8a3ff0c-5006-4d19-8236-e148184ae2962022-03-27T03:02:59ZAggregating semantic annotatorsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:a8a3ff0c-5006-4d19-8236-e148184ae296EnglishSymplectic Elements at Oxford2013Chen, LOrtona, SOrsi, GBenedikt, MA growing number of resources are available for enriching documents with semantic annotations. While originally focused on a few standard classes of annotations, the ecosystem of annotators is now becoming increasingly diverse. Although annotators often have very different vocabularies, with both high-level and specialist concepts, they also have many semantic interconnections. We will show that both the overlap and the diversity in annotator vocabularies motivate the need for semantic annotation integration: middleware that produces a unified annotation on top of diverse semantic annotators. On the one hand, the diversity of vocabulary allows applications to benefit from the much richer vocabulary available in an integrated vocabulary. On the other hand, we present evidence that the most widely-used annotators on the web suffer from serious accuracy deficiencies: the overlap in vocabularies from individual annotators allows an integrated annotator to boost accuracy by exploiting inter-annotator agreement and disagreement. The integration of semantic annotations leads to new challenges, both compared to usual data integration scenarios and to standard aggregation of machine learning tools. We overview an approach to these challenges that performs ontology-aware aggregation. We introduce an approach that requires no training data, making use of ideas from database repair. We experimentally compare this with a supervised approach, which adapts maximal entropy Markov models to the setting of ontology-based annotations. We further experimentally compare both these approaches with respect to ontologyunaware supervised approaches, and to individual annotators. © 2013 VLDB Endowment. |
| spellingShingle | Chen, L Ortona, S Orsi, G Benedikt, M Aggregating semantic annotators |
| title | Aggregating semantic annotators |
| title_full | Aggregating semantic annotators |
| title_fullStr | Aggregating semantic annotators |
| title_full_unstemmed | Aggregating semantic annotators |
| title_short | Aggregating semantic annotators |
| title_sort | aggregating semantic annotators |
| work_keys_str_mv | AT chenl aggregatingsemanticannotators AT ortonas aggregatingsemanticannotators AT orsig aggregatingsemanticannotators AT benediktm aggregatingsemanticannotators |