知识图谱需要的技术
知识图谱架早期知识图谱架构• 知识图谱一般架构:[来源自百科]• 早期知识图谱架构
架构讨论
知识抽取
• 实体概念抽取 • 实体概念映射 • 关系抽取 • 质量评估
A sampler of research problems
• "Apple CEO Steve Jobs said.." => (SteveJobs, CEO, Apple) • "Steve Jobs said that Apple will.." => NIL
• Traditional relation extraction datasets
• ACE 2004 • MUC-7 • Biomedical datasets (e.g BioNLP clallenges)
2. Entity resolution
• Single entity methods • Relational methods
3. Link prediction
• Rule-based methods • Probabilistic models • Factorization methods
Not in this tutorial:
80
Relation Extraction
? playFor
Kobe Bryant
LA Lakers
“Kobe Bryant, “Kobe
“Kobe Bryant
the franchise player of once again saved man of the match for
the Lakers” his team” Los Angeles”
KDD 2014 Tutorial on Constructing and Mining Web-scale Knowledge Graphs, New York, August 24, 2014
82
Supervised relation extraction
• Sentence-level labels of relation mentions
KDD 2014 Tutorial on Constructing and Mining Web-scale Knowledge Graphs, New York, August 24, 2014
• Growth: knowledge graphs are incomplete! • Link prediction: add relations • Ontology matching: connect graphs • Knowledge extraction: extract new entities and relations from web/text
•
Learn classifiers from +/- e: context words + POS, dependency path between
entities, named entity tags, token/parse-path/entity distance
•
Intelligence: can AI emerge from knowledge graphs?
• Automatic reasoning and planning
• Generalization and abstraction
KDD 2014 Tutorial on Constructing and Mining Web-scale Knowledge Graphs, New York, August 24, 2014
9
关系抽取
• 定义: • 常见手段:
– 语义模式匹配[频繁模式抽取,基于密度聚类,基于语义相 似性]
– 层次主题模型[弱监督]
Methods and techniques
1. Relation extraction:
• Supervised models • Semi-supervised models • Distant supervision
• Extracting semantic relations between sets of [grounded] entities
• Numerous variants:
• Undefined vs pre-determined set of relations • Binary vs n-ary relations, facet discovery • Extracting temporal information • Supervision: {fully, un, semi, distant}-supervision • Cues used: only lexical vs full linguistic features
• Validation: knowledge graphs are not always correct! • Entity resolution: merge duplicate entities, split wrongly merged ones
• Error detection: remove false assertions
• Entity classification • Group/expert detection • Ontology alignment • Object ranking
• Embedding models
KDD 2014 Tutorial on Constructing and Mining Web-scale Knowledge Graphs, New York, August 24, 2014
•
Interface: how to make it easier to access knowledge?
• Semantic parsing: interpret the meaning of queries
• Question answering: compute answers using the knowledge graph